Reviewerly/exHarmony huggingface dataset:
MANet: a multi-attention network for automatic liver tumor segmentation in computed tomography (CT) imaging

Automatic liver tumor segmentation is a paramount important application for liver tumor diagnosis and treatment planning. However, it has become a highly challenging task due to the heterogeneity of the tumor shape and intensity variation. Automatic liver tumor segmentation is capable to establish the diagnostic standard to provide relevant radiological information to all levels of expertise. Recently, deep convolutional neural networks have demonstrated superiority in feature extraction and learning in medical image segmentation. However, multi-layer dense feature stacks make the model quite inconsistent in imitating visual attention and awareness of radiological expertise for tumor recognition and segmentation task. To bridge that visual attention capability, attention mechanisms have developed for better feature selection. In this paper, we propose a novel network named Multi Attention Network (MANet) as a fusion of attention mechanisms to learn highlighting important features while suppressing irrelevant features for the tumor segmentation task. The proposed deep learning network has followed U-Net as the basic architecture. Moreover, residual mechanism is implemented in the encoder. Convolutional block attention module has split into channel attention and spatial attention modules to implement in encoder and decoder of the proposed architecture. The attention mechanism in Attention U-Net is integrated to extract low-level features to combine with high-level ones. The developed deep learning architecture is trained and evaluated on the publicly available MICCAI 2017 Liver Tumor Segmentation dataset and 3DIRCADb dataset under various evaluation metrics. MANet demonstrated promising results compared to state-of-the-art methods with comparatively small parameter overhead.
Semi-Automation of Meta-Theoretic Proofs in Beluga

We present a sound and complete focusing calculus for the core of the logic behind the proof assistant Beluga as well as an overview of its implementation as a tactic in Beluga's interactive proof environment Harpoon.The focusing calculus is designed to construct uniform proofs over contextual LF and its meta-logic in Beluga: a dependently-typed first-order logic with recursive definitions.The implemented tactic is intended to complete straightforward sub-cases in proofs allowing users to focus only on the interesting aspects of their proofs, leaving tedious simple cases to Beluga's theorem prover.We demonstrate the effectiveness of our work by using the tactic to simplify proving weak-head normalization for the simply-typed lambda-calculus.as recursive dependently-typed programs, following the Curry-Howard isomorphism.To ease proof developments, a tactic-based prover Harpoon was recently added [11].Users may now construct proofs interactively using a small set of high-level actions similar to those in Abella, and, upon completion of a proof, will be presented with a proof script which can be translated to a Beluga program that may be independently type-checked.It is still the case however that much human interaction is required for Harpoon proof developments.In this paper we present a focusing calculus designed to build uniform proofs over the core of Beluga's two-level logic, and provide an overview of its implementation as a Harpoon tactic.The tactic is meant to solve simple lemmas and simple cases of proofs, allowing users to concentrate on the interesting aspects of a proof.We have proven that the focusing calculus, presented in Section 2.3, is sound and complete with respect to the cut-free sequent calculus for Beluga, presented in Section 2.2.2.We have used our tactic on a number of interesting case studies in PL theory, which we summarize in Section 3.2, including type preservation and value soundness for MiniML (without fix points), weak-head normalization for the simply-typed lambda-calculus, and the Church-Rosser theorem for the untyped lambda-calculus.We have seen that they allow for automatic completion of many of the simpler lemmas and subcases of these theorems.We believe automating proof search over the core of Beluga results in simpler proof developments, making it more appealing to users looking to verify formal systems.
Determinants of central bank digital currency adoption – a study of 85 countries

This study attempts to explore the macroeconomic development factors that determine countries’ decisions to implement CBDCs. The study uses data regarding CBDC adoption and macroeconomic variables from 85 countries spanning the 2013–2021 period. The results of the probit regression analysis show that higher levels of financial inclusion, net foreign assets, remittances, and income are associated with a higher probability of CBDC adoption. In contrast, higher values for the Human Development Index, manufacturing value added, and population growth rate are inversely correlated with the probability of CBDC adoption. The findings of the study have implications for both investors and policymakers.
A Named Entity Recognition Approach for Electronic Medical Records Using BERT Semantic Enhancement and BiLSTM

Aiming at the problems of missing local context features, single word vector representation, and low entity recognition accuracy, a method for e-medical recording with named entity recognition, which is based on BERT and model fusion, is proposed. First, with the model of BERT for pre-training, the preceding and following contextual information is fused for the enhancement of word semantic representation and alleviation of the problem of polysemy; second, the network of bi-directional long-short term memory is for obtaining the sequence feature matrix, generation of optimal sequence in global sense achieved through the conditional random field model; finally, data enhancement is used to alleviate the class imbalance and improve the model ability in generalization. Results of the experiments find model proposal measured by F1 on CCKS21 data set reaches 0.8548, which is 0.51% and 0.08% higher than models with ID-CNNs-CRF and multi-task RNN. This demonstrates the excellent performance of the method proposed in this paper in improving named entity recognition.
Application of Genetic Programming to Unsolved Mathematical Problems II

In this research, the authors developed a new Swift programming library of symbolic regression based on genetic programming. Symbolic regression is a field of artificial intelligence where AI looks for formulas that describe the given data. The data used for the research is winning positions of combinatorial games. Compared to the library presented at the last GCCE conference, this library gets two new features to select the fittest formulae.The first is to find a minimum number of formulae that describe the given data.The second is to separate the data into smaller subsets, and find formulae to describe each subset.With these two new features, this new Swift programming library of symbolic regression can be a powerful tool in the research of mathematics and science.
A Proposal of Auxiliary Line Generation Algorithm for Portrait Drawing Learning Assistant System Using OpenPose and OpenCV

Traditionally, portrait drawing has been a basic skill of painting. To help novices practice drawing portraits using digital technologies, we have developed a Portrait Drawing Learning Assistant System (PDLAS). By providing the auxiliary lines of a human face, PDLAS can guide portrait drawing of novices. In this paper, we propose an algorithm to generate the auxiliary lines from a given face photo. It adopts OpenCV and OpenPose with Python, which have been commonly used in computer vision studies including human pose estimations, to extract facial features that are necessary for generating them. To evaluate the proposal, we applied it to 50 face photos of people with different genders, ages, and complexions, and generated the auxiliary lines from them. The results proved the validity of the algorithm.
A Multi-Task Learning and Data Augmentation-Based Pose Estimation Algorithm

Pose estimation is a fundamental task in computer vision that aims to localize human body joints in images or videos. However, the performance of pose estimation algorithms can be limited by various factors such as occlusions, complex body poses, and variations in clothing and lighting. To address these challenges, we propose a novel pose estimation algorithm based on multi-task learning and data augmentation. Our method utilizes a multi-task learning framework to incorporate multiple datasets as training data, leading to improved learning performance. We also employ various data augmentation techniques to improve the generalization ability of the network and mitigate the effects of data imbalance. Experimental results on the COCO dataset and MPII Human Pose dataset demonstrate that our method has higher accuracy and stronger robustness compared to the baseline algorithm on pose estimation tasks.
Who Needs External References?—Text Summarization Evaluation Using Original Documents

Nowadays, individuals can be overwhelmed by a huge number of documents being present in daily life. Capturing the necessary details is often a challenge. Therefore, it is rather important to summarize documents to obtain the main information quickly. There currently exist automatic approaches to this task, but their quality is often not properly assessed. State-of-the-art metrics rely on human-generated summaries as a reference for the evaluation. If no reference is given, the assessment will be challenging. Therefore, in the absence of human-generated reference summaries, we investigated an alternative approach to how machine-generated summaries can be evaluated. For this, we focus on the original text or document to retrieve a metric that allows a direct evaluation of automatically generated summaries. This approach is particularly helpful in cases where it is difficult or costly to find reference summaries. In this paper, we present a novel metric called Summary Score without Reference—SUSWIR—which is based on four factors already known in the text summarization community: Semantic Similarity, Redundancy, Relevance, and Bias Avoidance Analysis, overcoming drawbacks of common metrics. Therefore, we aim to close a gap in the current evaluation environment for machine-generated text summaries. The novel metric is introduced theoretically and tested on five datasets from their respective domains. The conducted experiments yielded noteworthy outcomes, employing the utilization of SUSWIR.
Using a Responsive Feedback Approach to Develop and Pilot a Counseling Chatbot to Strengthen Child Nutrition in Rural India

Nearly half of all deaths in children in India aged younger than 5 years are attributable to undernutrition. Reinforcing the caregiver's positive behaviors through multiple channels can lead to improved child nutrition outcomes. We describe the development and piloting of a chatbot to improve nutrition outcomes for children aged 0-12 months.We engaged key stakeholders to understand existing interventions to improve nutrition outcomes and developed a theory of change that included an intervention that provides nutrition-related information directly to beneficiaries. A chatbot, Poshan Didi, was developed to provide individual counseling to mothers with children aged 0-12 months on age-appropriate, nutrition-related topics. The chatbot was piloted in Katni district from February 2019 to October 2019 in 2 phases to investigate the acceptability and feasibility of the chatbot (Phase 1, n=10 mothers) and to assess whether users would continue to be engaged and would engage with both the automated content as well as through free form questions (Phase 2, n=100 mothers). Quantitative and qualitative data were collected in focus group discussions with health care workers and mothers (n=4) and interviews with mothers (n=26).Mothers viewed the chatbot as being a private channel to discuss topics and referred to Poshan Didi's persona as being knowledgeable. Eighty percent of users replied at least once to a chatbot-generated message. Sixty-four percent engaged beyond the standard chatbot content by discussing at least 1 issue with a nurse who responded to escalated messages.This work demonstrates how chatbots could create multiple interaction touchpoints between caregivers and health care workers to increase caregivers' access to age-appropriate nutrition counseling and information. The study shows the value of using the responsive feedback approach in the chatbot design and implementation to improve the efficacy of the digital tool.
Separability discrimination and decomposition of quantum mixed states based on the Broyden-Fletcher-Goldfarb-Shanno algorithm

In this paper, we consider separability discrimination and decomposition problems of quantum mixed states based on the tensor optimization method. We first convert the separability determination problem of quantum mixed states to the positive Hermitian decomposition problem of Hermitian tensors. Then we study a rank-$R$ positive Hermitian approximation model of Hermitian tensors and introduce a Broyden--Fletcher--Goldfarb--Shanno (BFGS) algorithm for rank-$R$ positive Hermitian approximation. We prove that the BFGS algorithm can be used to compute a decomposition of a mixed state if it is separable. Finally, we propose a BFGS algorithm for separability determination and decomposition of quantum mixed states. Numerical examples show the effectiveness of the algorithm.
Public health measures and the rise of incidental surveillance: Considerations about private informational power and accountability

The public health measures implemented in response to the COVID-19 pandemic have resulted in a substantially increased shared reliance on private infrastructure and digital services in areas such as healthcare, education, retail, and the workplace. This development has (i) granted a number of private actors significant (informational) power, and (ii) given rise to a range of digital surveillance practices incidental to the pandemic itself. In this paper, we reflect on these secondary consequences of the pandemic and observe that, even though collateral data disclosure and additional activity monitoring appears to have been generally socially accepted as inevitable consequences of the pandemic, part and parcel of a larger conglomeration of emergency compromises, these increased surveillance practices were not directly justified by appeals to solidarity and public health in the same way that the instigating public health measures were. Based on this observation, and given the increased reliance on private actors for maintaining the digital space, we argue that governments have a duty to (i) seek and ensure that there are justifications for collateral data disclosure and activity monitoring by private actors in the context of (future) public health emergencies like the COVID-19 pandemic, and (ii) regulate and provide accountability mechanisms for and oversight over these private surveillance practices on par with governmental essential services that engage in surveillance activities.
Continuous variable port-based teleportation

Abstract Port-based teleportation (PBT) is a generalisation of the standard teleportation protocol which does not require unitary operations by the receiver. This comes at the price of requiring N &gt; 1 entangled pairs, while N = 1 for the standard teleportation protocol. The lack of correction unitaries allows PBT to be used as a fundamental theoretical tool to simulate arbitrary channels with a general resource, with applications to study fundamental limits of quantum communication, cryptography and sensing, and to define general programmable quantum computers. Here we introduce a general formulation of port-based teleportation in continuous variable systems and study in detail the N = 2 case. In particular, we interpret the resulting channel as an energy truncation and analyse the kinds of channels that can be naturally simulated after this restriction.
Secure Instruction and Data-Level Information Flow Tracking Model for RISC-V

With the proliferation of electronic devices, third-party intellectual property (3PIP) integration in the supply chain of the semiconductor industry and untrusted actors/fields have raised hardware security concerns that enable potential attacks, such as unauthorized access to data, fault injection and privacy invasion. Different security techniques have been proposed to provide resilience to secure devices from potential vulnerabilities; however, no one technique can be applied as an overarching solution. We propose an integrated Information Flow Tracking (IFT) technique to enable runtime security to protect system integrity by tracking the flow of data from untrusted communication channels. Existing hardware-based IFT schemes are either fine-, which are resource-intensive, or coarse-grained models, which have minimal precision logic, providing either control-flow or data-flow integrity. No current security model provides multi-granularity due to the difficulty in balancing both the flexibility and hardware overheads at the same time. This study proposes a multi-level granularity IFT model that integrates a hardware-based IFT technique with a gate-level-based IFT (GLIFT) technique, along with flexibility, for better precision and assessments. Translation from the instruction level to the data level is based on module instantiation with security-critical data for accurate information flow behaviors without any false conservative flows. A simulation-based IFT model is demonstrated, which translates the architecture-specific extensions into a compiler-specific simulation model with toolchain extensions for Reduced Instruction Set Architecture (RISC-V) to verify the security extensions. This approach provides better precision logic by enhancing the tagged mechanism with 1-bit tags and implementing an optimized shadow logic that eliminates the area overhead by tracking the data for only security-critical modules.
An Efficient Steganographic Protocol for WebP Files

In this paper, several ideas of data hiding in WebP images are presented. WebP is a long-known, but not very poplar file format that provides lossy or lossless compression of data, in the form of a still image or an animation. A great number of WebP features are optional, so the structure of the image offers great opportunities for data hiding. The article describes distinct approaches to steganography divided into two categories: format-based and data-based. Among format-based methods, we name simple injection, multi-secret steganography that uses thumbnails, hiding a message in metadata or in a specific data chunk. Data-based methods achieve secret concealment with the use of a transparent, WebP-specific algorithm that embeds bits by choosing proper prediction modes and alteration of the color indexing transform. The capacity of presented techniques varies. It may be unlimited for injection, up to a few hundred megabytes for other format-based algorithms, or be content-dependent in data-based techniques. These methods fit into the container modification branch of steganography. We also present a container selection technique which benefits from available WebP compression parameters. Images generated with the described methods were tested with three applications, including the Firefox web browser, GNU Image Manipulation Program, and ImageMagick. Some of the presented techniques can be combined in order to conceal more than one message in a single carrier.
‘Let us teach our children’: Online racism and everyday far-right ideologies on TikTok

This paper identifies perceptions of injustice, grievance, and alienation as online drivers of radicalisation by concentrating on contemporary visual radicalisation patterns.It focuses on far-right agents of radicalisation in the UK with a particular analysis of visual and ephemeral drivers of radicalisation on social media platforms.We analysed widespread TikTok hashtags which embody mainstream right-wing ideologies.Using these hashtags, we selected four popular videos (> 30k views) for visual thematic analysis of their compositional content and comment-sphere to explore everyday representations and discourses of farright ideologies.Our findings highlight mundane online expressions on TikTok that collectively reinforce notions of a shared idealised identity built on nostalgic reinterpretations of an imperial past, which contribute to the mainstreaming of far-right ideas and ideologies.
Qualitative Analysis of the Construction of the Leadership Model of Principals in Small-Scale Rural Schools in China-Exploration Based on Grounded Theory

Background and Aims: There are a huge number of rural small-scale schools in China, and they have “normally existed”. After entering the new era, its core value positioning is to meet the high-quality education needs of the people who want to go to a good school nearby. This study aims to understand the leadership improvement mechanism of small-scale rural school principals in China Materials and methods: This study interviewed 12 small-scale rural school principals in G City, J Province, China, and coded the collected original data with the grounded theory. Results: The results show that the influencing factors of the leadership of small-scale rural school principals in China: are personal level, school level, family level, and social level. Conclusions: This study constructed the leadership improvement mechanism of small-scale rural school principals in China.
Exploring the opportunities and challenges of social media use in teaching and learning processes at public universities in Ethiopia

This study explored the opportunities and challenges of integrating social media into the teaching and learning process of Ethiopian universities. Universities have been under the influence of the impact of social media use among academia, students, and researchers. These inevitable academic needs addressed through a lens of uses and gratifications and social constructivism theory where used as a theoretical framework to conduct and analyze this study. A qualitative case study design used to investigate the current study by collecting data through in-depth interviews and focus group discussions. A purposive sampling technique was used to select 37 participants where 32 were students and five department heads respectively. The main findings indicate that social media platforms such as Telegram, Email and YouTube are widely used by both teachers and students for educational purposes. Participants demonstrated that their university's use of social media for teaching and learning is commendable-valued, yet there is also a need to expand on its current utilization. Besides, social media paves improved communication, information sharing, collaborative learning, flexibility, cost-time savings, academic and research collaboration, fosters enthusiasm and creativity, and smooth relationships among the communicators. Moreover, the study reveals that distractions from non-educational content, lack of responsibility, unethical and informal language usage, lack of feedback, infrastructure issues, confusion and dependence, blurring of boundaries between students and teachers, and academic dishonesty are the major drawbacks of utilizing social media in the teaching and learning processes. These findings will be important to take some lessons and improve the quality of social media use in teaching and learning processes. We suggest self-regulation and management when utilizing social media for educational purposes.