Reviewerly/exHarmony huggingface dataset:
DESIGN AND EXPERIMENT OF A SELF-PROPELLED JUJUBE COLLECTION STRIP PICK-UP MACHINE

Aiming at the problems of single mechanical performance, multi-person operation and low operation efficiency of air-suction jujube harvesting in China, combined with the requirements of mechanized jujube harvesting in Xinjiang, this study designed a self-propelled jujube collection strip pick-up machine. The machine is mainly composed of crawler chassis, landing jujube collection strip device, separation and impurity removal device and hydraulic system. According to the jujube planting mode, the size of the crawler chassis is 1780 mm × 1650 mm × 310 mm. In order to simulate the effect of artificial collecting strips, a reciprocating collecting strip blowing head was designed with a height of 15 cm from the ground, a horizontal and vertical angle of 35° and 15° respectively. The model of the collecting fan is selected as 9-19-4A; the fluid simulation analysis of the separation and impurity removal box is carried out to verify the rationality of the box structure design. The performance of the picker was tested by pick-up efficiency, cleanliness, breakage, and impurity rates. The results show that when the forward velocity was about 0.40 m·s-1 , the average pick-up efficiency could reach 752.7 km·h-1 , the average pick-up cleanliness rate was 95.66 %, the average breakage rate was 2.68 %, and the average impurity rate was 3.64 %. The self-propelled jujube collection strip pick-up machine designed in this experiment meets the requirements of mechanized harvesting of jujube and can realize single-person remote control operation.
Eye movement evidence for the V1 Saliency Hypothesis and the Central-peripheral Dichotomy theory in an anomalous visual search task

Typically, searching for a target among uniformly tilted non-targets is easier when this target is perpendicular, rather than parallel, to the non-targets. The V1 Saliency Hypothesis (V1SH) – that V1 creates a saliency map to guide attention exogenously – predicts exactly the opposite in a special case: each target or non-target is a pair of equally-sized disks, a homo-pair of two disks of the same color, black or white, or a hetero-pair of two disks of the opposite color; the inter-disk displacement defines its orientation. This prediction – parallel advantage – was supported by the finding that parallel targets require shorter reaction times (RTs) to report targets’ locations. Furthermore, it is stronger for targets further from the center of search images, as predicted by the Central-peripheral Dichotomy (CPD) theory entailing that saliency effects are stronger in peripheral than in central vision. However, the parallel advantage could arise from a shorter time required to recognize – rather than to shift attention to – the parallel target. By gaze tracking, the present study confirms that the parallel advantage is solely due to the RTs for the gaze to reach the target. Furthermore, when the gaze is sufficiently far from the target during search, saccade to a parallel, rather than perpendicular, target is more likely, demonstrating the Central-peripheral Dichotomy more directly. Parallel advantage is stronger among observers encouraged to let their search be guided by spontaneous gaze shifts, which are presumably guided by bottom-up saliency rather than top-down factors.
Clustering Area Covid-19 Indonesia With K-Means (Case study : Kaggle Dataset)

The spread of the coronavirus in Indonesia is quite fast. The spread of Covid 19 is almost evenly distributed in all provinces in Indonesia. Some areas even have a fairly high mortality rate. Therefore, it is necessary to group regions to find out which areas have the highest to lowest Covid cases so that the appropriate response process can be carried out. In addition, data visualization is also needed that provides information on COVID-19 data for each province. In this study, the data were grouped using the K-Means Clustering method. The dataset used is the Indonesian Covid-19 dataset from Kaggle. The criteria for each province's covid cluster are the number of cases and deaths. The Clustering process uses the Python programming language. From the results of this study, it can be seen that there are 3 groups of covid. The first group consists of 30 provinces with several cases below 200,000 and a number of deaths below 6000. The second group contains two provinces that have the highest number of cases, namely above 600,000, but the number of deaths is less than group 3, which is 15000. In group 3 there are 2 provinces where the number of cases is below 500,000 but the death rate is above 30,000.
Tunable coupler for mediating interactions between a two-level system and a waveguide from a decoupled state to the ultrastrong coupling regime

Two-level systems (TLSs) coupled to waveguides are a fundamental paradigm for light-matter interactions and quantum networks. We introduce and experimentally demonstrate a method to tune the interaction between a TLS, implemented as a flux qubit, and a transmission line waveguide from a decoupled state to a coupling strength that is a significant fraction of the TLS transition frequency, near the ultrastrong coupling regime. The coupling, controlled via magnetic flux, is described by a normalized coupling strength $\ensuremath{\alpha}$ that is measured to range between $6.2\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}5}$ and $2.19\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}2}$, with larger attainable maximum values predicted by a circuit model of the device. This system enables future investigations in the dynamics of the spin-boson model, microwave photonics, and relativistic quantum information.
Characterization of Loss Mechanisms in a Fluxonium Qubit

Using a fluxonium qubit with in situ tunability of its Josephson energy, we characterize its energy relaxation at different flux biases as well as different Josephson energy values. The relaxation rate at qubit energy values, ranging more than 1 order of magnitude around the thermal energy ${k}_{B}T$, can be quantitatively explained by a combination of dielectric loss and $1/f$ flux noise with a crossover point. The amplitude of the $1/f$ flux noise is consistent with that extracted from the qubit dephasing measurements at the flux sensitive points. In the dielectric loss dominant regime, the loss is consistent with that arising from the electric dipole interaction with two-level-system (TLS) defects. In particular, by increasing the Josephson energy, thus decreasing the qubit frequency at the flux insensitive spot, we find that the qubit exhibits increasingly weaker coupling to TLS defects, which is desirable for high-fidelity quantum operations. Our work establishes a generic noise model for the design of high coherence fluxonium and future generations of noise resilient superconducting qubits.
Improving Visual-Semantic Embedding with Adaptive Pooling and Optimization Objective

Zijian Zhang, Chang Shu, Ya Xiao, Yuan Shen, Di Zhu, Youxin Chen, Jing Xiao, Jey Han Lau, Qian Zhang, Zheng Lu. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Collaborative analysis for drug discovery by federated learning on non-IID data

With the increasing availability of large-scale QSAR (Quantitative Structure-Activity Relationship) datasets, collaborative analysis has become a promising approach for drug discovery. Traditional centralized analysis which typically concentrates data on a central server for training faces challenges such as data privacy and security. Distributed analysis such as federated learning offers a solution by enabling collaborative model training without sharing raw data. However, it may fail when the training data in the local devices are non-independent and identically distributed (non-IID). In this paper, we propose a novel framework for collaborative drug discovery using federated learning on non-IID datasets. We address the difficulty of training on non-IID data by globally sharing a small subset of data among all institutions. Our framework allows multiple institutions to jointly train a robust predictive model while preserving the privacy of their individual data. We leverage the federated learning paradigm to distribute the model training process across local devices, eliminating the need for data exchange. The experimental results on 15 benchmark datasets demonstrate that the proposed method achieves competitive predictive accuracy to centralized analysis while respecting data privacy. Moreover, our framework offers benefits such as reduced data transmission and enhanced scalability, making it suitable for large-scale collaborative drug discovery efforts.
Blockchain based Transparent and Genuine Charity Application

Abstract: Non-governmental organizations (NGOs) in under-developed countries are receiving funds from donor agencies for various purposes, including relief from natural disasters and other emergencies, promoting education, women empowerment, economic development, and many more. Some donor agencies have lost their trust in NGOs in under-developed countries, as some NGOs have been involved in the misuse of funds. This is evident from irregularities in the records. For instance, in education funds, on some occasions, the same student has appeared in the records of multiple NGOs as a beneficiary, when in fact, a maximum of one NGO could be paying for a particular beneficiary. Therefore, the number of actual beneficiaries would be smaller than the number of claimed beneficiaries. This research proposes a blockchain-based solution to ensure trust between donor agencies from all over the world, and NGOs in under-developed countries. The list of National IDs along with other keys would be available publicly on a blockchain. The distributed software would ensure that the same set of keys are not entered twice in this blockchain, preventing the problem highlighted above. The details of the fund provided to the student would also be available on the blockchain and would be encrypted and digitally signed by the NGOs. In the case that a record inserted into this blockchain is discovered to be fake, this research provides a way to cancel that record. A cancellation record is inserted, only if it is digitally signed by the relevant donor agency. Keywords: blockchain; ensuring trust; NGOs; encryption
GaMiCO: Game-slicing based multi-interface computation offloading in 5G vehicular networks

Smart vehicles require constantly running heavy vehicular computations with their limited computation/energy resources. 5G vehicular networks have potential to resolve the issue, by letting the vehicular tasks offloaded to 5G mobile edge computing (MEC) servers. To better support vehicular computation offloading, this paper proposes a road-side 5G infrastructure consisting of multiple millimeter-wave (mmWave) small-cell base stations (BSs) and a cellular mid-band based macro-cell BS where each BS is equipped with an MEC server. Then, the vehicles with mmWave/mid-band dual interfaces can decide which BS to choose for offloading. We propose a decentralized offloading decision mechanism where each vehicle tries to minimize the time-energy joint cost with three choices: local computing, offloading to a small-cell MEC, offloading to a macro-cell MEC. In particular, we model the problem as an ordinal potential game, derive its potential function to ensure the existence of and finite-time convergence to a Nash equilibrium (NE), analyze its Price-of-Anarchy, and develop an iterative offloading decision update algorithm. In doing so, we also consider slicing the global game into multiple non-overlapping smaller games and running them in parallel, to investigate the best slicing strategy. Our extensive simulations show the game's real-time convergence to an NE, reveal the NE's near-optimal performance, and present the efficacy of the proposed game slicing.
A Diffusion Model for POI Recommendation

Next Point-of-Interest (POI) recommendation is a critical task in location-based services that aim to provide personalized suggestions for the user’s next destination. Previous works on POI recommendation have laid focus on modeling the user’s spatial preference. However, existing works that leverage spatial information are only based on the aggregation of users’ previous visited positions, which discourages the model from recommending POIs in novel areas. This trait of position-based methods will harm the model’s performance in many situations. Additionally, incorporating sequential information into the user’s spatial preference remains a challenge. In this article, we propose Diff-POI : a Diffu sion-based model that samples the user’s spatial preference for the next POI recommendation. Inspired by the wide application of diffusion algorithm in sampling from distributions, Diff-POI encodes the user’s visiting sequence and spatial character with two tailor-designed graph encoding modules, followed by a diffusion-based sampling strategy to explore the user’s spatial visiting trends. We leverage the diffusion process and its reverse form to sample from the posterior distribution and optimized the corresponding score function. We design a joint training and inference framework to optimize and evaluate the proposed Diff-POI. Extensive experiments on four real-world POI recommendation datasets demonstrate the superiority of our Diff-POI over state-of-the-art baseline methods. Further ablation and parameter studies on Diff-POI reveal the functionality and effectiveness of the proposed diffusion-based sampling strategy for addressing the limitations of existing methods.
Inking cell blocks improves scanner detection for diagnosis in pathology

Cell blocks may be hard to be totally automatically detected by the scanner (ADS), generating incomplete whole slide images (WSIs), with areas that are not scanned, leading to possible false negative diagnosis. The aim of this study is to test if inking the cell blocks helps increasing ADS. Test 1: 15 cell blocks were sectioned, one half inked black (1HB) and the other inked green (1HG). Each of the halves was individually processed to generate a WSI stained by the H&E. 1HBs and 1HGs had similar scanning time (median 59 s vs. 65 s, p = .126) and file sizes (median 382 Mb vs. 381 Mb, p = .567). The black ink interfered less in the observation (2.2% vs. 44.4%; p < .001) than in the green one. Test 2: 15 cell blocks were sectioned, one half inked black (2HB) and the other left unstained/null (2HN). Each of the halves was individually processed to generate three WSIs-one HE, one periodic-acid Schiff (PAS), and one immunostained by cytokeratin AE1&AE3 (CKAE1AE3). HE and PAS WSIs from both 2HN and 2HB groups were all totally ADS and had similar scanning times and file sizes. Concerning immunostaining with CKAE1AE3: ADS (46.7% vs. 93.3%; p = .014), median time for scanning (57 s vs. 83 s; p < .001) and file size (178 Mb vs. 338 Mb; p < .001) were reduced significantly in the 2HN group in comparison with the 2HB. Although increasing scanning time and file size, inking the cell blocks helps increasing ADS after immunostaining, improving the safety and efficiency of the digital pathology workflow.
Creatively Adapting Touch-Based Practices to the Web Format During the COVID-19 Pandemic: Systematic Review

Background The COVID-19 pandemic forced numerous touch-based fields, including manual therapy, body psychotherapy, arts therapy, creative arts practices, and the fitness industry, to swiftly transition to web-based service delivery. These disciplines faced substantial challenges in adapting their traditionally in-person practices, which rely heavily on physical touch and close proximity, to a web format. Objective This review intends to provide a systematically sourced overview of the literature concerning innovative approaches for adapting touch-based practices to the web format in response to the COVID-19 pandemic. Methods A systematic search across 7 databases and gray literature sources identified studies presenting innovative web delivery methods, particularly those addressing the challenges arising from the absence of physical proximity and touch. The inclusion criteria were designed to encompass studies exploring the creative adaptation of touch-based practices to web formats in response to the COVID-19 pandemic irrespective of study methodology. The exclusion criteria applied to studies focusing solely on technical aspects of web delivery or nontouch or noninteractive practices. There were no geographical restrictions, but the selection was limited to publications from 2020 onward. As only qualitative studies were found, data synthesis was conducted thematically. Results The review encompassed 17 studies revealing that touch-based fields successfully devised innovative and creative methods for web service delivery. These methods were categorized into five main themes: (1) adapted working methods (cross-field methods), (2) adapted working methods for sensorial experiences and nonphysical connections, (3) creative methods using materials or additional tools, (4) creative use of web-based platform tools or additional technologies, and (5) creative methods requiring previous preparation of practitioners or participants. These creative approaches allowed practitioners to address the challenges of web touch-based practices, fostering connections and offering unique sensory experiences, albeit with some concerns related to technology and preparation. Conclusions These innovative methods demonstrate promise in circumventing the limitations imposed by the lack of physical touch and proximity in web settings during the COVID-19 pandemic. Furthermore, these insights hold potential for application in other fields in the future. This systematic search and thematic synthesis provide valuable guidance for practitioners and educators seeking to navigate the evolving landscape of web service delivery in touch-based disciplines, ensuring continuity of care even in challenging circumstances. Trial Registration PROSPERO CRD42022379731; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=379731
XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning

In the last 10 years, various automated machine learning (AutoML) systems have been proposed to build end-to-end machine learning (ML) pipelines with minimal human interaction. Even though such automatically synthesized ML pipelines are able to achieve competitive performance, recent studies have shown that users do not trust models constructed by AutoML due to missing transparency of AutoML systems and missing explanations for the constructed ML pipelines. In a requirements analysis study with 36 domain experts, data scientists, and AutoML researchers from different professions with vastly different expertise in ML, we collect detailed informational needs for AutoML. We propose XAutoML , an interactive visual analytics tool for explaining arbitrary AutoML optimization procedures and ML pipelines constructed by AutoML. XAutoML combines interactive visualizations with established techniques from explainable artificial intelligence (XAI) to make the complete AutoML procedure transparent and explainable. By integrating XAutoML with JupyterLab , experienced users can extend the visual analytics with ad-hoc visualizations based on information extracted from XAutoML . We validate our approach in a user study with the same diverse user group from the requirements analysis. All participants were able to extract useful information from XAutoML , leading to a significantly increased understanding of ML pipelines produced by AutoML and the AutoML optimization itself.
SegNetr: Rethinking the Local-Global Interactions and Skip Connections in U-Shaped Networks

Recently, U-shaped networks have dominated the field of medical image segmentation due to their simple and easily tuned structure. However, existing U-shaped segmentation networks: 1) mostly focus on designing complex self-attention modules to compensate for the lack of long-term dependence based on convolution operation, which increases the overall number of parameters and computational complexity of the network; 2) simply fuse the features of encoder and decoder, ignoring the connection between their spatial locations. In this paper, we rethink the above problem and build a lightweight medical image segmentation network, called SegNetr. Specifically, we introduce a novel SegNetr block that can perform local-global interactions dynamically at any stage and with only linear complexity. At the same time, we design a general information retention skip connection (IRSC) to preserve the spatial location information of encoder features and achieve accurate fusion with the decoder features. We validate the effectiveness of SegNetr on four mainstream medical image segmentation datasets, with 59% and 76% fewer parameters and GFLOPs than vanilla U-Net, while achieving segmentation performance comparable to state-of-the-art methods. Notably, the components proposed in this paper can be applied to other U-shaped networks to improve their segmentation performance.
On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving

The existence of real-world adversarial examples (RWAEs) (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tasks such as visual perception in autonomous driving. This article presents an extensive evaluation of the robustness of semantic segmentation (SS) models when attacked with different types of adversarial patches, including digital, simulated, and physical ones. A novel loss function is proposed to improve the capabilities of attackers in inducing a misclassification of pixels. Also, a novel attack strategy is presented to improve the expectation over transformation (EOT) method for placing a patch in the scene. Finally, a state-of-the-art method for detecting adversarial patch is first extended to cope with SS models, then improved to obtain real-time performance, and eventually evaluated in real-world scenarios. Experimental results reveal that even though the adversarial effect is visible with both digital and real-world attacks, its impact is often spatially confined to areas of the image around the patch. This opens to further questions about the spatial robustness of real-time SS models.
AUTONOMOUS MOBILE ROBOT PATH PLANNING BASED ON ENHANCED A* ALGORITHM INTEGRATING WITH TIME ELASTIC BAND

Path planning is the core technology to realize mobile robot navigation. The enhanced A* algorithm with a time elastic band method (TEB) is utilized to aim at the collision problem in a complex environment. A grid-based method transfers the complex environment to a simple grid-based map. The mobile robot's position is determined based on information from the given maps. Optimizing the search point strategy and removing redundant path points reduces the path length and computational scale. The approach yields a safe trajectory in the local path using a time elastic band. The collision cost function combined with the preview deviation angle tracking method ensures the minimum collision-free distance to obstacles and the shortest path. Furthermore, the third-degree B-spline curve will improve the performance of the mobile robot path at the steering angles. Simulated results illustrate the correctness of the proposed method.