Reviewerly/exHarmony huggingface dataset:
Why, where and how are organizations using blockchain in their supply chains? Motivations, application areas and contingency factors

Purpose Blockchain is increasingly being considered for applications in operations and supply chain management. However, evidence from practice is still scarce on why, where and how organizations seek to apply the technology in the supply chain across different industries. The study develops a comprehensive framework to enhance understanding of the application areas of blockchain technology in the supply chain, as well as organizations' motivations in seeking blockchain solutions and relevant contingency factors influencing applications. Design/methodology/approach The authors investigate 50 use cases of blockchain applications in the supply chain, covering six industries. Contingency theory is applied in conducting a qualitative textual and correlation analysis to identify and compare blockchain adoption motivations, application areas and contingency factors across different industries. Findings The analysis develops an evidence-based framework that captures ten principal motivations in seeking blockchain solutions, three main blockchain application areas along with important application sub-categories and five clusters of contingency factors that influence blockchain deployment and its uses in different industrial sectors. Research limitations/implications The study expands the limited cross-sectoral research on blockchain applications and motivations in the supply chain. Using contingency theory, it presents a comprehensive framework that captures the drivers and factors relating to blockchain adoption in the supply chain in a nomological network. The study lays the foundation for further theoretical perspectives and empirical research to investigate relevant sectoral characteristics and their importance for different types of blockchain application in the supply chain. Practical implications The study informs practitioners about potential supply chain application areas that can be enhanced through blockchain technology, taking account of the specific characteristics of their products, business and manufacturing processes, supply network configurations, industry standards, regulations and market demand. Originality/value The study is the first to provide cross-sectoral evidence on the relevance of organizations' motivations and numerous contingency factors on blockchain application areas in the supply chain.
Information bottleneck-based interpretable multitask network for breast cancer classification and segmentation

Breast cancer is one of the most common causes of death among women worldwide. Early signs of breast cancer can be an abnormality depicted on breast images (e.g., mammography or breast ultrasonography). However, reliable interpretation of breast images requires intensive labor and physicians with extensive experience. Deep learning is evolving breast imaging diagnosis by introducing a second opinion to physicians. However, most deep learning-based breast cancer analysis algorithms lack interpretability because of their black box nature, which means that domain experts cannot understand why the algorithms predict a label. In addition, most deep learning algorithms are formulated as a single-task-based model that ignores correlations between different tasks (e.g., tumor classification and segmentation). In this paper, we propose an interpretable multitask information bottleneck network (MIB-Net) to accomplish simultaneous breast tumor classification and segmentation. MIB-Net maximizes the mutual information between the latent representations and class labels while minimizing information shared by the latent representations and inputs. In contrast from existing models, our MIB-Net generates a contribution score map that offers an interpretable aid for physicians to understand the model’s decision-making process. In addition, MIB-Net implements multitask learning and further proposes a dual prior knowledge guidance strategy to enhance deep task correlation. Our evaluations are carried out on three breast image datasets in different modalities. Our results show that the proposed framework is not only able to help physicians better understand the model’s decisions but also improve breast tumor classification and segmentation accuracy over representative state-of-the-art models. Our code is available at https://github.com/jxw0810/MIB-Net.
Implementation of digital technology for student involvement based on a 3D quest game for career guidance and assessing students' digital competences

This article describes the process of developing a career advice 3D adventure game for applicants interested in working in IT departments. The game is based on a 3D representation of the computer science and information technologies department at the Kharkiv Aviation Institute. The quest challenges are designed to measure applicants' and first-year students' digital competency. The theoretical foundation, software tools, development stages, implementation obstacles, and gaming application scenario were all used in the article. The game scenario includes a virtual tour of a 3D university department. In terms of how closely the game resembles real-life stuff, applicants can examine the department's equipment and classrooms. The team used C# and C++, Unity 3D, and Source Engine to create the game application. We used Hammer Editor, Agisoft PhotoScan Pro, and photogrammetry technology to model objects for realistic gaming. Based on the Digital Competence Framework for Citizens (DigComp 2.2), players can assess their digital competences in a variety of ways, including a test activity, a puzzle, assembling a computer, and putting up an IT-specialist firm. The experiment conducted at the online open house day 2020 demonstrated the efficiency of the 3D quest game. A 3D quest was rated as a more modern and appealing kind of involvement by the candidates. According to the 3D quest findings, applicants displayed an average degree of digital competence, with certain specific item challenges graded at 0.5. Several psychometric item parameters were thoroughly examined in order to increase the item's quality.
The impact of inconsistent human annotations on AI driven clinical decision making

In supervised learning model development, domain experts are often used to provide the class labels (annotations). Annotation inconsistencies commonly occur when even highly experienced clinical experts annotate the same phenomenon (e.g., medical image, diagnostics, or prognostic status), due to inherent expert bias, judgments, and slips, among other factors. While their existence is relatively well-known, the implications of such inconsistencies are largely understudied in real-world settings, when supervised learning is applied on such 'noisy' labelled data. To shed light on these issues, we conducted extensive experiments and analyses on three real-world Intensive Care Unit (ICU) datasets. Specifically, individual models were built from a common dataset, annotated independently by 11 Glasgow Queen Elizabeth University Hospital ICU consultants, and model performance estimates were compared through internal validation (Fleiss' κ = 0.383 i.e., fair agreement). Further, broad external validation (on both static and time series datasets) of these 11 classifiers was carried out on a HiRID external dataset, where the models' classifications were found to have low pairwise agreements (average Cohen's κ = 0.255 i.e., minimal agreement). Moreover, they tend to disagree more on making discharge decisions (Fleiss' κ = 0.174) than predicting mortality (Fleiss' κ = 0.267). Given these inconsistencies, further analyses were conducted to evaluate the current best practices in obtaining gold-standard models and determining consensus. The results suggest that: (a) there may not always be a "super expert" in acute clinical settings (using internal and external validation model performances as a proxy); and (b) standard consensus seeking (such as majority vote) consistently leads to suboptimal models. Further analysis, however, suggests that assessing annotation learnability and using only 'learnable' annotated datasets for determining consensus achieves optimal models in most cases.
Towards quantum enhanced adversarial robustness in machine learning

Machine learning algorithms are powerful tools for data-driven tasks such as image classification and feature detection. However, their vulnerability to adversarial examples—input samples manipulated to fool the algorithm—remains a serious challenge. The integration of machine learning with quantum computing has the potential to yield tools offering not only better accuracy and computational efficiency, but also superior robustness against adversarial attacks. Indeed, recent work has employed quantum-mechanical phenomena to defend against adversarial attacks, spurring the rapid development of the field of quantum adversarial machine learning (QAML) and potentially yielding a new source of quantum advantage. Despite promising early results, there remain challenges in building robust real-world QAML tools. In this Perspective, we discuss recent progress in QAML and identify key challenges. We also suggest future research directions that could determine the route to practicality for QAML approaches as quantum computing hardware scales up and noise levels are reduced. To fulfil the potential of quantum machine learning for practical applications in the near future, it needs to be robust against adversarial attacks. West and colleagues give an overview of recent developments in quantum adversarial machine learning, and outline key challenges and future research directions to advance the field.
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, documentlevel and multimodal scenarios in knowledge base population.DeepKE implements various information extraction tasks, including named entity recognition, relation extraction and attribute extraction.With a unified framework, DeepKE allows developers and researchers to customize datasets and models to extract information from unstructured data according to their requirements.Specifically, DeepKE not only provides various functional modules and model implementation for different tasks and scenarios but also organizes all components by consistent frameworks to maintain sufficient modularity and extensibility.We release the source code at GitHub 1 with Google Colab tutorials and comprehensive documents 2 for beginners.Besides, we present an online system 3 for real-time extraction of various tasks, and a demo video 4 .
Ranking challenges, risks and threats using Fuzzy Inference System

This paper presents a Fuzzy Inference System (FIS) designed to comprehensively assess challenges, risks, and threats. In the realm of security and defense, defining these elements is inherently uncertain and complex. The paper addresses this challenge by integrating fuzzy logic into the model. As a pivotal instrument for decision-making, the model not only facilitates the precise identification of challenges, risks, and threats but also provides vital support for the strategic and doctrinal document development process. The methodology proves instrumental in reconciling divergent perspectives, aligning theoretical intricacies with practical applications. By effectively capturing the nuanced interplay between variables, the model offers a dynamic framework that enhances the accuracy and efficiency of security-related decision-making.
CodeT5+: Open Code Large Language Models for Code Understanding and Generation

Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations. First, they often adopt a specific architecture (encoder-only or decoder-only) or rely on a unified encoder-decoder network for different downstream tasks, lacking the flexibility to operate in the optimal architecture for a specific task. Secondly, they often employ a limited set of pretraining objectives which might not be relevant to some tasks and hence result in substantial performance degrade. To address these limitations, we propose “CodeT5+”, a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of code tasks. Such flexibility is enabled by our proposed mixture of pretraining objectives, which cover span denoising, contrastive learning, text-code matching, and causal LM pretraining tasks, on both unimodal and bimodal multilingual code corpora. Furthermore, we propose to initialize CodeT5+ with frozen off-the-shelf LLMs without training from scratch to efficiently scale up our models, and explore instruction-tuning to align with natural language instructions. We extensively evaluate CodeT5+ on over 20 code-related benchmarks in different settings, including zero-shot, finetuning, and instruction-tuning. We observe state-of-the-art (SoTA) performance on various code-related tasks, and our instruction-tuned CodeT5+ 16B achieves new SoTA results of 35.0% pass@1 and 54.5% pass@10 on the HumanEval code generation task against other open code LLMs, even surpassing the OpenAI code-cushman-001 model.
Puppet Search: Enhancing Scripted Behavior by Look-Ahead Search with Applications to Real-Time Strategy Games

Real-Time Strategy (RTS) games have shown to be very resilient to standard adversarial tree search techniques. Recently, a few approaches to tackle their complexity have emerged that use game state or move abstractions, or both. Unfortunately, the supporting experiments were either limited to simpler RTS environments (uRTS, SparCraft) or lack testing against state-of-the-art game playing agents. Here, we propose Puppet Search, a new adversarial search framework based on scripts that can expose choice points to a look-ahead search procedure. Selecting a combination of a script and decisions for its choice points represents a move to be applied next. Such moves can be executed in the actual game, thus letting the script play, or in an abstract representation of the game state which can be used by an adversarial tree search algorithm. Puppet Search returns a principal variation of scripts and choices to be executed by the agent for a given time span. We implemented the algorithm in a complete StarCraft bot. Experiments show that it matches or outperforms all of the individual scripts that it uses when playing against state-of-the-art bots from the 2014 AIIDE StarCraft competition.
Quantum Query Algorithms Are Completely Bounded Forms

We prove a characterization of $t$-query quantum algorithms in terms of the unit ball of a space of degree-$(2t)$ polynomials. Based on this, we obtain a refined notion of approximate polynomial degree that equals the quantum query complexity, answering a question of Aaronson et al. [``Polynomials, Quantum Query Complexity, and Grothendieck's Inequality," in Proceedings of the 31st Conference on Computational Complexity, CCC 2016, Schloss Dagstuh, 2016, pp. 25:1--25:19]. Our proof is based on a fundamental result of Christensen and Sinclair [J. Funct. Anal., 72 (1987), pp. 151--181] that generalizes the well-known Stinespring representation for quantum channels to multilinear forms. Using our characterization, we show that many polynomials of degree four are far from those coming from two-query quantum algorithms. We also give a simple and short proof of one of the results of Aaronson et al. showing an equivalence between one-query quantum algorithms and bounded quadratic polynomials.
DHC: A Distributed Hierarchical Clustering Algorithm for Large Datasets

Hierarchical clustering is a classical method to provide a hierarchical representation for the purpose of data analysis. However, in practical applications, it is difficult to deal with massive datasets due to their high computation complexity. To overcome this challenge, this paper presents a novel distributed storage and computation hierarchical clustering algorithm, which has a lower time complexity than the standard hierarchical clustering algorithms. Our proposed approach is suitable for hierarchical clustering on massive datasets, which has the following advantages. First, the algorithm is able to store massive dataset exceeding the main memory space by using distributed storage nodes. Second, the algorithm is able to efficiently process nearest neighbor searching along parallel lines by using distributed computation at each node. Extensive experiments are carried out to validate the effectiveness of the DHC algorithm. Experimental results demonstrate that the algorithm is 10 times faster than the standard hierarchical clustering algorithm, which is an effective and flexible distributed algorithm of hierarchical clustering for massive datasets.
Faithful measure of quantum non-Gaussianity via quantum relative entropy

We introduce a measure of quantum non-Gaussianity (QNG) for those quantum states not accessible by a mixture of Gaussian states in terms of quantum relative entropy. Specifically, we employ a convex-roof extension using all possible mixed-state decompositions beyond the usual pure-state decompositions. We prove that this approach brings a QNG measure fulfilling the properties desired as a proper monotone under Gaussian channels and conditional Gaussian operations. As an illustration, we explicitly calculate QNG for the noisy single-photon states and demonstrate that QNG coincides with non-Gaussianity of the state itself when the single-photon fraction is sufficiently large.
Tracking Logical Difference in Large-Scale Ontologies: A Forgetting-Based Approach

This paper explores how the logical difference between two ontologies can be tracked using a forgetting-based or uniform interpolation (UI)-based approach. The idea is that rather than computing all entailments of one ontology not entailed by the other ontology, which would be computationally infeasible, only the strongest entailments not entailed in the other ontology are computed. To overcome drawbacks of existing forgetting/uniform interpolation tools we introduce a new forgetting method designed for the task of computing the logical difference between different versions of large-scale ontologies. The method is sound and terminating, and can compute uniform interpolants for ALC-ontologies as large as SNOMED CT and NCIt. Our evaluation shows that the method can achieve considerably better success rates (&gt;90%) and provides a feasible approach to computing the logical difference in large-scale ontologies, as a case study on different versions of SNOMED CT and NCIt ontologies shows.
Experimental demonstration of quantum learning speedup with classical input data

We consider quantum-classical hybrid machine learning in which large-scale input channels remain classical and small-scale working channels process quantum operations conditioned on classical input data. This does not require the conversion of classical (big) data to a quantum superposed state, in contrast to recently developed approaches for quantum machine learning. We performed optical experiments to illustrate a single-bit universal machine, which can be extended to a large-bit circuit for a binary classification task. Our experimental machine exhibits quantum learning speedup of approximately $36%$, as compared with the fully classical machine. In addition, it features strong robustness against dephasing noise.
Experimental Implementation of Efficient Quantum Pseudorandomness on a 12-Spin System

Quantum pseudorandomness, also known as unitary designs, comprise a powerful resource for quantum computation and quantum engineering. While it is known in theory that pseudorandom unitary operators can be constructed efficiently, realizing these objects in realistic physical systems can be a challenging task. In this work, we study quantum pseudorandomness generation on a 12-spin nuclear magnetic resonance system. The experimental process is based on the recently proposed design Hamiltonian approach, which has the merit of being significantly more efficient than previous protocols. By applying random refocusing sequences to the experimental system we create a design Hamiltonian the dynamics of which quickly forms unitary designs. We then use multiple-quantum techniques to measure spreading of quantum coherences over system's degrees of freedom, and so to probe the growth of quantum pseudorandomness. The measured multiple-quantum coherence spectra indicate that substantial quantum pseudorandomness have been achieved.
Surgical tool tracking based on two CNNs: from coarse to fine

The current convolution neural network (CNN)-based methods suffer from mass computation and low frame rate without special acceleration, they cannot be applied in some real applications. The authors propose a coarse to fine method for surgical tool tracking based on CNNs. Two CNNs are designed for the coarse and fine locations of the surgical tool. The coarse CNN is a classification network of 10 classes, and the fine CNN is a regression network for the tool tip area. The spatial and temporal context updating makes two CNNs cooperating together for the whole tracking process of surgical tool. The authors validate their method in the experiments with eight datasets, where there are two ex vivo datasets and six in vivo datasets, as well as comparing their method with other four methods in terms of accuracy and speed of tracking. The ex vivo and in vivo experiments demonstrate that the method gives consideration to both accuracy and speed, and provides a good accuracy and a high frame rate of tracking. For the future research of multi-tool tracking, the design of new regression CNN with the output of tensor will be focused, which contains multiple tools' labels and tracking information.
The usability analysis of using augmented reality for linus students

&lt;p&gt;Mobile technology with Augmented Reality has become popular worldwide with a broad range of users, including students from all levels of education and the impact of mobile technology in classrooms has been extensively studied. This technology can be the source of motivation for LINUS students especially students with disabilities. The word usability also refers to the methods to improve the ease of use during a design process. Among the difficulties in teaching a LINUS student is the lack of visual media for understanding the subject especially in basic mathematics such as evaluating a number and calculating the number. The objective of the study was to evaluate the usability of using Augmented Reality in a mobile application among LINUS students in primary schools. This study used survey data gathered from 32 LINUS students of 3 different primary schools in Marang and Kuala Terengganu Districts. The questionnaire collected data on five construct of usability test to the LINUS students. The students used the mobile application while being guide by their teacher. They navigated all the buttons provided and answered the quiz too. The criteria of usability test consist of five constructs. The students showed a significant interest in learning numbers by actively participating in the LINUS sessions. The usability level was measured based on the five constructs. In summary, the augmented reality mobile application has a great potential to be used in teaching and learning, as in the Malaysia Education Development Plan 2013-2025 especially on the LINUS students.&lt;em&gt;&lt;/em&gt;&lt;/p&gt;