Reviewerly/exHarmony huggingface dataset:
A Novel Elastomeric UNet for Medical Image Segmentation

Medical image segmentation is of important support for clinical medical applications. As most of the current medical image segmentation models are limited in the U-shaped structure, to some extent the deep convolutional neural network (CNN) structure design is hard to be accomplished. The design in this study mimics the way the wave is elastomeric propagating, extending the structure from both the horizontal and spatial dimensions for realizing the Elastomeric UNet (EUNet) structure. The EUNet can be divided into two types: horizontal EUNet and spatial EUNet, based on the propagation direction. The advantages of this design are threefold. First, the training structure can be deepened effectively. Second, the independence brought by each branch (a U-shaped design) makes the flexible design redundancy available. Finally, a horizontal and vertical series-parallel structure helps on feature accumulation and recursion. Researchers can adjust the design according to the requirements to achieve better segmentation performance for the independent structural design. The proposed networks were evaluated on two datasets: a self-built dataset (multi-photon microscopy, MPM) and publicly benchmark retinal datasets (DRIVE). The results of experiments demonstrated that the performance of EUNet outperformed the UNet and its variants.
Mobile Assisted Language Learning: Evaluation of Accessibility, Adoption, and Perceived Outcome among Students of Higher Education

The present study was aimed at analyzing students’ perceptions toward mobile assisted language learning (MALL), their use patterns, and their usefulness in their academic outcomes. The results indicated that students use widely different MALL software for different applications. In a survey of 581 students from Indian colleges and universities, the research identified five antecedent factors (namely, individual desire and motives, perceived ease of use, technological factors, social influence, and perceived usefulness) which influenced students in the areas of adopting MALL software, students’ readiness, students’ motivation, and the subsequent effect on student performances. The research further indicated that students’ readiness and their motivation level mediated the relationship between factors of adoption of MALL and perceived outcome. The relevance of positive language learning outcomes, theoretical contribution and managerial implications of the study are discussed.
Bounds for the Security of Ascon against Differential and Linear Cryptanalysis

The NIST Lightweight Cryptography project aims to standardize symmetric cryptographic designs, including authenticated encryption and hashing, suitable for constrained devices. One essential criterion for the evaluation of the 10 finalists is the evidence for their security against attacks like linear and differential cryptanalysis. For Ascon, one of the finalists and previous winner of the CAESAR competition in the ‘lightweight’ category, there is a large gap between the proven bounds and the best known characteristics found with heuristic tools: The bounds only cover up to 3 rounds with 15 differentially and 13 linearly active S-boxes, insufficient for proving a level of security for the full constructions.In this paper, we propose a new modeling strategy for SAT solvers and derive strong bounds for the round-reduced Ascon permutation. We prove that 4 rounds already ensure that any single characteristic has a differential probability or squared correlation of at most 2−72, and 6 rounds at most 2−108. This is significantly below the bound that could be exploited within the query limit for keyed Ascon modes. These bounds are probably not tight. To achieve this result, we propose a new search strategy of dividing the search space into a large number of subproblems based on ‘girdle patterns’, and show how to exploit the rotational symmetry of Ascon using necklace theory. Additionally, we evaluate and optimize several aspects of the pure SAT model, including the counter implementation and parallelizability, which we expect to be useful for future applications to other models.
Self-guided quantum state tomography for limited resources

Quantum state tomography is a process for estimating an unknown quantum state; which is innately probabilistic. The exponential growth of unknown parameters to be estimated is a fundamental difficulty in realizing quantum state tomography for higher dimensions. Iterative optimization algorithms like self-guided quantum tomography have been effective in robust and accurate ascertaining a quantum state even with exponential growth in Hilbert space. We propose a faster convergent simultaneous perturbation stochastic approximation algorithm which is more practical in a resource-deprived situation for determining the underlying quantum states by incorporating the Barzilai-Borwein two-point step size gradient method with minimal loss of accuracy.
Balancing Precision and Recall for Neural Biomedical Event Extraction

Biomedicalevent extraction is an essential task in the biomedical research. Existing models suffer from the issue of low recall due to the large proportion of unrecognized events and inflexible event argument combination. To address this issue, we propose an end-to-end multi-task approach for biomedical event extraction. Our model is able to achieve balanced precision and recall with several nichetargeting designs. First, neural encoders with rich lexical and syntactic features are used and shared by multiple subtasks such as event trigger recognition and argument relation extraction, in order to enhance the generalizability of the model. Second, a novel auxiliary subtask is added to identify the proteins that participate in the events, which helps decreasing the challenge of mining event-related proteins from the large candidate space. Third, event argument combination is performed using a strong neural network rather than inflexible rules or templates, to further increase the recall, especially for complex nested events. To demonstrate the effectiveness of our model, we evaluate it on two widely-used biomedical event extraction datasets used in the BioNLP 2011 and 2013 shared tasks. Our model achieves the state-of-the-art results (63.15% and 55.67% in F1 score) by significantly improving the recalls (compared with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepEvnetMine<inline-formula><tex-math notation="LaTeX">$_{SciBERT}$</tex-math></inline-formula></i> , 4.65% and 5.0%) on the two datasets. Further experiments and analyses show the effectiveness of our proposed features and modules in the model.
Drivers of Research Outcomes in Developing Countries: The Case of Lebanon

This paper uses a unique dataset from Lebanon, a developing country with unstable political conditions, to explore the drivers of research outcomes. We use the Negative Binomial model to empirically examine the determinants of the total number of publications and single and co-authored articles. The results indicate that males are more likely to publish co-authored papers than females. Moreover, our findings show a quadratic relationship between age and the number of published papers with a peak at the age of 40. After this turning point, the publication rate starts to decrease at an increasing rate. When we run the model by gender, we find that females in large departments tend to publish more co-authored papers. We also find that full professors tend to publish more papers in Q1 and Q2 journals, while associate professors have more papers in Q2 and Q3 journals.
Variational <i>Ansatz</i> preparation to avoid CNOT-gates on noisy quantum devices for combinatorial optimizations

The variational quantum eigensolver (VQE), which is a quantum–classical hybrid approach, has latent powers to leverage near-term quantum devices by effectively managing a limited number of qubits with finite coherent lifetimes. While it is generally argued that the quantum approximate optimization algorithm (QAOA), which is a special case of VQE with a variational Ansatz based on the adiabatic theorem, may enable practical applications of noisy quantum devices for classical combinatorial optimizations, the strategy to improve the performance of this algorithm by increasing the circuit depth conflicts with the limited coherence time of near-term quantum devices. Here, we introduce strategies involving the VQE to reduce the circuit resources required for solving combinatorial optimizations. Our concept of a parameterized quantum circuit allows the Ansatz preparation to be achieved by only single-qubit operation. We find that the variational Ansatz without controlled X-gates leads to quick convergence in a classical subroutine used to determine the variational parameters. In addition, the variational Ansatz with optimized parameters maintains performance over the problem sizes both on the numerical simulation and IBM 27-qubit processor “ibm_kawasaki.” Therefore, the variational Ansatz introduced in this study has several advantages considering the total calculation time and performance scaling over the problem sizes. We also show that the variational Ansatz consisting of a lower number of gate operations than that of QAOA can approximate the eigenstates of diagonal Hamiltonians with high accuracy. We illustrate our ideas with a maximum-cut problem and show that near-term quantum applications may be feasible using short-depth circuits.
Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model

Abstract Objective To develop a framework for identifying temporal clinical event trajectories from Observational Medical Outcomes Partnership-formatted observational healthcare data. Materials and Methods A 4-step framework based on significant temporal event pair detection is described and implemented as an open-source R package. It is used on a population-based Estonian dataset to first replicate a large Danish population-based study and second, to conduct a disease trajectory detection study for type 2 diabetes patients in the Estonian and Dutch databases as an example. Results As a proof of concept, we apply the methods in the Estonian database and provide a detailed breakdown of our findings. All Estonian population-based event pairs are shown. We compare the event pairs identified from Estonia to Danish and Dutch data and discuss the causes of the differences. The overlap in the results was only 2.4%, which highlights the need for running similar studies in different populations. Conclusions For the first time, there is a complete software package for detecting disease trajectories in health data.
Improving Facial Emotion Recognition Using Residual Autoencoder Coupled Affinity Based Overlapping Reduction

Emotion recognition using facial images has been a challenging task in computer vision. Recent advancements in deep learning has helped in achieving better results. Studies have pointed out that multiple facial expressions may present in facial images of a particular type of emotion. Thus, facial images of a category of emotion may have similarity to other categories of facial images, leading towards overlapping of classes in feature space. The problem of class overlapping has been studied primarily in the context of imbalanced classes. Few studies have considered imbalanced facial emotion recognition. However, to the authors’ best knowledge, no study has been found on the effects of overlapped classes on emotion recognition. Motivated by this, in the current study, an affinity-based overlap reduction technique (AFORET) has been proposed to deal with the overlapped class problem in facial emotion recognition. Firstly, a residual variational autoencoder (RVA) model has been used to transform the facial images to a latent vector form. Next, the proposed AFORET method has been applied on these overlapped latent vectors to reduce the overlapping between classes. The proposed method has been validated by training and testing various well known classifiers and comparing their performance in terms of a well known set of performance indicators. In addition, the proposed AFORET method is compared with already existing overlap reduction techniques, such as the OSM, ν-SVM, and NBU methods. Experimental results have shown that the proposed AFORET algorithm, when used with the RVA model, boosts classifier performance to a greater extent in predicting human emotion using facial images.
Multi-Attack Detection: General Defense Strategy Based on Neural Networks for CV-QKD

The security of the continuous-variable quantum key distribution (CVQKD) system is subject to various attacks by hackers. The traditional detection method of parameter estimation requires professionals to judge known attacks individually, so the general detection model emerges to improve the universality of detection; however, current universal detection methods only consider the independent existence of attacks but ignore the possible coexistence of multiple attacks in reality. Here, we propose two multi-attack neural network detection models to handle the coexistence of multiple attacks. The models adopt two methods in multi-label learning: binary relevance (BR) and label power (LP) to deal with the coexistence of multiple attacks and can identify attacks in real-time by autonomously learning the features of known attacks in a deep neural network. Further, we improve the model to detect unknown attacks simultaneously. The experimental results show that the proposed scheme can achieve high-precision detection for most known and unknown attacks without reducing the key rate and maximum transmission distance.
Regulation and management of the biosecurity for synthetic biology

Synthetic biology (SynBio) is a high-profile interdiscipline combining engineering with science. As a dual-purpose discipline, SynBio is bringing large changes to many fields and providing great benefits to humans. However, due to its characteristic of complexity and uncertainty, SynBio also presents potential biosafety and biosecurity risks. Biosecurity risks refer to unauthorized access, loss, theft, misuse, diversion or intentional release. If a biosecurity accident happens, it would pose a huge threat to humans and nature. Therefore, it is crucial to establish a set of regulations and management practices for the biosecurity risks of SynBio. In this paper, we summarized the sources of the biosecurity risks of SynBio, from its research materials, products, technologies, information to Do-it-yourself synthetic biology. We reviewed and analyzed the current situation of regulation and management of biosecurity for SynBio in the international community and in China. We found that in most countries and regions, SynBio risks commonly follow the regulation and management of Genetically Modified Organisms which has loopholes if applied to the regulation for SynBio without any amendments. Here, we proposed suggestions for the Chinese-featured regulation and management of biosecurity for SynBio, including a top-to-bottom governing framework, a think-tank implementation mechanism, a Synthetic Biology Laboratory Biosecurity Manual safeguarding system, and strengthening biosecurity education on synthetic biology and self-regulation awareness among relevant personnel. Through this work, we aim to improve the standardized process of biosecurity regulation and management for SynBio in China and thereby map out a peaceful, profitable, and practical development path for synthetic biology.
Blind Person Assistant: Object Detection

Abstract: It’s a known fact that estimated number of blind persons in the world is about 285 million, approximately equal to the 20% of the Indian Population. They are mostly dependent on someone for even accessing their basic daily needs. In our project, we used TensorFlow, it's a new library from Google. TensorFlow model our neural networks. The TensorFlow Object Detection API is used to detect many objects. We have Introduce an algorithm (SSD). SSD uses a similar phase while training, to match the appropriate anchor box with the bounding boxes of each ground truth object within an image. Essentially, the anchor box with the highest degree of flap with an object is responsible for predicting that object's class and its location. It has microcontroller which has wi-fi inbuilt module. This guide is convenient and offers data to the client to move around in new condition, regardless of whether indoor or open air, through an ease to use interface. Then again, and so as to lessen route challenges of the visually impaired, a deterrent location framework utilizing ultrasounds is added to this gadget. The proposed framework identifies the closest hindrance through ultrasonic sensors and it gives an alert to illuminate the visually impaired about its confinement. Keywords: Object Detection, Blind Person, location, ultrasonic sensors, gadget
Energy-Efficient Scientific Workflow Scheduling Algorithm in Cloud Environment

Scheduling extensive scientific applications that are deadline-aware (usually referred to as workflow) is a difficult task. This research provides a virtual machine (VM) placement and scheduling approach for effectively scheduling process tasks in the cloud environment while maintaining dependency and deadline constraints. The suggested model’s aim is to reduce the application’s energy consumption and total execution time while taking into account dependency and deadline limitations. To select the VM for the tasks and dynamically deploy/undeploy the VM on the hosts based on the jobs’ requirements, an energy-efficient VM placement (EVMP) algorithm is presented. Demonstrate that the proposed approach outperforms the existing PESVMC (power-efficient scheduling and VM consolidation) algorithm.
A Rationale-Centric Framework for Human-in-the-loop Machine Learning

We present a novel rationale-centric framework with human-in-the-loop -Rationales-centric Double-robustness Learning (RDL) -to boost model out-of-distribution performance in few-shot learning scenarios.By using static semi-factual generation and dynamic humanintervened correction, RDL exploits rationales (i.e.phrases that cause the prediction), human interventions and semi-factual augmentations to decouple spurious associations and bias models towards generally applicable underlying distributions, which enables fast and accurate generalisation.Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-distribution tests compared to many state-of-the-art benchmarks-especially for few-shot learning scenarios.We also perform extensive ablation studies to support in-depth analyses of each component in our framework.* These authors contributed equally to this work.This video is 100% bad with the brain cell killing acting and plot.
How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation

Graph neural networks (GNNs), as a group of powerful tools for representation learning on irregular data, have manifested superiority in various downstream tasks. With unstructured texts represented as concept maps, GNNs can be exploited for tasks like document retrieval. Intrigued by how can GNNs help document retrieval, we conduct an empirical study on a large-scale multi-discipline dataset CORD-19. Results show that instead of the complex structure-oriented GNNs such as GINs and GATs, our proposed semantics-oriented graph functions achieve better and more stable performance based on the BM25 retrieved candidates. Our insights in this case study can serve as a guideline for future work to develop effective GNNs with appropriate semantics-oriented inductive biases for textual reasoning tasks like document retrieval and classification. All code for this case study is available at https://github.com/HennyJie/GNN-DocRetrieval .
Deep Learning with Logical Constraints

In recent years, there has been an increasing interest in exploiting logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) able to learn from less data, and/or (iii) guaranteed to be compliant with the background knowledge itself, e.g., for safety-critical applications. In this survey, we retrace such works and categorize them based on (i) the logical language that they use to express the background knowledge and (ii) the goals that they achieve.
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning

Recently, finetuning a pretrained language model to capture the similarity between sentence embeddings has shown the state-of-theart performance on the semantic textual similarity (STS) task.However, the absence of an interpretation method for the sentence similarity makes it difficult to explain the model output.In this work, we explicitly describe the sentence distance as the weighted sum of contextualized token distances on the basis of a transportation problem, and then present the optimal transport-based distance measure, named RCMD; it identifies and leverages semantically-aligned token pairs.In the end, we propose CLRCMD, a contrastive learning framework that optimizes RCMD of sentence pairs, which enhances the quality of sentence similarity and their interpretation.Extensive experiments demonstrate that our learning framework outperforms other baselines on both STS and interpretable-STS benchmarks, indicating that it computes effective sentence similarity and also provides interpretation consistent with human judgement.The code and checkpoint are publicly available at https: //github.com/sh0416/clrcmd.
Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering

Texts in scene images convey critical information for scene understanding and reasoning.The abilities of reading and reasoning matter for the model in the text-based visual question answering (TextVQA) process.However, current TextVQA models do not center on the text and suffer from several limitations.The model is easily dominated by language biases and optical character recognition (OCR) errors due to the absence of semantic guidance in the answer prediction process.In this paper, we propose a novel Semantics-Centered Network (SC-Net) that consists of an instance-level contrastive semantic prediction module (ICSP) and a semantics-centered transformer module (SCT).Equipped with the two modules, the semantics-centered model can resist the language biases and the accumulated errors from OCR. Extensive experiments on TextVQA and ST-VQA datasets show the effectiveness of our model.SC-Net surpasses previous works with a noticeable margin and is more reasonable for the TextVQA task.
Fast Path Planning of Autonomous Vehicles in 3D Environments

Three dimensional path planner is crucial for the safe navigation of autonomous vehicles (AV), such as unmanned aerial vehicles or unmanned underwater vehicles, which operate in three dimensions. In this paper, we develop a novel 3D path planner, which is fast in generating a near-optimal solution path. The planner generates the 3D path considering the size of an AV so that as the AV traverses the constructed path, it does not collide with an obstacle. This paper introduces a 3D path planner with novel concepts, such as a virtual agent and virtual sensors. In order to generate a 3D path to the goal as fast as possible, we let the virtual agent deploy virtual sensors iteratively, such that the connected sensor network can be formed. The constructed sensor network serves as a topological map for the AV, and we find a shortest path from the start to the goal utilizing the network. The virtual agent’s maneuver is biased towards the goal, in order to find a path to the goal as fast as possible. Moreover, the size of the agent is set considering the safety margin of the generated path. Through MATLAB simulations, we demonstrate the outperformance (low computational load and short path length) of our 3D path planner by comparing it with the 3D RRT-star algorithm.