Reviewerly/exHarmony huggingface dataset:
DeceFL: a principled fully decentralized federated learning framework

Traditional machine learning relies on a centralized data pipeline for model training in various applications; however, data are inherently fragmented. Such a decentralized nature of databases presents the serious challenge for collaboration: sending all decentralized datasets to a central server raises serious privacy concerns. Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication burden and high vulnerability when there exists a failure at or an attack on the central client. Here we propose a principled decentralized federated learning algorithm (DeceFL), which does not require a central client and relies only on local information transmission between clients and their neighbors, representing a fully decentralized learning framework. It has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate $O(1/T)$ (where $T$ is the number of iterations in gradient descent) as centralized federated learning when the loss function is smooth and strongly convex. Finally, the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions, time-invariant and time-varying topologies, as well as IID and Non-IID of datasets, demonstrating its applicability to a wide range of real-world medical and industrial applications.
Improving software maintenance with improved bug triaging

Bug triaging is a critical and time-consuming activity of software maintenance. This paper aims to present an automated heuristic approach combined with fuzzy multi-criteria decision-making for bug triaging. To date, studies lack consideration of multi-criteria inputs to gather decisive and explicit knowledge of bug reports. The proposed approach builds a bug priority queue using the multi-criteria fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and combines it with Bacterial Foraging Optimization Algorithm (BFOA) and Bar Systems (BAR) optimization to select developers. A relative threshold value is computed and categorization of developers is performed using hybrid optimization techniques to make a distinction between active, inactive, or new developers for bug allocation. The harmonic mean of precision, recall, f-measure, and accuracy obtained is 92.05%, 89.21%, 85.09%, and 93.11% respectively. This indicates increased overall accuracy of 90%±2% when compared with existing approaches. Overall, it is a novel solution to improve the bug assignment process which utilizes intuitive judgment of triagers using fuzzy multi-criteria decision making and is capable of making a distinction between active, inactive, and new developers based on their relative workload categorization.
Engineering Classical Capacity of Generalized Pauli Channels with Admissible Memory Kernels

In this paper, we analyze the classical capacity of the generalized Pauli channels generated via memory kernel master equations. For suitable engineering of the kernel parameters, evolution with non-local noise effects can produce dynamical maps with a higher capacity than a purely Markovian evolution. We provide instructive examples for qubit and qutrit evolution. Interestingly, similar behavior is not observed when analyzing time-local master equations.
Intelligent Recognition of Ore‐Forming Anomalies Based on Multisource Data Fusion: A Case Study of the Daqiao Mining Area, Gansu Province, China

Abstract Novel prediction methods using artificial intelligence have been developed to improve the identification, discovery, and utilization of new types of mineral resources at new depths or using new technologies. However, most artificial intelligence methods require large training data sets that are often unavailable for mineralization prediction models, leading to inaccuracies. To address this issue, we developed a semi‐supervised machine‐learning method to identify metallogenic anomalies using the density‐based spatial clustering of applications with noise method and autoencoder. The outputs of this method show irregularity in distributions inferred from geological, geochemical, and hyperspectral remote sensing data that match known mineralization locations. We focus on the Daqiao mining area of Gansu Province in China to show that the model predictions are highly consistent with known deposits of the Yinmahe and Daqiao gold mines, and two new prospecting areas have been highlighted for further field confirmation. The accuracy of this semi‐supervised learning method was verified by an interdisciplinary intelligent analysis, showing that this method could have wide‐reaching applications for improving regional geological surveys.
Learning Representations of Inactive Users

Understanding inactive users is the key to user growth and engagement for many Internet companies. However, learning inactive users' representations and their preferences is still challenging because the features available are missing and the positive responses or labels are insufficient. In this paper, we propose a cross domain learning approach to exclusively recommend customized items to inactive users by leveraging the knowledge of active users. Particularly, we represent users, no matter active or inactive users, by their friends' browsing behaviors based on a graph neural network (GNN) layer atop of a heterogeneous graph defined on social networks (user-user friendships) and browsing behaviors (user-page clicks). We jointly optimize the learning tasks of active users in source domain and inactive users in target domain based on the domain invariant features extracted from the embedding of our GNN layer, where the domain invariant features that are learned to benefit both tasks on active/inactive users, and are indiscriminate with respect to the shift between the domains. Extensive experiments show that our approach can well capture the preference of inactive users using both public data and real-world data at Alipay.
Interpretable time-aware and co-occurrence-aware network for medical prediction

Disease prediction based on electronic health records (EHRs) is essential for personalized healthcare. But it's hard due to the special data structure and the interpretability requirement of methods. The structure of EHR is hierarchical: each patient has a sequence of admissions, and each admission has some co-occurrence diagnoses. However, the existing methods only partially model these characteristics and lack the interpretation for non-specialists.This work proposes a time-aware and co-occurrence-aware deep learning network (TCoN), which is not only suitable for EHR data structure but also interpretable: the co-occurrence-aware self-attention (CS-attention) mechanism and time-aware gated recurrent unit (T-GRU) can model multilevel relations; the interpretation path and the diagnosis graph can make the result interpretable.The method is tested on a real-world dataset for mortality prediction, readmission prediction, disease prediction, and next diagnoses prediction. Experimental results show that TCoN is better than baselines with 2.01% higher accuracy. Meanwhile, the method can give the interpretation of causal relationships and the diagnosis graph of each patient.This work proposes a novel model-TCoN. It is an interpretable and effective deep learning method, that can model the hierarchical medical structure and predict medical events. The experiments show that it outperforms all state-of-the-art methods. Future work can apply the graph embedding technology based on more knowledge data such as doctor notes.
A Study on Semantic-Based Autonomous Computing Technology for Highly Reliable Smart Factory in Industry 4.0

Smart factories have made great progress with the development of various ICT technologies, such as IoT, big data, and artificial intelligence. The recent development of smart factory technology has shown results in automation and data acquisition and processing. However, it still has incomplete points to be converted to advanced technology, including intelligence. For intelligentization, there is a need to propose a new research method in addition to the previous methodologies. Considering the specificity of the factory, the data structure and methodology of the Semantic Web can be effective. Therefore, in this study, a smart factory was designed by the convergence of monitoring technology, autonomous control technology, and semantic web technologies. Based on the proposed methodology, a methodology for the autonomous control of a smart factory on a digital twin was designed.
KnowMeme: A Knowledge-enriched Graph Neural Network Solution to Offensive Meme Detection

This paper studies a critical problem of identifying offensive meme posts on online social media where images are superimposed with deliberately altered or crafted captions to deliver offensive information. Existing solutions often ignore the implicit relationship between visual and textual contents in the meme and their implied meanings, which are critical to accurately detect offensive memes. Two important challenges exist in solving the problem: i) how to effectively incorporate human commonsense knowledge to capture the symbolic or implied meaning of the meme contents that implicitly deliver offensive information? ii) How to accurately identify the cross-modal knowledge-based relations between entities in both the visual and textual content of the meme that jointly insinuate offensive messages? To address the above challenges, we develop KnowMeme, a knowledge-enriched graph neural network solution that leverages knowledge facts from human commonsense knowledge to effectively detect offensive meme posts on online social media. Evaluation results show that KnowMeme achieves significant performance gains compared to the state-of-the-art baseline methods in accurately detecting offensive memes.
Optimization of Ice and Snow Sports Industry Chain Structure Based on Sensor Network Communication and Artificial Intelligence

As a new tourism industry model, the ice and snow sports tourism industry has played an increasingly important role in the development of China’s entire tourism industry. This paper analyzes the structural optimization of the ice and snow sports industry chain based on sensor network communication and artificial intelligence and aims to fully apply sensor network communication and artificial intelligence technology to the development of the ice and snow sports industry. Let us look for an optimized method to promote the development of the ice and snow sports industry chain structure. This paper conducts a research on the optimization of the ice and snow sports industry chain structure based on sensor network communication and artificial intelligence. In the method part, this article explains the architecture of sensor nodes and networks, uses the long-term monitoring of wireless sensor networks and the flexibility of the data acquisition structure, combined with the superiority of artificial intelligence in information processing and analysis, to obtain and analyze the experimental data of this research, and introduces the specific content of artificial intelligence and the ice and snow sports industry. In the algorithm, the grey relational analysis method is introduced. In the part of the experimental results, experiments were carried out on the ice and snow city resources, competitive advantages, the quarterly average value of the ice and snow sports enterprise prosperity index, ice and snow sports venues, GDP, and the speed comparison and structure of the development of the sports industry, and simulation experiments were carried out from the perspective of sensors. On the whole, the development curve of China’s ice and snow sports industry basically conforms to the economic development curve. With the slowdown of GDP growth rate, the growth rate of the sports industry slowed down simultaneously. However, despite the continued decline in economic growth, the sports industry has ushered in a peak of development, with a growth rate of 89.45%, a year-on-year increase of 69.3 percentage points. This phenomenon shows that the sports industry has sufficient endogenous development momentum and huge development potential. It is an important driving force and emerging growth point of the economic society and modern services. It has a comprehensive driving role in stimulating urban consumption, promoting economic transformation and upgrading, and driving the development of the service industry.
Severity Prediction for COVID-19 Patients via Recurrent Neural Networks.

The novel coronavirus disease-2019 (COVID-19) pandemic has threatened the health of tens of millions of people worldwide and imposed heavy burden on global healthcare systems. In this paper, we propose a model to predict whether a patient infected with COVID-19 will develop severe outcomes based only on the patient's historical electronic health records (EHR) prior to hospital admission using recurrent neural networks. The model predicts risk score that represents the probability for a patient to progress into severe status (mechanical ventilation, tracheostomy, or death) after being infected with COVID-19. The model achieved 0.846 area under the receiver operating characteristic curve in predicting patients' outcomes averaged over 5-fold cross validation. While many of the existing models use features obtained after diagnosis of COVID-19, our proposed model only utilizes a patient's historical EHR to enable proactive risk management at the time of hospital admission.
Patch-Based attack on traffic sign recognition

Deep neural networks are found to be vulnerable to adversarial examples. These drawbacks can cause the security problem of machine vision systems. For automated driving, it leads to a more lethal safety-critical issue of the perception systems. In this paper, we propose an adversarial patch method to attack the recognition of traffic signs. On the dataset of GTSRB (the German Traffic Sign Recognition Benchmark), we train three classifiers with good generalization abilities: GTSRB-VGG16, GTSRB-ResNet34, and GTSRB-GoogLeNet. The crafted patches are robust and not limited by locations after the convergence of training under a wide variety of transformations and random locations. The attack success rate (ASR) can be achieved over 90% on GTSRB-ResNet34 and GTSRB-GoogLeNet. For GTSRB-VGG16 with a relatively more robust structure, the utilization of the ensemble method helps attack the classifier successfully. Our method shows the superior performance in the physical attack experiment which achieves over 70% ASR on GTSRB-ResNet34. We also give an analysis of the robustness of GTSRB-VGG16 and two findings can be highlighted: 1) The learning features of GTSRB-VGG16 are more explainable than the learning features of the other two models that contribute to the robustness; 2) GTSRB-VGG16 has more parameters which is potentially robust faced with the attack. Our code is available at https://github.com/yeibn999/Patch-attack-on-traffic-sign.
Machine learning with asymmetric abstention for biomedical decision-making

Abstract Machine learning and artificial intelligence have entered biomedical decision-making for diagnostics, prognostics, or therapy recommendations. However, these methods need to be interpreted with care because of the severe consequences for patients. In contrast to human decision-making, computational models typically make a decision also with low confidence. Machine learning with abstention better reflects human decision-making by introducing a reject option for samples with low confidence. The abstention intervals are typically symmetric intervals around the decision boundary. In the current study, we use asymmetric abstention intervals, which we demonstrate to be better suited for biomedical data that is typically highly imbalanced. We evaluate symmetric and asymmetric abstention on three real-world biomedical datasets and show that both approaches can significantly improve classification performance. However, asymmetric abstention rejects as many or fewer samples compared to symmetric abstention and thus, should be used in imbalanced data.
Bottleneck-Aware Non-Clairvoyant Coflow Scheduling With Fai

Coflow scheduling is critical to data-parallel applications in data centers. While schemes like Varys can achieve optimal performance, they require a priori information about coflows which is hard to obtain in practice. Existing non-clairvoyant solutions like Aalo generalize least attained service (LAS) scheduling discipline to address this issue. However, they fail to identify the bottleneck flows in a coflow and tend to allocate excessive bandwidth to the non-bottleneck flows, leading to bandwidth wastage and inferior overall performance. To this end, we present Fai that strives to improve the overall coflow performance by accelerating the bottleneck flows without priori knowledge. Fai employs bottleneck-aware scheduling. It adopts loose coordination to update coflow priority and flow rates based on total bytes sent. In addition, Fai detects bottleneck flows based on a flow’s rate and bytes sent, and de-allocates bandwidth for other flows to match the bottleneck rate without affecting the coflow completion time (CCT). The saved bandwidth is then distributed among coflows according to their priority to improve overall performance. Testbed evaluation on a 40-node cluster shows that Fai improves average (P95) CCT by 1.73× (3.43×), compared to Aalo. Large-scale trace-driven simulations also show that Fai outperforms Aalo substantially.
Design and Research of Intelligent Educational Administration Management System Based on Mobile Edge Computing Internet

Educational administration management is the primary link in the teaching management of colleges and universities. Mobile edge computing can create a carrier-class service environment with high performance, low latency, and high bandwidth and accelerate the rapid download of various contents, services, and applications in the network, which greatly promotes the upgrade of the educational administration system. Using educational administration management system to manage educational administration can promote the teaching work of colleges and universities better. This paper aims to design and develop a set of educational management information systems, using mobile edge computing (MEC) technology to combine the IT service environment and cloud computing technology at the edge of the network to improve the computing and performance of the edge network. Storage capacity reduces network operation and service delivery delay, improves user service quality experience, and helps universities improve the efficiency of educational administration management. This paper first discusses the implementation mode and related technologies of educational administration management system, then discussing the demand analysis of each functional module of the system; in the nonfunctional demand analysis part, the system needs to meet the security and performance. According to the function modules included in the system, using the way of running interface screenshot, implementation code, and flowchart, the paper analyzes the realization process of the function module and also completes the function test of the function module, as well as the performance test of the whole system. The experimental results show that the rule 4 of teaching level evaluation data mining reveals that the support degree of excellent teaching effect is 18% and the confidence degree is 53% in the age of 50–60. Rule 5 shows that the degree of support is 16% and the degree of confidence is 52%. Rule 10 shows that the degree of support for excellent teaching effect is 22% and the confidence level is 71%. The high confidence level of backbone teachers aged 50–60 indicates that the old teachers are more experienced and popular with students, while the young teachers under 30 need to focus on training to help young teachers improve their professional level. From the above data, it can be seen that through the application of mobile edge technology the educational administration system is more efficient in processing and analyzing data in terms of teacher management and teaching level, which once again shows the impact of this network technology on the construction and development of educational administration systems highly feasible.
Boosting Afaan Oromo Named Entity Recognition with Multiple Methods

Named Entity Recognizer (NER) is a widely used method of Information extraction (IE) in Natural language processing (NLP) and Information Retrieval (IR) aimed at predicting and categorizing words of a given text into predefined classes of Named Entities like a person, date/time, organization, location, etc.This paper adopts boosting NER for Afaan Oromo by using multiple methods.Combinations of approaches such as machine learning, the stored rules, and pattern matching make a system more efficient and accurate to recognize candidates name entities (NEs).It takes the strongest points from each method to boost the system performance by voting a candidate NE which is detected in more than 1 entity category or out of context because of word ambiguity, it penalized by Word senses disambiguation.Subsequent NEs tagged with identical tags merged as a single tag before the final output.The evaluation shows the system is outperformed.Finally, the future direction is forwarded a hybrid approach of rule-based with unsupervised zero-resource cross-lingual to enhance more.
Demonstration of Gamification in Education for Understanding Artificial Intelligence Principles at Elementary School Level

Artificial intelligence(AI) has emerged as an important key issue in 2020, as predict ed by the prospect that those who conquer AI will hold the hegemony of the world in the future which is why the world is paying attention to AI.However, the research on effective ways for students to better understand and utilize AI is in its infancy globally.It is crucial t o educate young students(k-1 to 6) AI principles through an efficient method, as part of ICT education.In this paper, we suggest a gamification education based on board games as a w ay to promote elementary school students' motivation to learn AI, which is still ambiguous a nd challenging for adults to understand.In addition, the proposed education method aims to demonstrate the effectiveness of education by analyzing the satisfaction of the results of educ ation conducted in elementary schools.An interesting way to teach relatively complex concept s of AI has been designed, using board games that elementary school students like.The boar d game-based gamification curriculum proposed in this paper has been piloted, resulting in el ementary students getting a better understanding of AI than before education and an average satisfaction of 4.73 out of 5. Gamification in education is an effective means to boost stude nts' interest in their studies.The gamification education proposed in this paper is expected t o educate early on the concept of AI emerging in information science so that students can b ecome more interested in AI in the future.