Reviewerly/exHarmony huggingface dataset:
Port Cybersecurity: Balancing Evolving Regulatory Compliance with Enterprise Risk Management

The threats and risks related to cybersecurity in seaports continue to increase, which is prompting national governments and intergovernmental organizations to explore, and in some cases implement, legislation and regulations to govern cybersecurity. This increasingly complex risk landscape is also pushing port operators and related industries to address the challenges posed by cyber risks to the operations and security of seaports from a non-regulatory perspective. This is reflected in the issuance of a number of port cybersecurity guidance documents and, more importantly, by a nascent but growing port cybersecurity insurance market. These approaches are distinct and can result in different priorities and focus areas but, with proper coordination, can be implemented in ways that are mutually supportive and serve the needs of the entire port sector.
A Preliminary Study on Chinese Learners’ Written Errors Based on an Error-Tagged Learner Corpus

With the development of technology, the need for compiling computer-based learner corpora has gradually gained more attention from language teachers and researchers. A learner corpus can reflect learners’ authentic use of a target language, which provides useful information for language teachers, researchers, and textbook editors. Limitations of retrieving errors in learner corpora, however, still exist. For example, it is difficult to retrieve omission errors if a corpus is not error-tagged beforehand. To offer researchers an error-tagged learner corpus of Chinese, this study manually error-tagged the two-million-word Chinese Learner Written Corpus of National Taiwan Normal University. A preliminary analysis of errors tagged in the learner corpus shows a total of 48,266 errors distributed to 119 tags. These 48,266 errors are mostly distributed to the incorrect selection of words or the missing of necessary word-level components, and the misuse of nouns, action verbs, adverbs, and structural particles is especially common. Among the 119 tags, the top 12 common error tags (i.e., occurring more than 1,000 times) accounted for more than 50% of the total errors, and incorrect selections of nouns and action verbs together constituted more than 27% of the total errors. These 12 common error types, especially the wrong choice of nouns and action verbs, should thus be regarded to be particularly difficult for second language (L2) learners of Chinese to acquire. Analysis of the top 12 common errors also reveals that learners’ misuse of verbs, adverbs, and structural particles were somewhat varied (i.e., involving different types of target modification, such as missing, redundant, and incorrect selection), whereas their misuse of nouns mostly resulted from an incorrect selection. A comparison between the top 10 common error types in this study with those in Lee et al. (2016) reveals that, regardless of some discrepancies in ranking, 90% of the top 10 error tags overlapped in the two studies, suggesting that these error types are indeed difficult for L2 Chinese learners to acquire and should be investigated further. Based on the findings yielded in this study, suggestions for further research on L2 Chinese learners’ errors are provided.
The Development of Relative Clauses in L2 Chinese: A Corpus-Based Study

Acquisition of relative clauses (RCs) in a second language (L2) has long been a popular research focus, particularly in determining whether L2 learners’ acquisition of RCs conforms to the Noun Phrase Accessibility Hierarchy (NPAH) (Keenan & Comrie, 1977), which proposes that subject-extracted RCs are the easiest to learn because they are the most commonly produced RC type with the fewest error rate. Early studies have mostly focused on Indo-European languages, especially English. In this study, we adopt a corpus-based approach to analyze the distribution of subject-extracted RCs (SRCs) and object-extracted RCs (ORCs) by Chinese learners with six different L1s and at two proficiency levels to test whether SRCs are easier than ORCs for Chinese L2 learners. The corpus we used is the Test of Chinese as a Foreign Language (TOCFL) Learner Corpus comprised 4,709 compositions written by test-takers of the writing section. A total of 2,055 RCs are analyzed, including 1,362 RCs at the CEFR-B1 (intermediate-high) level and 693 RCs at the CEFR-B2 (advanced) level by native speakers of English, Spanish, Japanese, Korean, Vietnamese, and Indonesian, representing three different language typologies. From the perspectives of RCs occurring in the grammatical position in the matrix sentence and the animacy of the head noun, the results show that ORCs for Chinese L2 learners are easier than SRCs. These results go against the NPAH hypothesis. In addition, no matter what branching types (i.e. left, right, or left-and-right) the learner’s native language was, all lower-proficiency level language learners produced more ORCs than SRCs. These results coincide with the development pattern of RCs for L1 Chinese acquisition. Therefore, we propose that the dominant factor in learning Chinese RCs is word order, since ORCs have the same SVO word order as Chinese simple sentences. Regardless of learners’ language background, learners can produce ORCs more naturally and with more ease. After the L2 language proficiency increases, SRCs will take over that advantage and learners’ language use will become gradually closer to the target language.
Deep Learning Approach for Breast Cancer Detection

In traditional method to detect cancer, pathologist examines biopsies to detect and segment, based on cell structure, shape, size, and form of cells. Newly computer-based methods are evolved in Digital Pathology, with increasing techniques and applications in detecting, segmenting, and classifying of nuclei. In cancer study, these techniques have played a vital role in reducing human interference and save the results for future cancer diagnosis. Convolution neural network, recurrent neural network, deep belief network, etc., are common techniques which are used in data science because these algorithms give improved and better results in comparison with machine learning.
Attending to the Cultures of Data Science Work

This essay reflects on the shifting attention to the “social” and the “cultural” in data science communities. While recently the “social” and the “cultural” have been prioritized in data science discourse, social and cultural concerns that get raised in data science are almost always outwardly focused – applying to the communities that data scientists seek to support more so than more computationally-focused data science communities. I argue that data science communities have a responsibility to attend not only to the cultures that orient the work of domain communities, but also to the cultures that orient their own work. I describe how ethnographic frameworks such as thick description can be enlisted to encourage more reflexive data science work, and I conclude with recommendations for documenting the cultural provenance of data policy and infrastructure.
Label Embedding Based Scoring Method for Secondary School Essays

Automatic essay scoring techniques can automatically evaluate and score essays, and they have become one of the hot issues in the application of natural language processing techniques in education. Current automatic essay scoring methods often use large pre-trained models to obtain semantic features, which do not perform well in the field of automatic essay scoring because the training corpus does not match the content domain of the essay, and the extraction of features for long essays is not effective. We propose a label embedding-based method for scoring secondary school essays, using a modified bidirectional long- and short-term memory network and a BERT model to extract domain features and abstract features of essays, while using a gating mechanism to adjust the influence of both types of features on essay scoring, and finally automatic scoring of essays through feature fusion. The experimental results show that the proposed model performs significantly better on the essay auto-scoring dataset of the Kaggle ASAP competition, with an average QWK value of 81.22%, which verifies the effectiveness of the proposed algorithm in the essay auto-scoring task.
Internet of Medical Things Enabled by Permissioned Blockchain on Distributed Storage

The recent COVID-19 pandemic has made the world suffer ravaging costs and damage to human lives, perhaps never seen in modern world. Pandemics will keep reviving till such time the humans attain a disease-less world state. Till such realizations are attained, we need to attempt retarding the pandemics by exploiting information systems enabled with new genre IT technologies, and blockchain offers one such way for realization. This paper proposes medical IoT architecture enabled by blockchain and further augmented with distributed storage protocol to retard any such pandemics ahead. The works have been performed on a Multichain permissioned blockchain platform and IPFS protocol.
Mining Repository for Module Reuse: A Machine Learning-Based Approach

Repositories play a key role in a field of software engineering especially in the life cycle of applications development. Millions of applications have been developed over the years with the possibility of common software modules. As of now, there is no automated model available for the organization which can help to the developers in reducing the applications development time. This paper presents a machine learning-based approach for the same objective. The proposed model first reads the developed applications folders and records the files created over an internal cloud. Binary classification is carried out on the raw data after extracting the features in the form of modules of each application. The modules are marked as features and application as a class. A module presents in a class is marked as 1 or 0. The test results of the proposed model conclude that random forest performs better than support vector machine, Naïve Bayes, logistic regression, logistic discriminant analysis, decision tree classifier, and K-nearest neighbours with accuracy of 33, 79, 87.5, 62.5, 75 and 75%, respectively. Random forest returned an accuracy of 97%.
Detection Method of Aggregated Floating Objects on Water Surface Based on Attention Mechanism and YOLOv3

With the development of smart water conservancy construction, a realistic demand for using computer vision technology to assist in the supervision of floating objects on the water surface has arisen. To address the problem that the current research on water surface floating objects detection mainly focuses on detecting scattered individual floating objects, and an improved YOLOv3 algorithm embedded with the SE attention module is proposed for detecting aggregated water surface floating objects. A self-made dataset containing aggregated floating objects “mixed garbage” and “water pollution” is developed and augmented with four data enhancement methods. The K-means++ algorithm was used to replace the K-means algorithm for clustering the dataset with ground truth box sizes to reduce the negative effects of randomly selecting the initial clustering centers. The localization loss in the loss function of the YOLOv3 model is improved, and GIoU Loss is introduced to improve the localization accuracy. The experimental results show that S-YOLOv3 outperforms other models in the field of water surface object detection on the self-made dataset compared with YOLOv3 and other commonly used models in the field of water surface object detection, and the mAP reaches 83.1%.
Emo-Spots: Detection and Analysis of Emotional Attributes Through Bio-Inspired Facial Landmarks

Modern engineering society requires qualified solutions to many practical problems, to meet accurate outcomes. Emotion detection through face landmarks has unique coordination toward improving the feedback system. Bio-inspired learning methods are an interesting area of research. Face expressions are detected through various methodologies. Bio-inspired techniques are used to extract facial landmarks to detect facial expressions even better. The selection of face attributes is crucial for the prediction of deeper emotions. The proposed system is focused on implementing a bio-inspired facial landmark detection system for emotion extraction. The dataset used for the analysis is taken from the (JAFEE) Japanese female face expressions dataset. It consists of a collection of unique expressions and recorded images. Human face attributes are extracted from the Haar cascade model. The proposed novel methodology is derived from the Robust Emo-Spot Extracting technique (RESET) using point mapping model-based landmark mapping for pre-trained images and labeling spots. The face images are correlated with databases and classified the type using a deep convolutional neural network, in which appropriate Emo-spots are mapped using RESET. The adjustable Emo-Spots are randomly tuned using the randomly looped K-nearest neighbor algorithm. The novel algorithm checks for the correlation ratio (CR) <20. Non-correlated values result in higher values of CR >20. The proposed system is compared with the state-of-art approach in terms of accuracy.
Banging Interaction in Ubiquitous Music

We explore a new conceptual framework for interaction design in the context of ubiquitous musical activities, banging interaction. Our proposal entails a shift of focus from instruments, tools or device-oriented approaches to a timbre-led design practice that targets the potential relationships between sonic and multimodal qualities and the local available technological resources. One thread of this perspective envisages more flexible usage through the application of adaptive techniques. Another thread engages with recent advances in mid-air interaction, hinting at multimodal sensing. Yet another thread involves the incorporation of an aesthetically pliable approach to sonic materials, materialised through timbre as a target, rather than ‘notes’, ‘instruments’ or ‘orchestras’. We present two examples of deployments: The prototype mixDroid, one of the first systems to employ mobile devices in everyday settings for creative purposes, and the prototype Dynamic Drum Collective, an adaptive implementation for percussive sources based on visual tokens and whole-body interaction.
Recreating Gaming Experience Through Spatial Augmented Reality

Online connections and virtual world have dominated the last few years of our social life. There has been a lack of each other’s physical presence. The aim of this paper is to create an experience to connect with people in a physical space. The project is named as Connect. Connect is a series of nostalgic games to be played in a spatially augmented environment. The experience aims to inculcate feel-good emotions and enhance socialization and collaboration. Connect involves the intersection of projection mapping, game design and experiential design. Two nostalgic games, Snake and Pacman, are developed for the experience. Several iterations are made and tested simultaneously. The games are made multiplayer and tested by projecting in different spaces. The final execution is done in Cave Automatic Virtual Environment (CAVE) where people play both the games. The participants felt good after playing and were seen socializing and collaborating with each other. Verbal feedback along with post experience questionnaire confirmed the same, expanding the future scope of the project.
A Hybrid Neural Network for Music Generation Using Frequency Domain Data

Currently, many deep learning based methods for music generation have been proposed. However, these methods use time-domain audio data to train their networks; consequently, these methods generate low-quality music. This paper proposes using frequency-domain data that are transformed from time-domain data to train a hybrid neural network that combines a recurrent neural network with a generative adversarial network to generate music in an end-to-end way. The automatic music generation method proposed in this paper explores a new representation of music data, and the results prove the effectiveness of our method.
Remote Sensing Image Object Detection Based on Improved SSD Algorithm

Aiming at the problems of complex background, serious illumination and small target in optical remote sensing image, a remote sensing image object detection algorithm based on decoupling head and attention mechanism (DHA-SSD) is proposed. The algorithm is based on the combination of resnet-50 and SSD algorithm. Firstly, the decoupling head structure is added to decouple the classification task and positioning task of object detection, so as to alleviate the conflict between the two tasks; Secondly, in the multi-scale detection stage, SimAM attention module is introduced to improve the multi-scale detection performance of the model without adding parameters; Finally, the experimental results on the public RSOD optical remote sensing image data set show that the mAP value is improved by 4.4% compared with the benchmark algorithm.
Framework for governance tailoring: case study for a blockchain-based supply chain application

Innovative uses of the blockchain technology in existing business models open up new opportunities to improve services and applications with new attributes such as integrity, transparency and auditability. In many cases, however, this involves a collaboration of different companies and entities, and therefore the integration with a blockchain depends on a good relationship between all parties. A key step in that direction is defining a governance both for the consortium that undertakes the service or application and for its wider business ecosystem. Thus, the governance must cover not only management aspects of the network, but also processes that regulate the application and the relationship between participants. This paper discusses the case study of a traceability service for animal protein supply chain, which has strengthened its value proposition by means of a new blockchain layer. A framework to facilitate the tailoring of its governance is proposed and discussed.
Elesto Protocol: Self-Sovereign Identity System

The key to the emerging digital society and economy is digital identity, a fundamental component of digital transformation in representing any real-world entity in digital form. An effective digital identity is essential to secure a successful digital future, especially as the demand for digital services grows.Since the genesis of digital identification, numerous identity management (IdM) systems have been developed and enforced to address the increasing need to manage identities based on the needs of individuals and organizations. This advancement of IdM systems offers an incremental process that leads the user to gain control of identity ownership and personal data while raising identity management concerns such as sovereignty, storage, access control, security, privacy, and safeguarding. Self-sovereign identity (SSI), recently recognized as a promising IdM, can provide sovereignty to the identity owner while addressing these concerns.The Elesto protocol is an emerging open-source, public permissionless blockchain-based implementation that uses SSI for identity management for users, organizations, and other entities. The protocol leverages W3C specifications for decentralized identifiers (DID), verifiable credentials (VC), DIDComm messaging specifications, and SSI agents.The Elesto protocol is an application-specific blockchain implementation using the Cosmos SDK framework, Tendermint Proof-of-Stake (PoS) consensus, the Inter-Blockchain Communication protocol (IBC), and design satisfies all of the fundamental requirements and principles of SSI.This paper presents the design and implementation of the Elesto protocol as an identity infrastructure. Three use cases illustrate how the protocol helps overcome the highlighted problems by decentralizing or extending functionalities in the blockchain infrastructure.
Performance Study for Improving Throughput in Hyperledger Fabric Blockchain Platform

Hyperledger Fabric (HLF) is a blockchain platform that supports immediate finality of transactions and can be used in various application domains such as Supply chain, Health etc. Researchers have reported significant improvement in throughput, in HLF v1.0, based on experiments carried out with certain optimizations when Kafka is used as a consensus mechanism. In our paper, we supplement the above study with a few more guidelines that improve the throughput. The guidelines have been derived on top of HLF platform v2.2.3 with Raft and SmartBFT consensus mechanisms and with different Tx sizes. Our experimental study has shown 3 to 5 times improvement when compared to default configuration settings of HLF v2.2.3 in the case of Raft.
Improved Resilience of Local Energy Markets using Blockchain Technology and Self-Sovereign Identity

The Digital Green Transition of the energy industry is accelerating as decarbonization and digitalization of the power market and system are gaining momentum. Unlocking the full and real potential of local energy markets is among the opportunities to be demystified in the coming years, with blockchain technology being a prominent enabler. Important subcomponents such as functional and fair local energy pricing mechanisms and management algorithms will be required for next-generation digital local energy trading and management platforms. Even though rapid digitalization of the energy sector offers considerable advantages, it also increases the cyber-physical risk levels of energy systems in general. The proposed framework of this paper accommodates considerable undiscovered aspects of democratization of the energy systems and future digital shared economy conventions. Nevertheless, privacy concerns must be addressed in tandem with the cyber-physical security of local energy markets and their ecosystem. This work proposes a joint privacy and cyber-physical security framework designed for local energy markets to improve cyber-physical resilience. All related parts, such as blockchain technology and self-sovereign identity, are used in an organized way to create a possible solution.
Potential of Smart Contracts in the Pharmaceutical Supply Chain of Belarus

Timely supply of quality pharmaceutical products is seen as not only an important aspect of healthcare system but also a matter of national security. Taking into account the social orientation of the pharmaceutical business, states impose special requirements for the safety, effectiveness, quality, conditions for the sale, transportation and storage of medicinal products. These requirements are not always fulfilled, which gives room for the technology to step in and assist. The aim of this paper is to analyze how smart contracts can help in addressing pharmaceutical supply chain issues with a focus on Belarus, which is the first country in the world to explicitly state that smart contracts are legal. This research uses a case study methodology to examine the potential for implementation of smart contracts in the supply chain of pharmaceutical products, the barriers for that and the possible influence of smart contracts on various stakeholders. Data collection was organized through document review and expert interviews. Results indicated that there are weak spots in the current pharmaceutical supply chain process, such as different kinds of errors in supporting documentation, quality defects and failures in climate control systems. Some vulnerable points in supply chain could be eliminated by smart contracts, for example, faults in maintaining temperature regime during transportation. The study also reveals a number of challenges for implementing smart contracts, such as resistance of users, bureaucracy, costs, the necessity for a lot of participants to be a part of a single network. In this regard, the study went on to propose a pilot project for implementing smart contracts in pharmaceutical supply chain.