Reviewerly/exHarmony huggingface dataset:
Investigating the role of leadership and organisational culture in fostering innovation

To be competitive in the global market, businesses must innovate. Innovation is a force of change in today's competitive market, and organisations that reject change are at danger. The goal of the study was to figure out what leadership style and organisational culture may be used to create a long-term work environment that encourages organisational innovation. Companies cannot protect themselves against change, regardless of the size of their resource pool or their organisational excellence. Change brings with its danger and uncertainty, but it also brings with its opportunity. The decision to innovate must be accompanied by activities that establish an atmosphere that encourages individuals to be creative. For an organization's innovation initiatives to be effective, it must have a culture that encourages individuals to participate in innovation. The study employed quantitative method. To investigate this relationship, the researcher adapted a survey instrument to obtain the date from UAE firms. A Self-administrated questionnaire was distributed to employees who were randomly selected from the organisation. The data was collected through survey questionnaire from 500 participants working in the companies in UAE. The data was analysed by using smart-PLS software.
Core Competency Modeling for Elementary Artificial Intelligence Education

본 연구는 인공지능 교육이 공교육에 도입되기 시작하는 초등학교 고학년을 대상으로 인공지능 핵심역량을 모델링함으로써 인공지능 시대의 인재상을 명확히 하고, 인공지능 교육의 방향성 수립에 기초를 제공하는 것을 목적으로 한다. 이를 위하여 전문가 일대일 면담, 포커스 그룹 인터뷰, 델파이 조사 등 의견수렴 및 타당화 과정을 반복적으로 실시하였고, 이러한 과정에서 초등 인공지능 교육의 핵심역량 구성요인을 도출하고 타당성을 검증하였다. 인공지능 교육 핵심역량은 ‘인공지능 이해’ 범주에 15개 역량, ‘인공지능 윤리’ 범주에 8개 역량, ‘인공지능 사회정서’ 범주에 8개 역량이 포함되어 총 3개 범주 31개 역량이 도출되었다. 최종안에 대한 델파이 조사결과, 전체 응답평균 3.66/4점, CVI 평균 0.930, CVR 평균 0.863으로 CVI 최솟값 0.70과, CVR 최솟값 0.455를 상회하는 등 높은 공감대를 획득하였다. 특히, ‘인공지능 이해’ 범주의 ‘AI 인지’와 ‘AI 활용’, ‘인공지능 윤리’ 범주의 ‘AI 윤리 문제인식’, ‘인공지능 사회정서’ 범주의 ‘인간-AI 협업’과 ‘능동적 자기관리’ 역량의 타당도와 중요도가 높게 나타났다. 본 연구는 초등 맥락의 인공지능 교육 핵심역량 모델링을 통해 인공지능 이해 교육을 넘어 인공지능 윤리와 사회정서 교육까지로 시야를 넓히는데 기여하고자 하였으며, 초중등 인공지능 교육의 체계를 마련하는데 기초를 제시하였다. 또한 인공지능 교육에 역량중심 융합교육을 도입할 수 있도록 토대를 마련한 것에 의의를 가진다.The purpose of this study is to clarify the concept of talent for the artificial intelligence era and to provide the foundation for designing the directivity of artificial intelligence education by modeling core competencies for upper grades of elementary school students where artificial intelligence education is initiated in public education. To this end, experts one-to-one interviews, focus group interviews, and Delphi surveys were repeatedly conducted, in which process, core competency components of elementary artificial intelligence education were derived and validated. The core competencies of artificial intelligence education included 15 competencies in the ‘artificial intelligence understanding’ category, 8 competencies in the ‘artificial intelligence ethics’ category, and 8 competencies in the ‘artificial intelligence social emotions’ category, resulting in a total of 31 competencies in 3 categories. As a result of the Delphi survey on the final draft, the overall response average was 3.66/4 points, the CVI average was 0.930, and the CVR average was 0.863, which exceeded the CVI minimum value of 0.70 and the CVR minimum value of 0.455. In particular, the validity and importance of the competencies of 'AI awareness', 'AI utilization' in the 'artificial intelligence understanding' category, ‘AI ethics problem recognition’ in the 'artificial intelligence ethics’ category, and 'human-AI collaboration', 'active self-management' in the 'artificial intelligence social emotions' category were found to be high. This study attempted to contribute to expanding the perspective beyond artificial intelligence principle education to artificial intelligence ethics and social-emotional education through modeling of core competencies for elementary schoolers and presented the basis for the systematic perspective of artificial intelligence education. It is also meaningful to lay the foundation for introducing competency-based convergence education into artificial intelligence education.
PENGEMBANGAN SISTEM MONITORING PASIEN OLEH DOKTER PADA RSUD BANGIL BERBASIS WEB

&lt;strong&gt;Abstract : &lt;/strong&gt;&lt;em&gt;Bangil Regional General Hospital is one of the hospitals in Pasuruan Regency. RSUD Bangil was inaugurated in 1981. The Bangil General Hospital is a hospital owned by the local government of Pasuruan Regency, which historically started as a puskesmas which along with its development had difficulty accommodating more and more patients. In this study, the problems faced by health workers, especially doctors, were when monitoring their patients or when they saw the patient's current condition, while at the same time doctors were not always there, but some were on holiday, some were out of town. Doctors find it difficult to get information, especially regarding the patient's current condition at that time. Not to mention that there is a critical patient and requires instructions for administering medication or other doses, the purpose of this study is to assist doctors in monitoring patients by using the help of a website. With this website-based application, it is hoped that it can facilitate the work of doctors in monitoring patients and provide direct instructions so that services are better and do not need to come repeatedly to see and give drug instructions while out of town or on holidays&lt;/em&gt;.
Generation-Focused Table-Based Intermediate Pre-training for Free-Form Question Answering

Question answering over semi-structured tables has attracted significant attention in the NLP community. However, most of the existing work focus on questions that can be answered with short-form answer, i.e. the answer is often a table cell or aggregation of multiple cells. This can mismatch with the intents of users who want to ask more complex questions that require free-form answers such as explanations. To bridge the gap, most recently, pre-trained sequence-to-sequence language models such as T5 are used for generating free-form answers based on the question and table inputs. However, these pre-trained language models have weaker encoding abilities over table cells and schema. To mitigate this issue, in this work, we present an intermediate pre-training framework, Generation-focused Table-based Intermediate Pre-training (GENTAP), that jointly learns representations of natural language questions and tables. GENTAP learns to generate via two training objectives to enhance the question understanding and table representation abilities for complex questions. Based on experimental results, models that leverage GENTAP framework outperform the existing baselines on FETAQA benchmark. The pre-trained models are not only useful for free-form question answering, but also for few-shot data-to-text generation task, thus showing good transfer ability by obtaining new state-of-the-art results.
A Deep Learning-Based Face Mask Detector for Autonomous Nano-Drones (Student Abstract)

We present a deep neural network (DNN) for visually classifying whether a person is wearing a protective face mask. Our DNN can be deployed on a resource-limited, sub-10-cm nano-drone: this robotic platform is an ideal candidate to fly in human proximity and perform ubiquitous visual perception safely. This paper describes our pipeline, starting from the dataset collection; the selection and training of a full-precision (i.e., float32) DNN; a quantization phase (i.e., int8), enabling the DNN's deployment on a parallel ultra-low power (PULP) system-on-chip aboard our target nano-drone. Results demonstrate the efficacy of our pipeline with a mean area under the ROC curve score of 0.81, which drops by only ~2% when quantized to 8-bit for deployment.
Towards an Effective Orthogonal Dictionary Convolution Strategy

Orthogonality regularization has proven effective in improving the precision, convergence speed and the training stability of CNNs. Here, we propose a novel Orthogonal Dictionary Convolution Strategy (ODCS) on CNNs to improve orthogonality effect by optimizing the network architecture and changing the regularized object. Specifically, we remove the nonlinear layer in typical convolution block “Conv(BN) + Nonlinear + Pointwise Conv(BN)”, and only impose orthogonal regularization on the front Conv. The structure, “Conv(BN) + Pointwise Conv(BN)”, is then equivalent to a pair of dictionary and encoding, defined in sparse dictionary learning. Thanks to the exact and efficient representation of signal with dictionaries in low-dimensional projections, our strategy could reduce the superfluous information in dictionary Conv kernels. Meanwhile, the proposed strategy relieves the too strict orthogonality regularization in training, which makes hyper-parameters tuning of model to be more flexible. In addition, our ODCS can modify the state-of-the-art models easily without any extra consumption in inference phase. We evaluate it on a variety of CNNs in small-scale (CIFAR), large-scale (ImageNet) and fine-grained (CUB-200-2011) image classification tasks, respectively. The experimental results show that our method achieve a stable and superior improvement.
[Comprehensive evaluation method of real-time non-reference ultrasound image involving soft tissue deformation].

Ultrasound guided percutaneous interventional therapy has been widely used in clinic. Aiming at the problem of soft tissue deformation caused by probe contact force in robot-assisted ultrasound-guided therapy, a real-time non-reference ultrasound image evaluation method considering soft tissue deformation is proposed. On the basis of ultrasound image brightness and sharpness, a multi-dimensional ultrasound image evaluation index was designed, which incorporated the aggregation characteristics of the organization. In order to verify the effectiveness of the proposed method, ultrasound images of four different models were collected for experiments, including prostate phantom, phantom with cyst, pig liver tissue, and pig liver tissue with cyst. In addition, the correlation between subjective and objective evaluations was analyzed based on Spearman's rank correlation coefficient. Experimental results showed that the average evaluation time of a single image was 68.8 milliseconds. The evaluation time could satisfy real-time applications. The proposed method realizes the effective evaluation of real-time ultrasound image quality in robot-assisted therapy, and has good consistency with the evaluation of supervisors.超声引导的经皮介入治疗已在临床中得到了广泛应用。针对机器人辅助的超声引导治疗中探头接触力引起软组织形变的问题，提出一种考虑软组织形变的实时无参考超声图像综合评估方法。所提方法在超声图像亮度和锐度评估的基础上，融合组织或腺体的聚集特性设计了一种多维度的超声图像评价指标。为了验证所提方法的有效性，分别采集前列腺体模、内置囊肿的体模、猪肝脏组织，以及内置囊肿的猪肝脏组织4种不同模型的超声图像进行实验测试与分析，并基于斯皮尔曼秩相关系数分析了主客观评价的相关性。实验结果表明单幅图像的平均评估时间为68.8 ms，满足实时应用的需要。本文所提方法实现了机器人辅助治疗中实时超声图像的有效评估，并且评估结果与主观评价具有良好的一致性。.
Advancing Research on Capability Development and Reconfiguration

Given their complex, company-specific and history-dependent nature, organizational capabilities are generally considered as the foundation of a firm’s long-term strategy and as major source of competitive advantage. While the strategic significance of firm capabilities is uncontested, we still have an incomplete understanding how they emerge, develop, and change over time. In particular, given discontinuously shifting environments, the question of how incumbent firms reconfigure their capabilities in response to such changes and successfully move from one capability configuration to another, is of great significance. It is the aim of this symposium to push the discussion and advance the theoretical understanding in this realm. Topics such as capability rigidity, learning barriers, capability integration and redeployment as well as dynamic capabilities will be covered. The symposium will bring together different capability scholars and provide a platform for a productive discussion of how to push the field forward. Overcoming social breakdowns in capability reconfiguration Presenter: Joakim Hans Netz; Jönköping U. Presenter: Patrick Reinmoeller; IMD The best of two worlds? Corporate ventures for capability renewal of an incumbent Presenter: Kathrin Borner; Vrije U. Amsterdam Presenter: Fleur Deken; Vrije U. Amsterdam Presenter: Hans Berends; Vrije U. Amsterdam Presenter: Frans Feldberg; KIN Research, VU Amsterdam Reconfiguring or rather redeploying capabilities in the face of technological change? Presenter: Stefan Konlechner; Johannes Kepler U. Linz Presenter: Arne Keller; Vienna U. of Technology Presenter: Wolfgang H. Guettel; TU Wien Overcoming barriers to the open innovation capability: A cognitive perspective Presenter: Ralf Wilden; Macquarie Business School, Macquarie U. Presenter: Krithika Randhawa; U. of Technology, Sydney Presenter: Siegfried P. Gudergan; Waikato Management School
Suggestions for the church Planting of the Seventh-day Adventist Church from a Missional church Perspective

오늘날 전 세계적으로 기독교가 직면한 선교적 도전과 실존적 위기 현상에 관해 다양한 연구와 해결책이 제시되고 있다. 그중에는 교회의 정체성을 회복하여 원래 하나님이 고안하셨던 교회의 본 모습으로 돌아가자는 선교적교회 운동을 들 수 있다. 본 연구는 일상의 삶을 강조하는 선교적 교회의 실천과 더불어, 함께 강조되고 있는 교회개척의 중요성을 강조하고 그것의 실천적 필요성을 주장하는 것을 목적으로 한다. 이를 위해 재림교회 초기의 역사적 유산이었던 교회개척운동의 실제적 성과를 확인하고, 북미주와 호주에서의 오늘날 재림교회 교회개척을 위한 선교적 노력을 살펴보았다. 또한, 왜 교회개척이 교회의 본질적인 정체성을 회복하는 것인지를 설명하면서 그 이유를 다섯가지로 제시했다. 교회가 가진 통전적 개념, 하나님의 선교에 대한 본질적 이해, 교회의 사도성, 교회의 자연적 성장의 가장 중요한 요소 중 하나로서의 단순성, 그리고 교회의 지속적 건강 유지가 그것이다.Today, various studies and solutions have been proposed to the missional challenges and existential crisis phenomena faced by Christianity around the world. Among them is the missional church movement to restore the identity of the church and return to the original form of the church that God originally devised. In addition to the practice of missional churches emphasizing daily life, this study aims to emphasize the importance of church planting and to argue for its practical necessity. To this end, the researcher confirmed the actual achievements of the church planting movement, which was a historical heritage of the early days of the Adventist Church, and looked at the missional efforts to develop today's Adventist Church in North America and Australia. In addition, five reasons were suggested while explaining why church planting restores the essential identity of the church. They include the wholistic concept of the church, an essential understanding of God's mission, the apostolicity of the church, simplicity as one of the most important factors of the church's natural growth, and the maintenance of the church's continuous health.
The Design, Practical Realization and the Programming of a Didactical <i>MB-UGV-1</i> Mini Crawler Robot Prototype for Engineering Operations

Abstract The authors of this scientific paper would like to highlight some didactic-applied contributions regarding the design and the programming of an MB-UGV-1 robotic technological tracked product intended for engineering missions. The following aspects are highlighted: the presentation of the main organological elements from the mechanical structure of the tracked mini-robot, aspects regarding the command and programming elements of the technological product, the advantages and the disadvantages it has. The originality of this paper consists in the following elements: the manner of assembling the organological elements on the mechanical structure of the product, the writing of a program specific to and necessary for the mobility of each degree of freedom associated with the mechanism generating trajectories, the possibility of implementing the mini-robot in didactical applications.
Explainable Conversational Question Answering over Heterogeneous Sources

State-of-the-art conversational question answering (ConvQA) operates over homogeneous sources of information: either a knowledge base (KB), or a text corpus, or a collection of tables. This inherently limits the answer coverage of ConvQA systems. Therefore, during my PhD, we would like to tap into heterogeneous sources for answering conversational questions. Further, we plan to investigate the explainability of such ConvQA systems, to identify what helps users in understanding the answer derivation process.
Blockchainizing Food Law

Blockchain refers to distributed ledger technologies that can store, maintain, and update data collaboratively along a network of computing nodes. With the help of cryptography, peer-to-peer networks, and consensus mechanisms, data input to the blockchain is simultaneously and permanently recorded and updated in all the nodes of the network, ensuring a high level of consistency and authenticity of such data. Given such technological advantages, blockchain’s potential to revolutionize the global food supply chain seems salient: transforming paper-based documents into a blockchain-enabled identity to generate a high level of transparency and data integrity, enabling smaller farmers to bypass middlemen in crops trading and cash transfers, and providing an efficient and cost-effective way to manage the production system. In 2017, IBM announced a collaboration with a few major food producers and retailers, including, inter alia, Dole, Nestlé, Tyson Foods, Kroger, Unilever, and Wal-Mart, to leverage disruptive technologies such as blockchain to enhance quality control, food safety, management, and traceability. Similarly, the United Nations World Food Program launched the “Building Block” program in 2017, which uses iris-scanning technologies and blockchains to help Syrian refugees verify their identities and directly receive aid without intermediaries. Despite such promising developments, blockchain is not a silver bullet to solve all food governance issues. Rather, there may be some new challenges that need to be adequately addressed. As argued by this chapter, “blockchainizing” governance of food safety, traceability, and sustainability may pose another layer of regulatory questions about technical capacity and infrastructure gaps, scalability and implementation costs, global standardization politics, cybersecurity and data protection, and the technologically inherent limits of blockchain. In addition, policy challenges to both developed and developing countries (albeit in different ways) in terms of operational expertise and technical infrastructure, scalability and implementation costs, and power asymmetry in international standard-setting cannot be ignored. More generally, this chapter argues that such regulatory questions may call for a reconceptualization of the forms and substances of conventional food law and policy as well as data protection law, anti-trust law, and trade law. In this light, this chapter calls for a more technologically informed policy-making process before rushing into the hype of blockchainizing food law.
Few-shot Information Extraction is Here

Deep Learning has made tremendous progress in Natural Language Processing (NLP), where large pre-trained language models (PLM) fine-tuned on the target task have become the predominant tool. More recently, in a process called prompting, NLP tasks are rephrased as natural language text, allowing us to better exploit linguistic knowledge learned by PLMs and resulting in significant improvements. Still, PLMs have limited inference ability. In the Textual Entailment task, systems need to output whether the truth of a certain textual hypothesis follows from the given premise text. Manually annotated entailment datasets covering multiple inference phenomena have been used to infuse inference capabilities to PLMs.
Another Look at Information Retrieval as Statistical Translation

Over two decades ago, Berger and Lafferty proposed "information retrieval as statistical translation" (IRST), a simple and elegant method for ad hoc retrieval based on the noisy channel model. At the time, they lacked the large-scale human-annotated datasets necessary to properly train their models. In this paper, we ask the simple question: What if Berger and Lafferty had access to datasets such as the MS MARCO passage ranking dataset that we take for granted today? The answer to this question tells us how much of recent improvements in ranking can be solely attributed to having more data available, as opposed to improvements in models (e.g., pretrained transformers) and optimization techniques (e.g., contrastive loss). In fact, Boytsov and Kolter recently began to answer this question with a replication of Berger and Lafferty's model, and this work can be viewed as another independent replication effort, with generalizations to additional conditions not previously explored, including replacing the sum of translation probabilities with ColBERT's MaxSim operator. We confirm that while neural models (particularly pretrained transformers) have indeed led to great advances in retrieval effectiveness, the IRST model proposed decades ago is quite effective if provided sufficient training data.
Modeling User Behavior With Interaction Networks for Spam Detection

Spam is a serious problem plaguing web-scale digital platforms which facilitate user content creation and distribution. It compromises platform's integrity, performance of services like recommendation and search, and overall business. Spammers engage in a variety of abusive and evasive behavior which are distinct from non-spammers. Users' complex behavior can be well represented by a heterogeneous graph rich with node and edge attributes. Learning to identify spammers in such a graph for a web-scale platform is challenging because of its structural complexity and size. In this paper, we propose SEINE (Spam DEtection using Interaction NEtworks), a spam detection model over a novel graph framework. Our graph simultaneously captures rich users' details and behavior and enables learning on a billion-scale graph. Our model considers neighborhood along with edge types and attributes, allowing it to capture a wide range of spammers. SEINE, trained on a real dataset of tens of millions of nodes and billions of edges, achieves a high performance of 80% recall with 1% false positive rate. SEINE achieves comparable performance to the state-of-the-art techniques on a public dataset while being pragmatic to be used in a large-scale production system.
Financial Institution Development and Economic Growth: Dynamic Panel Evidence from the Developing Economies

This study aims to explore the impact of financial institution development (FID) on economic growth for the world and 54 developing economies from 2000 to 2019 with dynamic panel system GMM estimation. With regard to FID, this study considers 4 different indicators: access, depth, efficiency, and stability. Estimation results are as follows. First, the FID indicators (access, depth, and efficiency) have positive impact on economic growth for the world group and developing economies with stronger effect of access in developing economies. Second, the stability as another FID indicator, liquid assets, is negative only for the developing economies. It means that the higher ratio of liquid assets, the more incentive for financial institution to take on risk. These results were also maintained in the robustness check. Overall, this study suggests that developing economies should enhance their access to financial institution and put in place the measures to manage liquid assets.
One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search

Convolutional neural networks (CNNs) are used in numerous realworld applications such as vision-based autonomous driving and video content analysis.