Reviewerly/exHarmony huggingface dataset:
Ethical Behavior of Firms and Foreign Direct Investments in African Settings: The Moderating Effect of Judicial Independence

This paper examines the relationship between ethical behavior of firms and foreign direct investments in African countries and tests whether the judicial independence moderates this association. The sample includes 108 country-year observations over a period of 2014–2017. Findings show that the ethical behavior of firms is positively associated with foreign direct investment. Similarly, judicial independence has a positive and significant effect on the same variable. When testing for the moderating effects of judicial independence, the association between ethical behavior of firms and foreign direct investment remains positive and significant for high judicial independence sub-sample, while it becomes insignificant for countries characterized by low judicial independence. The findings highlight the importance of business ethics in attracting foreign direct investment inflows in African countries. It also emphasizes the need to consider the role played by high judicial independence in strengthening this association. These results signal to policy makers in African countries the importance of adopting enforcing rules obliging firms to act ethically and strengthening judicial independence.
Multi-task Learning with Auxiliary Cross-attention Transformer for Low-Resource Multi-dialect Speech Recognition

In this paper, we apply multi-task learning to perform low-resource multi-dialect speech recognition, and propose a method combining Transformer and soft parameter sharing multi-task learning. Our model has two task streams: the primary task stream that recognizes speech and the auxiliary task stream identifies the dialect. The auxiliary task stream provides the dialect identification information to the auxiliary cross-attention of the primary task stream, so that the primary task stream has dialect discrimination. Experimental results on the task of Tibetan multi-dialect speech recognition show that our model outperforms the single-dialect model and hard parameter sharing based multi-dialect model, by reducing the average syllable error rate (ASER) by 30.22% and 3.89%, respectively.
MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation

Medical dialogue systems interact with patients to collect symptoms and provide treatment advice. In this task, medical entities (e.g., diseases, symptoms, and medicines) are the most central part of the dialogues. However, existing datasets either do not provide entity annotation or are too small in scale. In this paper, we present MedDG, an entity-centric medical dialogue dataset, where medical entities are annotated with the help of domain experts. It consists of 17,864 Chinese dialogues, 385,951 utterances, and 217,205 entities, at least one magnitude larger than existing entity-annotated datasets. Based on MedDG, we conduct preliminary research on entity-aware medical dialogue generation by implementing several benchmark models. Extensive experiments show that the entity-aware adaptions on the generation models consistently enhance the response quality but there still remains a large space of improvement for future research. The codes and the dataset are released at https://github.com/lwgkzl/MedDG .
Professionalising Socialisation for Pragmatic e-Curriculum to the Rescue of South African Universities

The COVID-19 and lockdown of 2020 promoted interest in the pragmatism in education in the universities in the Republic of South Africa (RSA), and in professionalising socialisation for the university electronic curriculum (e-curriculum). Achieving pragmatism in e-curriculum is an essential process. It interrogates professional systems and socialisation processes on which the South African university e-curriculum is based. However, the move has sparked contestations between universities’ professionalisation and socialisation of the e-curriculum over the digital divide. The digital divide is the division in the use of digital technologies for e-curriculum between the digitally well-resourced and the under-resourced universities. Therefore, this chapter explores how and why professionalising socialisation for a pragmatic e-curriculum resulted in the university digital divide occurring in the RSA. Further, it introduces rethinking of ways apropos of the university's pragmatic e-curriculum in overcoming COVID-19, lockdown, and other related challenges. The sample for the study was five research-intensive universities in RSA. Methodologically, the chapter uses extensive desk literature (secondary data) published in various sources such as HEI websites, HEI bulletins, articles, books, etc., and critical discourse analysis (CDA), before analysing such. Findings indicate that HEIs in RSA, as in most developed countries, have combined socialisation technologies (SMSs) with HEI-prescribed professional technologies (LMSs) to suit their situations and contexts, thus addressing their unique needs pragmatically. This study recommends that academics should create an opportunity for students to reflect on their experiences before/during/after learning in order to understand their identities. Such may help them to access a pragmatic e-curriculum.
Research and Development of User Clustering-Based Content Similarity Algorithms in Dance-Assisted Choreography Techniques

With the gradual development of digital information and software computing capabilities, the use of computers in dance-assisted choreography is becoming more and more widespread. But although the level of computers is now in rapid development, the technical level of using computers in dance choreography is not yet very mature, technical support is not in place, dance-assisted choreography is not effective, and the existing technical level is not yet able to meet the new needs of dance choreography. In order to improve the dance-assisted choreography technology and provide a more complete educational user interface for dance-assisted choreography, the content similarity algorithm of user clustering has a wide range of operations and a strong ability to calculate the amount of data, combined with the computer to apply the content similarity algorithm of user clustering in dance-assisted choreography technology to build a dance-assisted choreography system based on user clustering. The article proposes three major methods based on collaborative filtering algorithm of user clustering, collaborative filtering algorithm based on similarity class and user preference, and fuzzy cluster analysis of users and analyses their principles. In the experimental part, the performance of IBCF algorithm and collaborative filtering algorithm in dance-assisted choreography system is compared and analysed to observe the change of MAE value under the change of user similarity with number under different k values of cluster classes. The experimental results found that the MAE values of the IBCF algorithm and the collaborative filtering algorithm in the system were at 0.84 and 0.76, respectively, with a difference of about 8% between the two MAE values. The smaller the MAE value, the higher the effectiveness in the dance-assisted choreography technique. Applying the clustering algorithm to the system to make local adjustments and analysis of dance movement paths, it can grasp the choreography rules more precisely and innovate the choreography techniques.
Blockchain-enabled electric vehicle charging

Blockchain is the emerging technology in the fields of secured sharing of medical data, cross-border payments, logistics, food selling, etc. Simply put, it is a decentralized, shared network computing that demonstrates the overall origin of the digital item. Blockchain is trying to extend its features in numerous new industries, given its high potential. With its expertise proven in the supply chain, banking, digital authentication, investment management, and the arts and music sector, it is now expanding into the fast-growing market of electric vehicles (EV). The foremost barrier to EV adoption is range anxiety. The majority concern is about battery power running out on long road journeys. This is due to the fact that electric chargers are not readily available everywhere, and as a result of the absence of charging infrastructure, potential consumers remain hesitant to switch to EVs. As already said before, blockchain is a peer-to-peer technology that enables users to share their chargers when not in use. Moreover, in turn, charger owners will be benefited by getting paid for using the chargers. It also allows people and organizations to exchange, purchase, and transfer extra energy between their selves without the intrusion of a central authority or general public having control over how and when transactions can happen.
Knowledge Management Strategy for Handling Cyber Attacks in E-Commerce with Computer Security Incident Response Team (CSIRT)

Electronic Commerce (E-Commerce) was created to help expand the market share network through the internet without the boundaries of space and time.However, behind all the benefits obtained, E-Commerce also raises the issue of consumer concerns about the responsibility for personal data that has been recorded and collected by E-Commerce companies.The personal data is in the form of consumer identity names, passwords, debit and credit card numbers, conversations in email, as well as information related to consumer requests.In Indonesia, cyber attacks have occurred several times against 3 major E-Commerce companies in Indonesia.In 2019, users' personal data in the form of email addresses, telephone numbers, and residential addresses were sold on the deep web at Bukalapak and Tokopedia.Even though E-Commerce affected by the cyber attack already has a Computer Security Incident Response Team (CSIRT) by recruiting various security engineers, both defense and attack, this system still has a weakness, namely that the CSIRT operates in the aspect of handling and experimenting with defense, not yet on how to store data and prepare for forensics.CSIRT will do the same thing again, and so on.This is called an iterative procedure, one day the attack will come back and only be done with technical handling.Previous research has succeeded in revealing that organizations that have Knowledge Management (KM), the organization has succeeded in reducing costs up to four times from the original without using KM in the cyber security operations.The author provides a solution to create a knowledge management strategy for handling cyber incidents in CSIRT E-Commerce in Indonesia.This research resulted in 4 KM Processes and 2 KM Enablers which were then translated into concrete actions.The KM Processes are Knowledge Creation, Knowledge Storing,
Algorithm for Optimization of Inverse Problem Modeling in Fuzzy Cognitive Maps

Managerial decision-making is a complex process that has several problems. The more heterogeneous the system, the more immeasurable, non-numerical information it contains. To understand the cognitive processes involved, it is important to describe in detail their components, define the dependencies between components, and apply relevant algorithms for scenario modelling. Fuzzy cognitive maps (FCMs) is the popular approach for modeling a system’s behavior over time and defining its main properties. This work develops a new algorithm for scenario analysis in complex systems represented by FCMs to provide support for decision-making. The algorithm allows researchers to analyze system-development scenarios to obtain the required change to the system’s components that leads to the target state. The problem of determining a system’s initial state is most conspicuous when constructing a compound or unbalanced fuzzy maps. Currently, a brute force algorithm is used to calculate the steps needed to approach a target, but that takes exponential time. The paper describes a new algorithm to obtain the initial values of the controlled concepts in fuzzy cognitive maps using the theory of neutrosophic fuzzy equations. This approach reduces the time needed to find the optimal solution to a problem, and it allows inverse problems to be solved in the fuzzy cognitive maps as a part of the scenario-modeling framework.
TS2LBDP

Few scheduling, models focus on service-level agreement (SLA) enforcement, which limits their real-time applicability. Thus, this research work proposes the design of an improved task-side service level (TS2L) agreement model for efficient task-scheduling using elephant herd optimization (EHO) with deadline (BDP) awareness. TS2LBDP incorporates pattern analysis using ensemble hierarchical, k-means, and fuzzy C means (FCM) clustering methods. A combination of these modules assists in improving task diversity and scheduling efficiency. SLA-based distribution enhances scheduling fairness and reduces mean waiting time for different clients. The proposed model was tested on parallel workload archive wherein different cloud workload logs and their respective VM configurations are described. It was observed that the proposed model improves scheduling efficiency by 20%, task diversity by 45%, and deadline hit ratio by 1%. It is scalable and can be used for several types of cloud deployments.
Two-Stage Query Graph Selection for Knowledge Base Question Answering

Finding the best answer to a question in Knowledge Base Question Answering (KBQA) is always challenging due to its enormous searching space and the interactive performance requirement. A typical solution is to retrieve the answer by finding the optimal query graph, which is a sub-graph of the knowledge graph. However, existing methods usually generate a considerable number of sub-graph candidates, then fail to find the optimal one effectively, resulting in a significant gap between top-1 performance and the oracle score of all the graph candidates. To address this issue, this paper presents a novel two-stage method based on the idea of first reducing the candidates to form a shortlist, and then selecting the optimal one from them. Before the selection, we generate many, often hundreds of, candidates for each question. In the first selection stage, we sort the candidates and select a small set of query graphs (top-k), while in the second stage we propose to rerank them to select the final answer. We evaluate our system on both English and Chinese data, and the results show that our proposed two-stage method achieves competitive performance on all datasets (Our code is publicly available at https://github.com/EnernityTwinkle/KBQA-QueryGraphSelection .).
A Life-Long Learning Education Passport Powered by Blockchain Technology and Verifiable Digital Credentials: The BlockAdemiC Project

The academic and business domains are in constant transformation due to the technological advances. In this context, proof of knowledge, skills and training are becoming crucial for the students and employees. In a competitive environment like that, forgery of diplomas and certificates is a frequent problem that has not been faced properly yet. Additionally, the lack of a formal representation and acknowledgment of informal and life-long learning outcomes costs to all educational stakeholders and employee seekers. By using open source blockchain technology as a game changer throughout the educational process, security, privacy, integrity and immutability of the data related to diplomas, certificates and skills acquired by learners can be guaranteed. The result of building a system supported by blockchain for life-long learning, a positive impact in trust and transparency of the education achievements, institutions, students and companies is achieved.
Overview of NLPCC2022 Shared Task 5 Track 1: Multi-label Classification for Scientific Literature

Given the increasing volume of scientific literature in conferences, journals as well as open access websites, it is important to index these data in a hierarchical way for intelligent retrieval. We organized Track 1 in NLPCC2022 Shared Task 5 for multi-label classification for scientific literature. This paper will summarize the task information, the data set, the models returned from the participants and the final result. Furthermore, we will discuss key findings and challenges for hierarchical multi-label classification in the scientific domain.
A Pre-trained Language Model for Medical Question Answering Based on Domain Adaption

With the successful application of question answering (QA) in human-computer interaction scenarios such as chatbots and search engines, medical question answering (QA) systems have gradually attracted widespread attention, because it can not only help professionals make decisions efficiently, but also supply non-professional people advice when they are seeking useful information. However, due to the professionalism of domain knowledge, it is still hard for existing medical question answering systems to understand professional domain knowledge of medicine, which makes question answering systems unable to generate fluent and accurate answers. The goal of this paper is to train the language model on the basis of pre-training. With better language models, we can get better medical question answering models. Through the combination of DAP and TAP, the model can understand the knowledge of the medical domain and task, which helps question answering models generate smooth and accurate answers and achieve good results.
Novel Representative Sampling for Improved Active Learning

Active learning solves machine learning problems where acquiring labels for the data is costly. It allows for the learner to select training samples by asking intelligent questions. Various sampling strategies exist for choosing the training set for pool-based active learning. However, the existing representative querying approaches for active learning do not attempt to capture the underlying data distribution, which we believe is an important part of representative sampling. To that end, we propose an adaptation of the sigma point sampling technique from unscented transformation (UT) for constructing a representative subset. UT has shown to be very effective in non-linear transformation modeling in object tracking and robotics. When combined with the Gaussian mixture model, sigma points can estimate the statistical moments such as mean and co-variance of an unknown distribution with very few samples which are generated deterministically. Sigma point sampling being parameterized gives better control over the sampling process. We use sigma points for representative subset construction and train the learner on them. We compare our results with other sampling techniques and improve test accuracy on the handwritten digit recognition data set MNIST.
S-box Pooling: Towards More Efficient Side-Channel Security Evaluations

Nowadays, profiled attacks are the standard penetration tests for security evaluations. Often the security evaluators have to perform profiled attacks on each S-box to quantify the security strength of the target symmetric cryptographic algorithm implementations more accurately. The required time to conduct such profiled attacks is very long due to the number of profiling traces (for many certification bodies, at least 1,000,000 are mandated). It is getting even more time-consuming after introducing deep learning profiled attacks. Furthermore, some certification bodies instruct up to 5,000,000 or 10,000,000 profiling traces because modern embedded secure IC products have more and more countermeasures against side-channel attacks. It is a challenge to simultaneously decrease the number of required profiling traces and the required profiling time while retaining the attack performance for profiled attacks. In this work, we propose a simple yet remarkably effective pooling approach to address this problem for security evaluations. That is, pooling over the S-boxes to build a large profiling set and perform the profiling on this large set once. Intensive experiments are conducted with this pooling approach using different profiling tools (template attack and its pooled variant, stochastic model and deep learning) on three different AES implementations (a sequential S-box software AES implementation without masking, a sequential S-box software AES implementation with first-order masking and a parallel S-box hardware AES implementation with first-order masking). The experimental results have shown that the proposed pooling approach can lead to similar attack performance while decreasing both the required number of profiling traces and the required profiling time by a factor of 8 or even 16.
Black-Box for Blockchain Parameters Adjustment

This paper introduces a function for blockchain performance evaluation as a black-box. The function runs the Solana blockchain test network with the only differences between the main network in a configuration file and the physical network to operate in. The black-box takes setup parameters as input, launches blockchain in a cloud, emulates artificial users’ activity, and gives two outputs–transactions per second (tps) and drop rate. By default, the setup has six most important integer parameters and a network with three computers in the cloud, while one can vary eighty-nine parameters, the number of computers in the network and use local computers via black-box configuration files. The applied problem is to maximize the tps under a zero drop rate constraint. The black-box, like real blockchains, uses network communication, so reproducibility is an essential part of the design. We also provide an optimization baseline, showing the non-trivial results’ reachability.