Reviewerly/exHarmony huggingface dataset:
The Roles of Cloud-Based Supply Chain Integration on Firm Performances and Competitiveness

Cloud computing systems have emerged as a useful technology that contributes to this optimization SCI process by providing infrastructure, platform, and software solutions for the whole supply chain via the internet.But in developing regions like Ethiopia, there's a weak traditional, fragmental, non-integrated, weak, and underdeveloped supply chain process.There's still space for research on real industrial applications where Ethiopian firms strive for cloud-based integration and implement new models and integration strategies practices in their supply chains.Thus, this study is designed to investigate the impacts of cloud computing technology on the supply chain integration process to improve the performance and comparative advantage of manufacturing industries in general, and the Ethiopian basic metal industry in particular.Using primary and literature survey data, the study has been carried-out through SPSS correlation and regression analysis methods.The study shows that the adoption and utilization of cloud-based services in supply chain integration leads to improvements in both the performance and competitiveness of the firm at regional and global levels.In particular, the cloud-based supply chain integration strategies provide enhanced resource utilization, reduce the constraints on information flow in the firm, increase product and service visibility, and create flexibility within systems within supply chain partners, and manufacturing industries as a whole.According to these investigations, cloud computing has a great impact on supply chain integrations so as to improve the performance and comparative advantage of the firm.Thus, effective and efficient utilization of cloud-based supply chain integrations has a higher positive impact on firm performance and competitiveness.Furthermore, this study demonstrates that data privacy, information risk, security issues, cloud architecture, process customization, and trust among collaborators must be prioritized for long-term supply chain systems and joint ventures in cloud-based supply chain integration.
Wireless signal modulation identification method based on RF I/Q data distribution

Electromagnetic spectrum detection is the basis of the next generation wireless communication technology. Wireless signal identification is an important part of electromagnetic spectrum detection and management activities. This paper proposes to extract the distribution features of different modulated signals from the signal I/Q data. A two-dimensional gradient matrix is used to describe the characteristics of the signal classification. The minimum gradient cumulative distance (GCD) estimate between the sample and the model is used as the decision criterion for the signal classification. According to the result of the confusion matrix, the weight of the model is adjusted. Experiments show that the recognition rate of the modulated signal mentioned in this paper can reach 82.75%. The I/Q data sample was extracted under actual engineering conditions involving random noise, and the recognition rate dropped to approximately 79%. Based on the initial model gradient matrix, a reasonable algorithm is set to adjust the weight of the model, which can effectively improve the recognition rate of the modulated signal.
Exploring strategies teachers use to develop literacy skills among English first additional language learners in the foundation phase

Teachers play a significant role in literacy development. They are responsible for using different approaches to develop literacy skills among learners whose mother tongue is not the same as the language of learning and teaching in schools. This article aims to explore strategies teachers use to develop literacy skills among English First Additional Language learners. A qualitative case study was conducted that involved focus group interviews to gain in-depth knowledge of the strategies teachers use in their classes to develop literacy skills. For this article, Vygotsky’s Zone of Proximal Development is used as a theoretical framework. The participants were six foundation phase teachers from one district in Gauteng. The data gathered showed that teachers are trying to use strategies described in the curriculum policy document. Teachers were unaware that they were using some of the most contemporary strategies in their classes such as balanced-literacy approach, play-based approach and evidence-based approach. Findings showed that teachers had good knowledge and understanding of literacy however, they were experiencing some challenges. They cited lack of support from knowledgeable others and appropriate teacher development and training. The study recommends that teachers need continuous training and development, literacy text should be contextualised and continued support from the school heads of department, curriculum advisors and subject specialist. It also recommends an in-depth knowledge, understanding and implementation of the curriculum policy.
Certifying the classical simulation cost of a quantum channel

A fundamental objective in quantum information science is to determine the cost in classical resources of simulating a particular quantum system. The classical simulation cost is quantified by the signaling dimension which specifies the minimum amount of classical communication needed to perfectly simulate a channel's input-output correlations when unlimited shared randomness is held between encoder and decoder. This paper provides a collection of device-independent tests that place lower and upper bounds on the signaling dimension of a channel. Among them, a single family of tests is shown to determine when a noisy classical channel can be simulated using an amount of communication strictly less than either its input or its output alphabet size. In addition, a family of eight signaling dimension witnesses is presented that completely characterize when any four-outcome measurement channel, such as a Bell measurement, can be simulated using one communication bit and shared randomness. Finally, we bound the signaling dimension for all partial replacer channels in $d$ dimensions. The bounds are found to be tight for the special case of the erasure channel.
Leveraging Multi-Fidelity Aerodynamic Databasing to Efficiently Represent a Hypersonic Design Space

Predicting flight-performance of a hypersonic flight vehicle requires characterization of the complex aerothermodynamic phenomena present across the flight envelope. A stable and accurate trajectory simulation requires an aerodynamic database that covers a wide variety of flight conditions (aka using statistical terminology, a large design space). When preparing such an aerodynamic database, computational cost becomes critical to conserve limited computational resources while ensuring the quantities of interest are well resolved. In this study, a computationally efficient way to generate an aerodynamic coefficient database leveraging multiple fidelity levels will be demonstrated on a sample conic geometry at hypersonic flight conditions. The results show that this improves model fit over a high-fidelity model alone while saving significant amounts of computational time, and the active learning strategies further reduce errors over conventional sampling plans.
Improving Text-to-Code Generation with Features of Code Graph on GPT-2

Code generation, as a very hot application area of deep learning models for text, consists of two different fields: code-to-code and text-to-code. A recent approach, GraphCodeBERT uses code graph, which is called data flow, and showed good performance improvement. The base model architecture of it is bidirectional encoder representations from transformers (BERT), which uses the encoder part of a transformer. On the other hand, generative pre-trained transformer (GPT)—another multiple transformer architecture—uses the decoder part and shows great performance in the multilayer perceptron model. In this study, we investigate the improvement of code graphs with several variances on GPT-2 to refer to the abstract semantic tree used to collect the features of variables in the code. Here, we mainly focus on GPT-2 with additional features of code graphs that allow the model to learn the effect of the data stream. The experimental phase is divided into two parts: fine-tuning of the existing GPT-2 model, and pre-training from scratch using code data. When we pre-train a new model from scratch, the model produces an outperformed result compared with using the code graph with enough data.
Language Clustering for Multilingual Named Entity Recognition

Recent work in multilingual natural language processing has shown progress in various tasks such as natural language inference and joint multilingual translation. Despite success in learning across many languages, challenges arise where multilingual training regimes often boost performance on some languages at the expense of others. For multilingual named entity recognition (NER) we propose a simple technique that groups similar languages together by using embeddings from a pre-trained masked language model, and automatically discovering language clusters in this embedding space. Specifically, we fine-tune an XLM-Roberta model on a language identification task, and use embeddings from this model for clustering. We conduct experiments on 15 diverse languages in the WikiAnn dataset and show our technique largely outperforms three baselines: (1) training a multilingual model jointly on all available languages, (2) training one monolingual model per language, and (3) grouping languages by linguistic family. We also conduct analyses showing meaningful multilingual transfer for low-resource languages (Swahili and Yoruba), despite being automatically grouped with other seemingly disparate languages.
LexiClean: An annotation tool for rapid multi-task lexical normalisation

NLP systems are often challenged by difficulties arising from noisy, non-standard, and domain specific corpora.The task of lexical normalisation aims to standardise such corpora, but currently lacks suitable tools to acquire high-quality annotated data to support deep learning based approaches.In this paper, we present LexiClean 1 , the first open-source web-based annotation tool for multi-task lexical normalisation.LexiClean's main contribution is support for simultaneous in situ token-level modification and annotation that can be rapidly applied corpus wide.We demonstrate the usefulness of our tool through a case study on two sets of noisy corpora derived from the specialiseddomain of industrial mining.We show that LexiClean allows for the rapid and efficient development of high-quality parallel corpora.A demo of our system is available at: https: //youtu.be/P7_ooKrQPDU.
Hole Repairing Algorithm for 3D Point Cloud Model of Symmetrical Objects Grasped by the Manipulator

For the engineering application of manipulator grasping objects, mechanical arm occlusion and limited imaging angle produce various holes in the reconstructed 3D point clouds of objects. Acquiring a complete point cloud model of the grasped object plays a very important role in the subsequent task planning of the manipulator. This paper proposes a method with which to automatically detect and repair the holes in the 3D point cloud model of symmetrical objects grasped by the manipulator. With the established virtual camera coordinate system and boundary detection, repair and classification of holes, the closed boundaries for the nested holes were detected and classified into two kinds, which correspond to the mechanical claw holes caused by mechanical arm occlusion and the missing surface produced by limited imaging angle. These two kinds of holes were repaired based on surface reconstruction and object symmetry. Experiments on simulated and real point cloud models demonstrate that our approach outperforms the other state-of-the-art 3D point cloud hole repair algorithms.
University students’ perceptions on e-Learning: Cross-study in Portugal and Italy

The COVID-19 pandemic imposed dramatic changes on educational practices worldwide. This study aims to evaluate the distance learning experience of students during the COVID-19 pandemic by comparing results obtained in two countries: Italy and Portugal. A cross-sectional survey was used to analyse a survey questionnaire focused on two domains: digital skills and motivation to engage in distance learning. The results from Italian and Portuguese students indicated that majority showed a positive attitude towards online learning and the digital hybrid pedagogy. The recent e-learning experience created complex challenges and a variety of opportunities at the same time. Students and university staffs’ need for training was highly associated with motivation, engagement and connection with colleagues rather than with the infrastructures offered or the development of computer literacy.&#x0D; Keywords: COVID-19; digital skills; students' perceptions; students’ motivation.
Cybercrime and subculture of cybercriminals

Unlike traditional types of crime, such as murder or theft, that have a long history, cybercrime is a relatively young phenomenon and a new one that emerged with the advent of the Internet. It should be noted that the very nature of the Internet is quite favourable for committing crimes. Its properties such as globality, cross-border nature, the anonymity of users, wide audience coverage, distribution of main network nodes, and their interchangeability create advantages for cybercriminals who use the Internet at all stages of crime and also allow them to effectively hide from law enforcement agencies. An important aspect of cybercrime that contributes to its spread and hinders the fight against it is the subculture of cybercriminals. This subculture needs to be given special attention, so this issue is discussed in detail in the article. Therefore, the purpose of the article was to analyse cybercrime as one of the youngest types of crime in the modern world. The history of the emergence of cybercrime and cyber-security was considered, the types of cybercrime were characterized, and the functions of cybercrime were analysed.
Business School Student Satisfaction with Emergency Remote Teaching

This study analyzes Peruvian Business School students’ experiences with emergency remote teaching during the COVID-19 pandemic. We used the critical incident technique (CIT) to explore the salient factors on which students based their satisfaction and dissatisfaction with their online education. Data were collected through semi-structured interviews conducted with 27 MBA students. The results comprised 56 incidents – 25 satisfactory and 31 unsatisfactory – in the two major categories of Virtuality and the Teaching-Learning Process. This study contributes a new perspective on online education through the analysis, conducted through CIT, of the Peruvian Business School students’ experiences with online education during the COVID-19 pandemic. Some of the most relevant positive incidents included the recording of classes to review them later; the existence of Learning Management Systems to support the development of online materials; the possibility of entering online classes instead of in-person classes, as well as the flexibility with regard to location. The study allows us to determine several critical aspects in the two identified categories that should be considered when improving future online or hybrid programs.
An Enhanced Parallelisation Model for Performance Prediction of Apache Spark on a Multinode Hadoop Cluster

Big data frameworks play a vital role in storing, processing, and analysing large datasets. Apache Spark has been established as one of the most popular big data engines for its efficiency and reliability. However, one of the significant problems of the Spark system is performance prediction. Spark has more than 150 configurable parameters, and configuration of so many parameters is challenging task when determining the suitable parameters for the system. In this paper, we proposed two distinct parallelisation models for performance prediction. Our insight is that each node in a Hadoop cluster can communicate with identical nodes, and a certain function of the non-parallelisable runtime can be estimated accordingly. Both models use simple equations that allows us to predict the runtime when the size of the job and the number of executables are known. The proposed models were evaluated based on five HiBench workloads, Kmeans, PageRank, Graph (NWeight), SVM, and WordCount. The workload’s empirical data were fitted with one of the two models meeting the accuracy requirements. Finally, the experimental findings show that the model can be a handy and helpful tool for scheduling and planning system deployment.
Industrial Internet Federated Learning Driven by IoT Equipment ID and Blockchain

With the development of Internet of Things (IoT), 5G, and industrial technology, Industrial Internet has become an emerging research field. Due to the industrial specialty, higher requirements are put forward for time delay, safety, and stability of the identification analysis service. The traditional domain name system (DNS) cannot meet the requirements of industrial Internet because of the single form of identification subject and weak awareness of security protection. As a solution, this work applies blockchain and federated learning (FL) to the industrial Internet identification. Blockchain is a decentralized infrastructure widely used in digital encrypted currencies such as Bitcoin, which can make secure data storage and access possible. Federated learning protects terminal personal data privacy and can carry out efficient machine learning among multiple participants. The numerical results justify that our proposed federated learning and blockchain combination lays a strong foundation for the development of future industrial Internet.
HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition

Nested Named Entity Recognition (NNER) has been extensively studied, aiming to identify all nested entities from potential spans (i.e., one or more continuous tokens).However, recent studies for NNER either focus on tedious tagging schemas or utilize complex structures, which fail to learn effective span representations from the input sentence with highly nested entities.Intuitively, explicit span representations will contribute to NNER due to the rich context information they contain.In this study, we propose a Hierarchical Transformer (HiTRANS) network for the NNER task, which decomposes the input sentence into multi-grained spans and enhances the representation learning in a hierarchical manner.Specifically, we first utilize a two-phase module to generate span representations by aggregating context information based on a bottom-up and top-down transformer network.Then a label prediction layer is designed to recognize nested entities hierarchically, which naturally explores semantic dependencies among different spans.Experiments on GENIA, ACE-2004, ACE-2005 and NNE datasets demonstrate that our proposed method achieves much better performance than the state-of-the-art approaches.
More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge

Despite achieving remarkable performance, previous knowledge-enhanced works usually only use a single-source homogeneous knowledge base of limited knowledge coverage.Thus, they often degenerate into traditional methods because not all dialogues can be linked with knowledge entries.This paper proposes a novel dialogue generation model, MSKE-Dialog, to solve this issue with three unique advantages: (1) Rather than only one, MSKE-Dialog can simultaneously leverage multiple heterogeneous knowledge sources (it includes but is not limited to commonsense knowledge facts, text knowledge, infobox knowledge) to improve the knowledge coverage; (2) To avoid the topic conflict among the context and different knowledge sources, we propose a Multi-Reference Selection to better select context/knowledge; (3) We propose a Multi-Reference Generation to generate informative responses by referring to multiple generation references at the same time.Extensive evaluations on a Chinese dataset show the superior performance of this work against various state-of-the-art approaches.To our best knowledge, this work is the first to use the multi-source heterogeneous knowledge in the open-domain knowledge-enhanced dialogue generation.
Efficient Dialogue Complementary Policy Learning via Deep Q-network Policy and Episodic Memory Policy

Deep reinforcement learning has shown great potential in training dialogue policies.However, its favorable performance comes at the cost of many rounds of interaction.Most of the existing dialogue policy methods rely on a single learning system, while the human brain has two specialized learning and memory systems, supporting to find good solutions without requiring copious examples.Inspired by the human brain, this paper proposes a novel complementary policy learning (CPL) framework, which exploits the complementary advantages of the episodic memory (EM) policy and the deep Q-network (DQN) policy to achieve fast and effective dialogue policy learning.In order to coordinate between the two policies, we proposed a confidence controller to control the complementary time according to their relative efficacy at different stages.Furthermore, memory connectivity and time pruning are proposed to guarantee the flexible and adaptive generalization of the EM policy in dialog tasks.Experimental results on three dialogue datasets show that our method significantly outperforms existing methods relying on a single learning system.
How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI

Many real-world problems require the combined application of multiple reasoning abilities-employing suitable abstractions, commonsense knowledge, and creative synthesis of problem-solving strategies.To help advance AI systems towards such capabilities, we propose a new reasoning challenge, namely Fermi Problems (FPs), which are questions whose answers can only be approximately estimated because their precise computation is either impractical or impossible.For example, "How much would the sea level rise if all ice in the world melted?"FPs are commonly used in quizzes and interviews to bring out and evaluate the creative reasoning abilities of humans.To do the same for AI systems, we present two datasets: 1) A collection of 1k real-world FPs sourced from quizzes and olympiads; and 2) a bank of 10k synthetic FPs of intermediate complexity to serve as a sandbox for the harder real-world challenge.In addition to question-answer pairs, the datasets contain detailed solutions in the form of an executable program and supporting facts, helping in supervision and evaluation of intermediate steps.We demonstrate that even extensively fine-tuned large-scale language models perform poorly on these datasets, on average making estimates that are off by two orders of magnitude.Our contribution is thus the crystallization of several unsolved AI problems into a single, new challenge that we hope will spur further advances in building systems that can reason. Solving a Fermi ProblemHow much would the sea level rise if all the ice melted?Ice on land causes rise in sea levels.Div(a, b) Vol. of ice in the world?139.7 million mi² Area of ice?Area of ocean?Thickness of ice? 3 mi Area of Antarctica?Vol. of ice on land?How many Antarcticas fit in the world map?Vol. of ice in Antarctica?Mul(a, b) Ice on land Ice in Antarctica ≈ Area of Antarctica Area of ice in Antarctica