Reviewerly/exHarmony huggingface dataset:
Using finite-state machines to automatically scan Ancient Greek hexameter

Abstract This article presents a fully automatic approach to the scansion of Ancient Greek hexameter verse. In particular, the article describes an algorithm that uses deterministic finite-state automata and local linguistic rules to implement a targeted search for valid spondeus patterns and, in addition, a weighted finite-state transducer to correct and complete partial analyses and to reject invalid candidates. The article also details the results of an empirical evaluation of the annotation quality resulting from this approach on hand-annotated data. It is shown that a finite-state approach provides quick and linguistically sound analyses of hexameter verses as well as an efficient formalization of linguistic knowledge. The project code is available online.1
RE-DT-UX: Moving from a Discipline-Based Approach to a Role-Based One

A lot of companies work in traditional separation of professional disciplines, also Siemens. Experts define their role by being part and fulfilling the defined pattern of this discipline. The stuffed project teams are a mix of professional disciplines. But why is your original discipline the key differentiation factor? Why not look for tool and method-driven role expertise and combine it in teams with different relevant roles for a project? What happens if you change from a disciplined view to a role-defined perspective of expert impact? What happens if we stuff agile projects in an agile manor? Which role plays design thinking for it? Find it out in the following description of a new working culture at Siemens.
Linear Coded Federated Learning

In recent years, federated learning (FL) has attracted a lot of attention as a new edge computing paradigm for artificial intelligence (AI). FL facilitates multiple edge devices to collaboratively train a global model without leaking local data of any participant. In a typical edge computing scenario, the participants in FL are heterogeneous and can be composed of personal computers, smartphones, Internet of Things devices, network devices, etc.. In this heterogeneous setting, the slowest client in each training round becomes the bottleneck, which may limit the overall convergence speed and accuracy of the global model. To address this issue, one possible solution is to outsource the computing task in the slowest client to faster devices, which requires data transmission from the slowest client to other selected clients. Certainly, sending the original data set is not an option due to the privacy requirements. Therefore, in this paper, we propose an efficient linear coded federated learning (LCFL) framework to (1) speed up the convergence speed of heterogeneous FL and (2) protect the data privacy of the participants. Within the proposed framework, we design a collaborative client selection (CCS) algorithm that can select appropriate clients and assign the computation task of the slowest client to those selected devices. Finally, we build a practical experimental platform and conduct numerous experiments to evaluate the proposed LCFL framework from different aspects. The experimental results demonstrate that the proposed LCFL scheme can reduce the training time up to 93.73% when the participants have a large difference in terms of computing capability.
Uniform strong consistency of histogram density estimation for <i>φ</i>-mixing samples

This article is concerned with the uniform strong consistency of histogram density estimator and the convergence rate under φ-mixing samples. By using the Bernstein’s type inequality, we give the uniformly strong consistency of the estimator and obtain the corresponding rate under some mild conditions. These results extend or improve the corresponding ones for strong mixing process to umixing samples. Also, the simulation study of the finite samples provided in this article shows the validity of our results.
Distance Learning as a Safe Haven During the COVID-19 Pandemic and Prospects for Post Pandemic

The abrupt spread of COVID-19 has resulted in a global pandemic that has claimed millions of lives so far. This unprecedented health emergency has taken a toll on the economy of millions of individuals and families worldwide. The pandemic has also impacted the educational systems of many countries as it forced educational institutions to change their teaching and learning strategies. This chapter aims to provide an answer to four main research questions: the issues Moroccan and Mexican teachers faced in resorting to distance learning, what coping strategies teachers used in both countries, what policies and procedures were implemented in distance learning by the Moroccan and the Mexican governments, and finally, if there were any infrastructure issues reported in both countries with regard to the adoption of distance learning during the outbreak of the pandemic and the future prospects for the post-pandemic. The answers to these questions were sought by using both quantitative and qualitative data collected from teachers of whom 150 were Moroccan and 196 were Mexican.
Modeling of threats in the sphere of medical data storage

In medical institutions of various levels, a large amount of datais stored in electronic form, to ensure the safety of which arepresented with special requirements. Ensuring the safety of thestorage of medical data should begin with an analysis of existingthreats. This article provides a model of threats in thearea of data storage, which includes the following components:comunicative, spatial and destructive threat. It is shown that,to date, communicative and spatial threats have minor rates,which can be neglected. The destructive threat is considered inmore detail, its components are described. Recommendationsfor the development of medical data storage systems are given.
Actor-Centric Tubelets for Real-Time Activity Detection in Extended Videos

We address the problem of detecting human and vehicle activities in long, untrimmed surveillance videos that capture a large field of view. Most existing activity detection approaches are designed for recognizing atomic human actions performed in the foreground. Therefore, they are not suitable for detecting activities in extended videos, which contain multiple actors performing co-occurring, complex activities with extreme spatio-temporal scale variations. In this paper, we propose a modular, actor-centric framework for real-time activity detection in extended videos. In particular, we decompose an extended video into a collection of smaller actor-centric tubelets of interest. Each tubelet is a video sub-volume associated with an actor and includes adaptive visual context for recognizing the actor’s activities. Once these tubelets are extracted via an object-detection-based approach, we are able to detect activities in each tubelet by focusing on the actor situated in its foreground. To accurately detect the activities of a tubelet’s actor we take into account the interactions with other detected actors and objects within the tubelet. We encode such interactions with a dynamic visual spatio-temporal graph and process it with a Graph Neural Network that yields context-aware actor representations. We validate our activity detection framework on the MEVA (Multiview Extended Video with Activities) dataset and the ActEV 2021 Sequestered Data Leaderboard and demonstrate its effectiveness in terms of speed and performance.
Large-scale heterogeneous terminal multi-domain joint fault diagnosis direction

The safe and stable operation of the Internet of Things (IOT) system is strongly related to the economy and daily life. IOT equipment damage accidents have caused major economic losses, which impacted on people's lives, and endanger industrial and social safety. However, accidents caused by various accidental factors cannot be completely avoided, effective monitoring and fault diagnosis of the IOT system, in particular in power system are very important. From the perspective of large-scale heterogeneous terminals and heterogeneous terminals across domains, this paper conducts fault diagnosis on the operation of the IOT based on big data and artificial intelligence methods. Firstly, based on the datadriven method, the information collected by different sensors is processed through the deep neural network for dimensionality reduction and feature extraction. Secondly, the extracted multi-source information features are feature fused. Finally, the Softmax function is used to diagnose the faults of the fused features.
Performance Analysis Anime Character Generation Based on DCGAN Model

Today's widespread animation culture and growing interest of the public have contributed to the development of a variety of animation works and characters. However, in many cases, the artists have to invest a lot of time into the idea of the task to be able to deliver their work; and dealing with such a task, obviously, is a huge consumption of time and energy. Accordingly, in many cases, these artists may not have enough inspiration to create a unique character. To tackle this issue, this paper proposed a generative network, which can be utilized to randomly generate unique anime characters. The application of the whole model is divided into three steps. First, a program is adopted for face recognition and interception of the data collected on the network. From the program and the data collected, over 50,000 photos were collected that were different and contained only the faces of the characters; second, the random generation model of this DCGAN was trained for more than a week, and paused after achieving satisfactory results; third, this model can be used to randomly generate characters and output them as images. Finally, an elaborate model can be used to generate images with regular features, distinct characteristics, and similar style as the real works. In addition, a GUI was designed to help users select their favorite randomly generated photos and save them uniformly in a specified path. The created images can be used as design inspiration for the artists who lack the time to create their new inspirations.
Data Augmentation of Discrete Sequential Protocol Messages Based on Recurrent Generative Adversarial Networks

In some special networks such as IoT, industrial control networks, and sensor networks used in various industries, data are usually discrete and short, called Discrete Sequential Protocol Messages (DSM). DSM data contain less information, so feature extraction is difficult, and the effect of classification is poor. For imbalanced DSM data, the problem is more complex. This paper proposes a data augmentation algorithm based on Recurrent Generative Adversarial Networks (RGAN), and uses Long short-term Memory Network (LSTM) to improve the generator structure, making the model more suitable for DSM data. Under the multiple conditions of data imbalance, the binary classification and multi-classification tasks are carried out on 2012 MACCDC and USTC-TFC 2016 datasets respectively. The results show the RGAN method we proposed can solve the problem of data imbalance under various imbalanced conditions. It can effectively improve the classification performance of minority classes and ensure the overall classification performance. For the data length, the RGAN method can process imbalanced DSM data of more than 10 bytes, which shows RGAN applies to short data.
Distributed Association Mining for Discovering Interesting Rules for Tours and Travel Company

Mining is a favourite area of research of many researchers, developing algorithms for association rule mining on distributed data. Distributed mining is used in many commercial areas, and there is a need to explore new commercial applications of the mining. The application area chosen for the study is a tour and travel company organizing package tours, as the tourism industry is growing very fast and companies with small, medium and large-sized operations are operating in these areas. Tourism is a potential application where mining can be applied and new association rules can be generated which can help the companies to develop new strategies and target potential customers based on the mining outcomes. This paper applies the distributed data mining techniques on a medium-sized tour and travel company for finding the association between age and destination visited parameters. The results show that association rules generated by mining are useful and effective for the growth of the business and making new strategies.
Phishing Webpage Classification Method based on Joint Features

Phishing websites aiming at stealing users ‘ information by mimicking legitimate trustworthy websites are still a major threat for today ’ s Internet thread. This paper systematically describes a phishing webpage detection method based on joint features. The joint feature is introduced to carry out feature cross-expansion, and the expanded joint feature vector length ranges from 40 to 376. The paper compares four optimal parameter models based on basic features and joint features: random forest is the optimal model; neural network model has a small increase in classification ability after introducing joint features; logistic regression and Adaboost have classification ability after introducing joint features constant.
Development of an intelligent learning system based on fuzzy logic

Abstract In modern institutions of higher education, the positive result of their work is measured by the progress of the student or by the factor how well the student meets the standard set by the state and the internal situation of the educational institution. In the current study, a new approach is proposed in assessing the progress of students, which is based on a fuzzy logic system. The proposed method uses three indicators: test tasks, written work and the possibility of mutual evaluation of students. Then, the quality of knowledge is assessed through the use of fuzzy logic, which is based on the Mamdani method. The results of applying this approach show that the proposed method can have practical application in assessing the quality of students’ progress.
Toward Low-Bit Neural Network Training Accelerator by Dynamic Group Accumulation

Low-bit quantization is a big challenge for neural network training. Conventional training hardware adopts FP32 to accumulate the partial-sum result, which seriously degrades energy efficiency. In this paper, a technology called dynamic group accumulation (DGA) is proposed to reduce the accumulation error. First, we model the proposed group accumulation method and give the optimal DGA algorithm. Second, we design a training architecture and implement a hardware-efficient DGA unit. Third, we make a comprehensive analysis of the DGA algorithm and training architecture. The proposed method is evaluated on CIFAR and ImageNet datasets, and results show that DGA can reduce accumulation bit-width by 6 bits while achieving the same precision as the static group method. With the FP12 DGA, the CNN algorithm only loses 0.11% accuracy in ImageNet training, and our architecture saves 32% of power consumption compared to the FP32 baseline.
Dynamic Filtering and Prioritization of Static Code Analysis Alerts

We propose an approach for filtering and prioritizing static code analysis alerts while these alerts are being reviewed by the developer. We construct a Prolog knowledge base that captures the data flow information in the source code as well as the reported alerts, their properties and associations with the data flow. The knowledge base is updated as the developer reviews the listed alerts and decides whether they point at an actual fault or not. These updates provide useful information since some of the alerts of the same type can be related in terms of their root cause. Hence, dynamically updated knowledge base can be queried to eliminate or prioritize the remaining alerts in the review list. We present a motivating example to illustrate the approach and its automation by integrating a set of tools.
Bayesian multiple Gaussian graphical models for multilevel variables from unknown classes

Gaussian graphical models are a powerful tool for investigating the conditional dependency structure between random variables by estimating sparse precision matrices and can infer networks among variables from multiple classes. Many studies assume that classes of observations are given and use methods to learn the network structures within one level (e.g. pathways or genes). In most cases, however, heterogeneous data may be obtained at different levels. Therefore, in this paper, we consider the learning of multiple connected graphs with multilevel variables from unknown classes. We estimate the classes of the observations from the mixture distributions by evaluating the Bayes factor and learn about the network structures by fitting a neighborhood-selection algorithm. This approach can be used to identify the class memberships and reveal the network structures for lower level and higher level variables simultaneously. Unlike most existing methods, which solve this problem using frequentest approaches, we assess an alternative and novel hierarchical Bayesian approach for incorporating prior knowledge. We demonstrate the unique advantages of our methods through several simulations. A breast cancer application shows that our model's results can provide insight into biological studies.
A Novel Stroke Measurement Operator for Visual Objects

Stroke is an important intrinsic property of objects which may be effectively used for object characterization. However, stroke measures reported so far lack accurate measurement of stroke width of general objects, and varying degree of stroke uniformity, which is very crucial for characterization, is not taken into consideration. In this paper, a novel stroke measurement operator is proposed using distance transform and medial skeleton map of visual objects, which effectively measures stroke width and degree of stroke uniformity for any kind of object or component. Possible utilities of this operator are also outlined in the context of object detection and classification. Finally, in order to validate and demonstrate the applicability of this operator, computed stroke features are applied in text non-text classification on a benchmark dataset and 94.5% accuracy is achieved, which establishes its robustness and effectiveness.
Using Deep Learning and Sentiment Analysis to Identify Mismatches between Online Courses’ Reviews and Ratings

Thousands of reviews and ratings are created on the internet by reviewers expressing satisfaction or lack thereof, towards a product or service. With all this massive amount of information available, it raises a problem when it comes to analysing the vast information generated from all the reviews created. Mismatches between reviews and ratings occur in some cases, this research project explores the e-commerce mismatch problem regarding the analysis of review data. Some interesting studies have been conducted within the area of Natural Language Processing (NLP) to enable a machine to essentially analyse and extract information from these raw forms of text, most of the studies only focus on sentiment analysis alone. We used sentiment analysis as a basis for further predictive analysis to determine the relationship between reviews and their corresponding ratings in order to detect any mismatches between them. We perform sentiment analysis combined with deep learning on reviews scrapped from the Coursera online learning platform. We explored NLP pre-processing techniques such as text vectorization in order to prepare the reviews for classification and comparison with their respective ratings to identify any mismatches between them. Overall, the 10-fold cross validation model achieved an average accuracy of around 87% with regards to predicting the sentiment class for a given review, the predicted sentiment was used for comparison with the given ratings in order to determine whether there might be a mismatch between them.
Research on Task Scheduling Algorithm for Edge Computing in Low-voltage transformer the area network

Aiming at the task scheduling strategy and performance optimization problems in the edge computing of the power Internet of Things, to meet the needs of refined and intelligent power distribution in the Low-voltage transformer the area, this paper studies how to effectively schedule applications to achieve task operation, processing time, and task delay And the trade-off between system performance. Taking into account the local advantages of the edge of the Internet of Things agents in the Low-voltage transformer the area(LV-TAN), as well as the characteristics of the edge intelligent computing tasks in the power distribution domain of the Low-voltage transformer the area, and the physical static characteristics of the Low-voltage transformer the area, it is established to minimize task running time as the goal, while considering the stability of the task queue, Balance, priority, and propose a scheduling strategy for edge computing based on container scheduling algorithm. This strategy decomposes the optimization problem into a series of sub-problems, and distributes tasks according to the current status of the queue backlog and unloading target nodes to meet the optimization goals of the distribution Low-voltage transformer the area and ensure the stability of the system. The simulation results show that the algorithm effectively reduces the task scheduling time, increases the CPU utilization rate, and reduces the jitter of task processing.
<scp>AI</scp>‐based abstractive text summarization towards <scp>AIoT</scp> and edge computing

The task of text summarization has been widely concerned in the fields of news, document indexing and literature retrieval. Recently, due to the rise of mobile Internet devices, natural language processing for AIoT and edge computing has become a hot spot. This paper focuses on the research of text summarization for AIoT and edge computing. For a long time, abstractive summarization is limited to academic research due to the lack of controllability of the generated content. Recently, the appearance of Transformer has changed the current situation of abstractive text summarization. Transformer follows encoder‐decoder architecture, including attention mechanism and feed‐forward network. The encoder encodes the semantic information of source text, and decoder adaptively selects the effective context information through the attention mechanism to generate a coherent summary. To extract more semantic information and control the generated text better, this paper proposes multi‐scale semantic information Transformer (MSIT). Specifically, we introduce depth‐wise separable convolution to the encoder to extract more local semantic information, so that the attention mechanism can make better use of contextual semantic information. Additionally, we combine the encoding vector of encoder and target summary as the input to the attention layer of the decoder, and introduce time series mechanism so that the decoder can consider context information when generating text. Experiments on CNN‐Daily Mail Dataset show that this model is superior to other methods.