Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence-Based Opportunities in Liver Pathology—A Systematic Review

Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field.The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields.42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias.DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.
EPT-Net: Edge Perception Transformer for 3D Medical Image Segmentation

The convolutional neural network has achieved remarkable results in most medical image seg- mentation applications. However, the intrinsic locality of convolution operation has limitations in modeling the long-range dependency. Although the Transformer designed for sequence-to-sequence global prediction was born to solve this problem, it may lead to limited positioning capability due to insufficient low-level detail features. Moreover, low-level features have rich fine-grained information, which greatly impacts edge segmentation decisions of different organs. However, a simple CNN module is difficult to capture the edge information in fine-grained features, and the computational power and memory consumed in processing high-resolution 3D features are costly. This paper proposes an encoder-decoder network that effectively combines edge perception and Transformer structure to segment medical images accurately, called EPT-Net. Under this framework, this paper proposes a Dual Position Transformer to enhance the 3D spatial positioning ability effectively. In addition, as low-level features contain detailed information, we conduct an Edge Weight Guidance module to extract edge information by minimizing the edge information function without adding network parameters. Furthermore, we verified the effectiveness of the proposed method on three datasets, including SegTHOR 2019, Multi-Atlas Labeling Beyond the Cranial Vault and the re-labeled KiTS19 dataset called KiTS19-M by us. The experimental results show that EPT-Net has significantly improved compared with the state-of-the-art medical image segmentation method.
Learning noise via dynamical decoupling of entangled qubits

Understanding noise in entangled systems is a prerequisite for developing scalable quantum computers. Here, we propose and apply multiqubit dynamical decoupling sequences that characterize noise during two-qubit gates. This noise is qualitatively different from the well-studied noise that leads to single-qubit dephasing; it simultaneously affects the two qubits, inducing fluctuations in their entangling parameter. In our superconducting system, the experimentally observed noise comes from coupler flux fluctuations and is observed to be non-Gaussian, leading to the stepwise decay of signals.
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Entanglement-assisted quantum error-correcting codes (EAQECCs) can improve information rate by using pre-shared entanglement between the sender and the receiver. In this paper, we construct three new infinite families of maximum distance separable (MDS) EAQECCs with less pre-shard maximally entangled states based on the Gray images of constacyclic codes over the finite non-chain ring Fq2+vFq2 with v2=v. The parameters of these MDS EAQECCs are not covered by the codes available in the previous literatures. Furthermore, our results generalize and improve the work of Lu et al. (2018) [16], (2020) [19].
Model-Predictive Control for Omnidirectional Mobile Robots in Logistic Environments Based on Object Detection Using CNNs

Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion control algorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictive controller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictive control approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot.
Augmented Reality Technology in Physical Education: A Systematic Review in Instructional Design, and AR Implementation Option Over the Last 5 Years

Augmented Reality (AR) is one of the advanced digital technologies that is being used in education. As its growth globally, significant research has demonstrated the value of AR as a pedagogical resource in a variety of educational settings. However, little is known regarding the contribution of AR technology in the teaching and learning process in the context of Physical Education (PE), necessitating a thorough investigation. Hence, this systematic review is presented to identify the most commonly used instructional model in developing AR materials for PE and its implementation option towards undergraduates over the previous 5 years. With a total of 22 research papers analysed in this review, this paper provides vital insight that meets the study’s objectives. The findings indicate that ADDIE was the most frequently used instructional design by the previous researchers, and AR technology adoption was not the only option for undergraduates in the last five years.
Adoption and sustainability of bitcoin and the blockchain technology in Nigeria

The rise of cryptocurrency, especially bitcoin, has opened up a lot of doors in the world of Financial Technology (FinTech) by attracting investors, media, and financial industry regulators. Bitcoin operates on blockchain technology, and its value is not a determinant of the value of a tangible asset, an organisation, or a country’s economy. Instead, it relies on an encryption technique that allows tracking of all transactions. Globally, over $2 trillion has been generated through cryptocurrency trading. Due to these financial prospects, the youths in Nigeria have cashed in on this virtual currency to create employment and wealth. This research investigates the adoption and sustainability of bitcoin and blockchain in Nigeria. A survey method with a non-probability purposive sampling technique and a homogeneous approach was employed to collect 320 responses via an online survey. Descriptive and correlational analysis in IBM SPSS version 25 was used to analyse the collected data. According to the findings, bitcoin is the most popular cryptocurrency, with 97.5% acceptance, and is expected to be the leading virtual currency in the next five years. The research findings will help researchers and authorities comprehend the need for cryptocurrency adoption, leading to its sustainability.
Key-aggregate searchable encryption supporting conjunctive queries for flexible data sharing in the cloud

Searchable encryption (SE) meets users' demand for the keyword search on encrypted data. Key-aggregate searchable encryption (KASE) improves data owners' ability to selectively share encrypted data with users. In KASE, the data owner encrypts different documents/document classes with distinct keys and can share any selected subset of documents by simply transmitting an aggregate key to the user. The user only uploads an aggregate trapdoor to the server for querying these shared documents. However, the existing KASE schemes have some limitations: the security definition is incomplete, only single-keyword search is supported, and the provable security scheme relies on the random oracle model. For these reasons, in this paper, we propose the Key-Aggregate Searchable Encryption supporting Conjunctive Queries (KASE-CQ) framework and its two security models: indistinguishability against selective-document chosen keyword attack and existential unforgeability against selective-document chosen keyword attack. These models reflect the indistinguishability of ciphertext and the unforgeability of the aggregate key, respectively. Our system supports flexible data sharing and the conjunctive keyword search on encrypted data. Furthermore, we design a concrete KASE-CQ construction, which can be proven secure in the standard model. We also demonstrate that our construction is secure against the insider trapdoor attack presented by Zhou et al. [40]. Finally, performance analysis and comparisons with Cui et al.'s scheme [10] illustrate the superior efficiency of our scheme.
High-Capacity Spatial Structured Light for Robust and Accurate Reconstruction

Spatial structured light (SL) can achieve three-dimensional measurements with a single shot. As an important branch in the field of dynamic reconstruction, its accuracy, robustness, and density are of vital importance. Currently, there is a wide performance gap of spatial SL between dense reconstruction (but less accurate, e.g., speckle-based SL) and accurate reconstruction (but often sparser, e.g., shape-coded SL). The central problem lies in the coding strategy and the designed coding features. This paper aims to improve the density and quantity of reconstructed point clouds by spatial SL whilst also maintaining a high accuracy. Firstly, a new pseudo-2D pattern generation strategy was developed, which can improve the coding capacity of shape-coded SL greatly. Then, to extract the dense feature points robustly and accurately, an end-to-end corner detection method based on deep learning was developed. Finally, the pseudo-2D pattern was decoded with the aid of the epipolar constraint. Experimental results validated the effectiveness of the proposed system.
Cogito Ergo Summ: Abstractive Summarization of Biomedical Papers via Semantic Parsing Graphs and Consistency Rewards

The automatic synthesis of biomedical publications catalyzes a profound research interest elicited by literature congestion. Current sequence-to-sequence models mainly rely on the lexical surface and seldom consider the deep semantic interconnections between the entities mentioned in the source document. Such superficiality translates into fabricated, poorly informative, redundant, and near-extractive summaries that severely restrict their real-world application in biomedicine, where the specialized jargon and the convoluted facts further emphasize task complexity. To fill this gap, we argue that the summarizer should acquire semantic interpretation over input, exploiting structured and unambiguous representations to capture and conserve the most relevant parts of the text content. This paper presents CogitoErgoSumm, the first framework for biomedical abstractive summarization equipping large pre-trained language models with rich semantic graphs. Precisely, we infuse graphs from two complementary semantic parsing techniques with different goals and granularities—Event Extraction and Abstract Meaning Representation, also designing a reward signal to maximize information content preservation through reinforcement learning. Extensive quantitative and qualitative evaluations on the CDSR dataset show that our solution achieves competitive performance according to multiple metrics, despite using 2.5x fewer parameters. Results and ablation studies indicate that our joint text-graph model generates more enlightening, readable, and consistent summaries. Code available at: https://github.com/disi-unibo-nlp/cogito-ergo-summ.
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from a Conditional Causal Perspective

Few-shot learning models learn representations with limited human annotations, and such a learning paradigm demonstrates practicability in various tasks, e.g., image classification, object detection, etc. However, few-shot object detection methods suffer from an intrinsic defect that the limited training data makes the model cannot sufficiently explore semantic information. To tackle this, we introduce knowledge distillation to the few-shot object detection learning paradigm. We further run a motivating experiment, which demonstrates that in the process of knowledge distillation, the empirical error of the teacher model degenerates the prediction performance of the few-shot object detection model as the student. To understand the reasons behind this phenomenon, we revisit the learning paradigm of knowledge distillation on the few-shot object detection task from the causal theoretic standpoint, and accordingly, develop a Structural Causal Model. Following the theoretical guidance, we propose a backdoor adjustment-based knowledge distillation method for the few-shot object detection task, namely Disentangle and Remerge (D&amp;R), to perform conditional causal intervention toward the corresponding Structural Causal Model. Empirically, the experiments on benchmarks demonstrate that D&amp;R can yield significant performance boosts in few-shot object detection. Code is available at https://github.com/ZYN-1101/DandR.git.
Small Object Detection and Tracking: A Comprehensive Review

Object detection and tracking are vital in computer vision and visual surveillance, allowing for the detection, recognition, and subsequent tracking of objects within images or video sequences. These tasks underpin surveillance systems, facilitating automatic video annotation, identification of significant events, and detection of abnormal activities. However, detecting and tracking small objects introduce significant challenges within computer vision due to their subtle appearance and limited distinguishing features, which results in a scarcity of crucial information. This deficit complicates the tracking process, often leading to diminished efficiency and accuracy. To shed light on the intricacies of small object detection and tracking, we undertook a comprehensive review of the existing methods in this area, categorizing them from various perspectives. We also presented an overview of available datasets specifically curated for small object detection and tracking, aiming to inform and benefit future research in this domain. We further delineated the most widely used evaluation metrics for assessing the performance of small object detection and tracking techniques. Finally, we examined the present challenges within this field and discussed prospective future trends. By tackling these issues and leveraging upcoming trends, we aim to push forward the boundaries in small object detection and tracking, thereby augmenting the functionality of surveillance systems and broadening their real-world applicability.
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing

In this paper, we propose a novel SQL guided pre-training framework STAR for contextdependent text-to-SQL parsing, which leverages contextual information to enrich natural language (NL) utterance and table schema representations for text-to-SQL conversations.Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation.In addition, we construct a high-quality large-scale context-dependent text-to-SQL conversation corpus to pre-train STAR.Extensive experiments show that STAR achieves new state-ofthe-art performance on two downstream benchmarks (SPARC and COSQL), significantly outperforming previous pre-training methods and ranking first on the leaderboard.We believe the release of the constructed corpus, codebase and pre-trained STAR checkpoints would push forward the research in this area.For reproducibility, we release our code and data at https:
On the Paradox of Learning to Reason from Data

Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained end-to-end to solve logical reasoning problems presented in natural language? We attempt to answer this question in a confined problem space where there exists a set of parameters that perfectly simulates logical reasoning. We make observations that seem to contradict each other: BERT attains near-perfect accuracy on in-distribution test examples while failing to generalize to other data distributions over the exact same problem space. Our study provides an explanation for this paradox: instead of learning to emulate the correct reasoning function, BERT has, in fact, learned statistical features that inherently exist in logical reasoning problems. We also show that it is infeasible to jointly remove statistical features from data, illustrating the difficulty of learning to reason in general. Our result naturally extends to other neural models (e.g. T5) and unveils the fundamental difference between learning to reason and learning to achieve high performance on NLP benchmarks using statistical features.
PEMODELAN SPASIAL PERUBAHAN TUTUPAN LAHAN DAN PREDIKSI TUTUPAN LAHAN KECAMATAN TELUK AMBON BAGUALA MENGGUNAKAN CA-MARKOV

Teluk Ambon Baguala District, is one of the sub-districts in Ambon City which has experienced a significant increase in population. Along with the increasing population, the need for residential, infrastructure and industrial land is increasing. This will certainly have an impact on the conversion of other cover to uncontrolled built-up land, environmental damage and natural disasters. This study aims to analyze land cover changes in Teluk Ambon Baguala District in 2014, 2018, 2022 and predict land cover models in 2026, 2030 and 2034. The driving factors used are slope, land height and distance from the road. This study uses the CA-Markov method to predict future land cover. The results of the analysis of changes and predictions of land cover in 2014, 2018, 2022, 2026, 2030 and 2034 show that built-up land continues to increase in area along with increasing population and the high demand for built-up land which is increasing.
DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence

Recently, there has been a growing interest in designing text generation systems from a discourse coherence perspective, e.g., modeling the interdependence between sentences. Still, recent BERT-based evaluation metrics are weak in recognizing coherence, and thus are not reliable in a way to spot the discourse-level improvements of those text generation systems. In this work, we introduce DiscoScore, a parametrized discourse metric, which uses BERT to model discourse coherence from different perspectives, driven by Centering theory. Our experiments encompass 16 non-discourse and discourse metrics, including DiscoScore and popular coherence models, evaluated on summarization and document-level machine translation (MT). We find that (i) the majority of BERT-based metrics correlate much worse with human rated coherence than early discourse metrics, invented a decade ago; (ii) the recent state-of-the-art BARTScore is weak when operated at system level—which is particularly problematic as systems are typically compared in this manner. DiscoScore, in contrast, achieves strong system-level correlation with human ratings, not only in coherence but also in factual consistency and other aspects, and surpasses BARTScore by over 10 correlation points on average. Further, aiming to understand DiscoScore, we provide justifications to the importance of discourse coherence for evaluation metrics, and explain the superiority of one variant over another. Our code is available at https://github.com/AIPHES/DiscoScore.
Automatic Temporal Relation in Multi-Task Learning

Multi-task learning with temporal relation is a common prediction method for modelling the evolution of a wide range of systems. Considering the inherent relations between multiple time points, many works apply multi-task learning to jointly analyse all time points, with each time point corresponding to a prediction task. The most difficult challenge is determining how to fully explore and thus exploit the shared valuable temporal information between tasks to improve the generalization performance and robustness of the model. Existing works are classified as temporal smoothness and mean temporal relations. Both approaches, however, utilize a predefined and symmetric task relation structure that is too rigid and insufficient to adequately capture the intricate temporal relations between tasks. Instead, we propose a novel mechanism named Automatic Temporal Relation (AutoTR) for directly and automatically learning the temporal relation from any given dataset. To solve the biconvex objective function, we adopt the alternating optimization and show that the two related sub-optimization problems are amenable to closed-form computation of the proximal operator. To solve the two problems efficiently, the accelerated proximal gradient method is used, which has the fastest convergence rate of any first-order method. We have preprocessed six public real-life datasets and conducted extensive experiments to fully demonstrate the superiority of AutoTR. The results show that AutoTR outperforms several baseline methods on almost all datasets with different training ratios, in terms of overall model performance and every individual task performance. Furthermore, our findings verify that the temporal relation between tasks is asymmetrical, which has not been considered in previous works. The implementation source can be found at https://github.com/menghui-zhou/AutoTR.