Reviewerly/exHarmony huggingface dataset:
Exploiting Hybrid Subword Information for Chinese Historical Named Entity Recognition

Historical named entity recognition plays a very important role on the historical studies, especially for Chinese historical domain. This paper introduces a novel Deep learning (DL)-based approach with rich subword information of Chinese characters, which shows a powerful entity recognition of Chinese classical literatures. Our experiments on a manually constructed corpus of CMAG indicates that our model can achieve the best performance compared to other state-of-the-art approaches. The ablation studies on entity categories shows that the morphological information of Chinese characters can significantly improve the performance of NER models, especially combined with a gate-based neural unit GLU and the multi-head attention mechanism. This research provides some meaningful suggestions for automatic extraction of named entities in Chinese history, even extended to other low-resource domains.
Motivation for Effective Work of University Teachers or Why Innovations in Higher Education are Being Hindered

The article deals with the issues of motivation of teaching staff as a complex category that includes several motivational aspects that affect the involvement of the teaching staff in the educational process. The article is based on the results of the study of staff motivation. The main research has been conducted since 2002 on the materials of organizations of the automobile market. The results of previous research were published in early articles of the authors; additional research on the application of vector motivational theory to scientific and pedagogical personnel was conducted in the period from 2007 to 2018. The research was conducted on the basis of observations and surveys of teaching staff of at least 30 organizations of higher and secondary vocational education, and motivational experiments at the level of departments and institutes in at least five organizations of professional education in Russia. The article touches the following results: 1. A clarification of the concept of human capital capacity of a scientific and pedagogical worker with the consideration of the features of intellectual work of higher school teachers. 2. A description of the main motives that form the motivational space of employees in the pedagogical system taking into account the specific features of the studied area. According to the authors, such main motives are classified into monetary, conservative, image, hygienic and self-actualization ones. 3. A proposal of the concept of effective use of the university’s human potential on the basis of a five-vector main motivational space. The concept is a modified theory of human capital management of industrial enterprises and incorporates the specifics of managing scientific and pedagogical staff in educational institutions. 4. A presentation of the author’s opinion on the reasons of reduction of the effectiveness of innovation in higher education institutions and professional depreciation of human capital based on a vector motivational model. 5. A consideration of the best practices of implementing innovative solutions in higher education institutions and an explanation of their impact on the performance of teaching staff in the context of the proposed motivational theory.
Visual Analysis of FIFA World Cup Data

Soccer is one of the most popular sports in the world, played by thousands of professionals and amateurs every week. Consequently, it is no surprise that it generates an enormous amount of data. In today's data-driven world it is essential to find an optimal, self-explanatory, way to present the data in a way to be able to derive visual patterns that relate to the underlying data patterns. In this paper, we describe an interactive visualization for analyzing soccer data and identifying patterns, correlations, and insights. We illustrate the usefulness of our approach, especially targeted towards non-visualization experts, by applying it to World Cup data and by discussing potential use cases.
DeepRings: A Concentric-Ring Based Visualization to Understand Deep Learning Models

Artificial Intelligent (AI) techniques, such as ma-chine learning (ML), have been making significant progress over the past decade. Many systems have been applied in sensitive tasks involving critical infrastructures which affect human well-being or health. Before deploying an AI system, it is necessary to validate its behavior and guarantee that it will continue to perform as expected when deployed in a real-world environment. For this reason, it is important to comprehend specific aspects of such systems. For example, understanding how neural networks produce final predictions remains a fundamental challenge. Existing work on interpreting neural network predictions for images via feature visualization often focuses on explaining predictions for neurons of one single convolutional layer. Not presenting a global perspective over the features learned by the model leads the user to miss the bigger picture. In this work we focus on providing a representation based on the structure of deep neural networks. It presents a visualization able to give the user a global perspective over the feature maps of a convolutional neural network (CNN) in a single image, revealing potential problems of the learning representations present in the network feature maps.
Live Migration Timing Optimization Integration with VMware Environments

Live migration is an essential feature in virtual infrastructure and cloud computing datacenters. Using live migration, virtual machines can be online migrated from a physical machine to another with negligible service interruption. Load balance, power saving, dynamic resource allocation, and high availability algorithms in virtual data-centers and cloud computing environments are dependent on live migration. Live migration process has six phases that result in live migration overhead. Currently, virtual datacenters admins run live migrations without an idea about the migration cost prediction and without recommendations about the optimal timing for initiating a VM live migration especially for large memory VMs or for concurrently multiple VMs migration. Without cost prediction and timing optimization, live migration might face longer duration, network bottlenecks and migration failure in some cases. The previously proposed timing optimization approach is based on using machine learning for live migration cost prediction and the network utilization predict ion of the cluster. In this paper, we show how to integrate our machine learning based timing optimization algorithm with VMware vSphere. This integration deployment proves the practicality of the proposed algorithm by presenting the building blocks of the tools and backend scripts that should run to implement this timing optimization feature. The paper shows also how the IT admins can make use of this novel cost prediction and timing optimization option as an integrated plug-in within VMware vSphere UI to be notified with the optimal timing recommendation in case of a having live migration request.
Characterizing Dementia Caregivers’ Information Exchange on Social Media: Exploring an Expert-Machine Co-development Process

Social media platforms have introduced new opportunities for supporting family caregivers of persons with Alzheimer's disease and related dementias (ADRD). Existing methods for exploring online information seeking and sharing (i.e., information exchange) involve examining online posts via manual analysis by human experts or fully automated data-driven exploration through text classification. Both methods have limitations. In this paper, we propose an innovative expert–machine co-development (EMC) process that enables rich interactions and co-learning between human experts and automatic algorithms. By applying the EMC in analyzing ADRD caregivers' online behaviors, we illustrate steps required by the EMC, and demonstrate its effectiveness in enhancing human experts' representations of ADRD caregivers' online information exchange and developing more accurate automatic classification models for ADRD caregivers' information exchange.
Harnessing Artificial Intelligence and Machine Learning in Biomedical Applications with the Appropriate Regulation of Data

Medical devices and systems are increasingly relying on software using artificial intelligence (AI) and machine learning (ML) algorithms to increase efficiency, provide better diagnoses, and increase the quality of care for patients. AI- and ML-based devices and systems have an advantage over traditional medical device systems because they are designed to learn and improve using large databases of actual or simulated patient data. However, the use of these datasets could introduce harmful biases to certain populations, restrict economic development if policy were to change in the future, and negatively impact healthcare. We recommend amending the Food Drug and Cosmetic Act to explicitly direct the Secretary of Health and Human Services to regulate databases used by AI systems and require that the premarket review of medical databases includes assessments of potential bias and security.
On the Evolution of Semantic Warehouses: The Case of Global Record of Stocks and Fisheries

Semantic Warehouses integrate data from various sources for offering a unified view of the data and enabling the answering of queries which cannot be answered by the individual sources. However, such semantic warehouses have to be refreshed periodically as the underlying datasets change. This is a challenging requirement, not only because the mappings and transformations that were used for constructing the semantic warehouse can be invalidated, but also because additional information (not existing in the initial datasets) may have been added in the semantic warehouse, and such information needs to be preserved after every reconstruction. In this paper we focus on this particular problem in a real setting: the Global Record of Stocks and Fisheries, a semantic warehouse that integrates data about stocks and fisheries from various information systems. We propose and detail a process that can tackle these requirements and we report our experiences from implementing it.
The Surgeon and the Smartphone — is the Association Really Smart?

The aim of this literature review is to encompass the importance of integrating smartphones in the life of a surgeon especially in the era of the COVID-19 pandemic. With the progression of technology, telesurgery, remote pre and postoperative care, smartphone-assisted intraoperative navigation and transcontinental education can be made a reality. Nonetheless, rates of nosocomial infections have been raised and the increased use of a smartphone can compound this issue. Similarly, there is a greater potential for operator distraction, medical equipment interference and increased radiation exposure for the user. All the above create a new set of problems for the surgeon. We hereby attempt a review of the advantages and harmful effects from the usage of smartphones.
Bitcoin Abnormal Transaction Detection Based on Machine Learning

This paper is devoted to the development of a reliable abnormal bitcoin transaction detection that may be involved in money laundering and illegal traffic of goods and services. The article proposed an algorithm of abnormal bitcoin transaction detection based on machine learning. For training and evaluation of the model, the Elliptic dataset is used, consisting of 46564 Bitcoin transactions: 4545 of “illegal” and 42019 of “legal”. The proposed algorithm for detecting abnormal bitcoin transactions is based on various machine learning algorithms with the selection of hyperparameters. To evaluate the proposed algorithm, we used the metric of accuracy, precision, recall, F1 score and index of balanced accuracy. Using the resampling algorithm in conditions of class imbalance, it was possible to increase the reliability of the classification of abnormal bitcoin transactions in comparison to the best known result on the Elliptic dataset.
ID-based key-insulated signcryption with equality test in cloud computing

Abstract Key-insulated encryption reduces the problem of secret key exposure in hostile setting while signcryption cryptosystem attains the benefits of digitally signing a ciphertext and public key cryptosystem. In this study, we merge the primitives of parallel key-insulation cryptosystem and signcryption with equality test to construct ID-based parallel key-insulated signcryption with a test for equality (ID-PKSET) in cloud computing. The construction prevent data forgery, data re-play attacks and reduces the leakage of secret keys in harsh environments. Our scheme attains the security property of existential unforgeable chosen message attack (EUF-CMA) and indistinquishable identity chosen ciphertext attack (IND-ID-CCA2) using random oracle model.
Efficient Communication Scheduling for Parameter Synchronization of DML in Data Center Networks

It's common practice to speed up machine learning (ML) training by distributing it across a cluster of computing nodes. Data-parallel distributed ML (DML) training relieves the pressure of computing node; however, the communication traffic introduced during parameter synchronization becomes bottleneck of DML training. Regarding communication bottleneck, we identify two primary causes: high contention of concurrent communication and large volume of redundant transmission in the push and pull stage while synchronizing parameters. To address these issues, we propose a novel Group Stale Synchronous Parallel (GSSP) scheme, which divides the nodes into groups and coordinates groups to synchronize in a circular order. GSSP mitigates network contention and is proven to converge. We provide analysis of the optimal group's number based on bandwidth and buffer size. For reducing traffic redundancy, we propose a multicast-based scheme, which generates multicast trees by minimizing links overlap and allocates transmission rate to multicast flows by solving the min-max optimization problem. Finally, we conduct extensive simulations to evaluate the performance of our proposals. We simulate parameter transmission of All-Reduce and parameter server in Fat-Tree with traffics trace of ML models. Simulation results show that our proposals provide communication-efficiency for DML training by mitigating contention and reducing redundancy.
Mitigation Strategic of Drought in Central Java Indonesia during Covid-19 Pandemic

Abstract In 2019, most of the areas in Central Java Indonesia experienced drought disaster. There are 3 districts that conduct emergency response due to drought disasters. If there is another drought in this year during Covid-19 pandemic, it will be very fatal due to the need for more water than usual. Besides being used for daily needs, water is also needed for washing hands and bathing during the Covid-19 pandemic. This study aims to determine the level of drought in Central Java province and provide solutions on how to reduce the drought strategy so as not to aggravate the spread of Covid-19. The method used to determine rainfall prediction is the Canonical Correlation Analysis (CCA) method with input data of average monthly rainfall for approximately 30 years in Central Java. Then to determine the drought map using the Standardized Precipitation Index (SPI) method with input data of rainfall prediction for the last 3 months from the CCA method. The results of data processing show that in August to October 2020 there was a drought with normal levels in almost all regions in central Java, except in the Muria peninsula which is still very wet (August-September) and wet (October). In November and December 2020 there was a drought in all regions in Central Java with normal levels. The level of dryness in this period is normal, meaning it is neither too high nor too dry. Meanwhile, based on several references that at the end of the year there will be a La Nina phenomenon, so that at the end of the year there will be no drought but there will be lots of rain. Several recommendations to overcome this situation will be presented in the final section of this paper.
A Statistical Test for Legal Interpretation: Theory and Applications

Many questions of legal interpretation hinge on whether two groups of people assign different meanings to the same word. For example: Do 18th- and 21st-century English speakers assign the same meaning to commerce? Do judges and laypersons agree on what makes conduct reasonable? We propose a new statistical test to answer such questions. In three applications, we use our test to (1) quantify differences in the meanings of specialized words from civil procedure, (2) identify statistically significant differences between judges and laypersons' understandings of reasonable and consent, and (3) assess differences across various effort standards in commercial contracts (phrases like best effort and good faith effort). Our approach may be readily applied outside the law to quantify semantic disagreements between or within groups.
Time-constrained strong multi-designated verifier signature suitable for Internet of things–based collaborative fog computing systems

Fog computing is viewed as an extended technique of cloud computing. In Internet of things–based collaborative fog computing systems, a fog node aggregating lots of data from Internet of things devices has to transmit the information to distributed cloud servers that will collaboratively verify it based on some predefined auditing policy. However, compromised fog nodes controlled by an adversary might inject bogus data to cheat or confuse remote servers. It also causes the waste of communication and computation resources. To further control the lifetime of signing capability for fog nodes, an appropriate mechanism is crucial. In this article, the author proposes a time-constrained strong multi-designated verifier signature scheme to meet the above requirement. In particular, a conventional non-delegatable strong multi-designated verifier signature scheme with low computation is first given. Based on its constructions, we show how to transform it into a time-constrained variant. The unforgeability of the proposed schemes is formally proved based on the famous elliptic curve discrete logarithm assumption. The security requirement of strong signer ambiguity for our substantial constructions is also analyzed by utilizing the intractable assumption of decisional Diffie–Hellman. Moreover, some comparisons in terms of the signature size and computational costs for involved entities among related mechanisms are made.
Modeling of Pupillometric Signals for Studying Children’s Rare Diseases

The use of chromatic pupil responses may be a novel way to diagnose and monitor diseases affecting either the outer or inner retina. Here we developed a model-based approximation of the dynamics of pupillary diameter after an optical stimulation; we assume the response of the pupil is approximated as the output of a 2nd-order linear model. Model parameters are identified by using a least-squares fitting procedure, thus obtaining an optimal estimate of the activation time and of the shape of the pupil response to the stimulation. Results indicate the model adequately represents the curve shape; despite the presence of artifacts, that hinder the fitting procedure, a significant difference in the time constants of the model of controls and of patients is present.
Improved Object Detection using Data Enhancement method based on Generative Adversarial Nets

Abstract In deep learning-based object detection, especially in face detection, small target and small face has always been a practical and common difficult problem due to its low resolution, blurred image, less information and more noise. In some applications, sensing image data is hard to collect, leading to limited object detection performance. In this paper, we investigate using a generative adversarial network model to augment data for object detection in images. We use generative adversarial network to generate the diverse objects based on the current image data. An improved generative adversarial network is added in the network and a new loss funtion is applied during the trianing process to generate diverse and high-quality traing images. Experiments show that images generated by generative adversarial network have higher quality than counterparts.
Clothoid-based Trajectory Following Approach for Self-driving vehicles

Lately self-driving navigation and control have obtained significant attention in many fields, such as mobile robotics or autonomous driving. Although sensing, perception, planning and following subtasks associated with autonomous vehicles persist with open challenges. In this paper the autonomous following subtask is targeted. The paper proposes trajectory following approach which is designed for self-driving vehicles.
Inline Double Layer Depth Estimation with Transparent Materials

3D depth computation from stereo data has been one of the most researched topics in computer vision. While state-of-art approaches have flourished over time, reconstruction of transparent materials is still considered an open problem. Based on 3D light field data we propose a method to obtain smooth and consistent double-layer estimates of scenes with transparent materials. Our novel approach robustly combines estimates from models with different layer hypotheses in a cost volume with subsequent minimization of a joint second order $$\mathrm {TGV}$$ energy on two depth layers. Additionally we showcase the results of our approach on objects from common inspection use-cases in an industrial setting and compare our work to related methods.
Online Learning as a Necessary Measure During a Pandemic and as an Opportunity to Increase the Engineering Education Efficiency

This research aim was to analyze problems, which educational systems are facing in the global upheaval era, statement of tasks, which must be solved, as well as search for solutions. The engineering education system has been improved in recent years through the use of new features, which are provided by e-learning software. Future engineers will require such skills as creative thinking, ability to make informed, effective decisions, as well as social responsibility and eco-thinking. The research was carried out on the tools used to organize online learning during the COVID-19 pandemic in several universities. It gave a possibility to compare the effectiveness of using different virtual environments to organize the training of different profiles' engineers, as well as to analyze the educational content and its difference from that used in traditional classroom teaching. To make a correct assessment, evaluation criteria were identified, which can be formalized, and also motivational characteristics were classified, which can be influenced on the quality improvement. For a case study, different student's groups were selected to compare them involvement in the process with traditional and online training forms as well as their performance. Additionally, was done an analysis how a training form impacts on their projects quality.