Reviewerly/exHarmony huggingface dataset:
An Improved Nonlinear Tuna Swarm Optimization Algorithm Based on Circle Chaos Map and Levy Flight Operator

The tuna swarm optimization algorithm (TSO) is a new heuristic algorithm proposed by observing the foraging behavior of tuna populations. The advantages of TSO are a simple structure and fewer parameters. Although TSO converges faster than some classical meta-heuristics algorithms, it can still be further accelerated. When TSO solves complex and challenging problems, it often easily falls into local optima. To overcome the above issue, this article proposed an improved nonlinear tuna swarm optimization algorithm based on Circle chaos map and levy flight operator (CLTSO). In order to compare it with some advanced heuristic algorithms, the performance of CLTSO is tested with unimodal functions, multimodal functions, and some CEC2014 benchmark functions. The test results of these benchmark functions are statistically analyzed using Wilcoxon, Friedman test, and MAE analysis. The experimental results and statistical analysis results indicate that CLTSO is more competitive than other advanced algorithms. Finally, this paper uses CLTSO to optimize a BP neural network in the field of artificial intelligence. A CLTSO-BP neural network model is proposed. Three popular datasets from the UCI Machine Learning and Intelligent System Center are selected to test the classification performance of the new model. The comparison result indicates that the new model has higher classification accuracy than the original BP model.
The Rubber Hand Illusion in Virtual Reality and the Real World - Comparable but Different

Feeling ownership of a virtual body is crucial for immersive experiences in VR. Knowledge about body ownership is mainly based on rubber hand illusion (RHI) experiments in the real world. Watching a rubber hand being stroked while one's own hidden hand is synchronously stroked, humans experience the rubber hand as their own hand and underestimate the distance between the rubber hand and the real hand (proprioceptive drift). There is also evidence for a decrease in hand temperature. Although the RHI has been induced in VR, it is unknown whether effects in VR and the real world differ. We conducted a RHI experiment with 24 participants in the real world and in VR and found comparable effects in both environments. However, irrespective of the RHI, proprioceptive drift and temperature differences varied between settings. Our findings validate the utilization of the RHI in VR to increase our understanding of embodying virtual avatars.
Detection of River Floating Garbage Based on Improved YOLOv5

The random dumping of garbage in rivers has led to the continuous deterioration of water quality and affected people’s living environment. The accuracy of detection of garbage floating in rivers is greatly affected by factors such as floating speed, night/daytime natural light, viewing angle and position, etc. This paper proposes a novel detection model, called YOLOv5_CBS, for the detection of garbage objects floating in rivers, based on improvements of the YOLOv5 model. Firstly, a coordinate attention (CA) mechanism is added to the original C3 module (without compressing the number of channels in the bottleneck), forming a new C3-CA-Uncompress Bottleneck (CCUB) module for improving the size of the receptive field and allowing the model to pay more attention to important parts of the processed images. Then, the Path Aggregation Network (PAN) in YOLOv5 is replaced with a Bidirectional Feature Pyramid Network (BiFPN), as proposed by other researchers, to enhance the depth of information mining and improve the feature extraction capability and detection performance of the model. In addition, the Complete Intersection over Union (CIoU) loss function, which was originally used in YOLOv5 for the calculation of location score of the compound loss, is replaced with the SCYLLA-IoU (SIoU) loss function, so as to speed up the model convergence and improve its regression precision. The results, obtained through experiments conducted on two datasets, demonstrate that the proposed YOLOv5_CBS model outperforms the original YOLOv5 model, along with three other state-of-the-art models (Faster R-CNN, YOLOv3, and YOLOv4), when used for river floating garbage objects detection, in terms of the recall, average precision, and F1 score achieved by reaching respective values of 0.885, 90.85%, and 0.8669 on the private dataset, and 0.865, 92.18%, and 0.9006 on the Flow-Img public dataset.
Semi-supervised Video Paragraph Grounding with Contrastive Encoder

Video events grounding aims at retrieving the most relevant moments from an untrimmed video in terms of a given natural language query. Most previous works focus on Video Sentence Grounding (VSG), which localizes the moment with a sentence query. Recently, researchers extended this task to Video Paragraph Grounding (VPG) by retrieving multiple events with a paragraph. However, we find the existing VPG methods may not perform well on context modeling and highly rely on video-paragraph annotations. To tackle this problem, we propose a novel VPG method termed Semi-supervised Video-Paragraph TRansformer (SVPTR), which can more effectively exploit contextual information in paragraphs and significantly reduce the dependency on annotated data. Our SVPTR method consists of two key components: (1) a base model VPTR that learns the video-paragraph alignment with contrastive encoders and tackles the lack of sentence-level contextual interactions and (2) a semi-supervised learning framework with multimodal feature perturbations that reduces the requirements of annotated training data. We evaluate our model on three widely-used video grounding datasets, i.e., ActivityNet-Caption, Charades-CD-OOD, and TACoS. The experimental results show that our SVPTR method establishes the new state-of-the-art performance on all datasets. Even under the conditions of fewer annotations, it can also achieve competitive results compared with recent VPG methods.
Image caption generation using Visual Attention Prediction and Contextual Spatial Relation Extraction

Abstract Automatic caption generation with attention mechanisms aims at generating more descriptive captions containing coarser to finer semantic contents in the image. In this work, we use an encoder-decoder framework employing Wavelet transform based Convolutional Neural Network (WCNN) with two level discrete wavelet decomposition for extracting the visual feature maps highlighting the spatial, spectral and semantic details from the image. The Visual Attention Prediction Network (VAPN) computes both channel and spatial attention for obtaining visually attentive features. In addition to these, local features are also taken into account by considering the contextual spatial relationship between the different objects. The probability of the appropriate word prediction is achieved by combining the aforementioned architecture with Long Short Term Memory (LSTM) decoder network. Experiments are conducted on three benchmark datasets—Flickr8K, Flickr30K and MSCOCO datasets and the evaluation results prove the improved performance of the proposed model with CIDEr score of 124.2.
Less than 5mm — The unseen threat: A practice-led investigation into micro-plastics effects on coral reefs

This article presents an artistic practice-led research project that asks: How can communication design be used to raise awareness about the effect of microplastics on coral reefs, encouraging a shift in the mentality of single-use plastic? The study highlights the scale of the issue of micro-plastics, where environmental pollution stemmed from the throw-away society, intending to ignite action – from micro-plastics to micro-changes. An opportunity arose to visually communicate how such a desirable product, the micro plastic, is destroying ecosystems. The project employs a reflective inquiry methodology supported by a heuristic approach, surfacing the tacit and the experiential to heighten self-awareness within the researchers' practice. This approach enables the researcher to draw connections between the subconscious and unconscious mentality, challenging the stigma and pre-existing assumptions. The project explores risograph printing, a digital screenprinting method, complimented with the use of laser cutting to encourage the reader to engage with the artefact and shift the mindset from unconscious buying patterns to become a conscious consumer. In exploring these methods, the project draws on visual inspiration through contextual knowledge. Gathering information and understanding the scale of the issue contributed to the originality and the essence of artefact. The design artefact takes the form of a risograph publication design, embracing the tactility and sustainable practices of the risograph printer in responding the research question. The artefact encompasses the essence of microplastics, uncovering the truth that lies within its aesthetic appearance. Significantly, the project is a conscious, self-reflective inquiry that contributes to the field of knowledge surrounding designing for good, and to giving the ocean personhood. It contributes to discourse about practice-led research in graphic design to engage the reader in behaviour change.
Long-Distance Person Detection Based on YOLOv7

In the research field of small object detection, most object detectors have been successfully used for pedestrian detection, face recognition, lost and found, and automatic driving, among other applications, and have achieved good results. However, when general object detectors encounter challenging low-resolution images from the TinyPerson dataset, they will produce undesirable detection results because of the dense occlusion between people and different body poses. In order to solve these problems, this paper proposes a tiny object detection method TOD-YOLOv7 based on YOLOv7.First, this paper presents a reconstruction of the YOLOv7 network by adding a tiny object detection layer to enhance its detection ability. Then, we use the recursive gated convolution module to realize the interaction with the higher-order space to accelerate the model initialization process and reduce the reasoning time. Secondly, this paper proposes the integration of a coordinate attention mechanism into the YOLOv7 feature extraction network to strengthen the pedestrian object information and weaken the background information.Additionally, we leverage data augmentation techniques to improve the representation learning of the algorithm. The results show that compared with the baseline model YOLOv7, the detection accuracy of this model on the TinyPerson dataset is improved from 7.1% to 9.5%, and the detection speed reaches 208 frames per second (FPS). The algorithm of this paper is shown to achieve better detection results for tiny object detection.
Data processing related to the impact of performance expectation, effort expectation, and perceived usefulness on the use of electronic banking services for customers of Jordanian banks

The aim of this study was to investigate and identify the influence of performance expectation, effort expectation, and perceived usefulness on the usage of electronic banking services by Jordanian bank customers. The study used a quantitative method, with questionnaires administered to Jordanian bank clients. The statistics support the association between performance expectation, effort expectation, perceived utility, and the usage of electronic banking services. This implies that these characteristics have a major influence on Jordanian bank customers' electronic banking services. The association between performance expectation and use of electronic banking services is the strongest, followed by effort expectation and use of electronic banking services, and the poorest between perceived usefulness and use of electronic banking services. Even the most tenuous association (perceived usefulness and use of electronic banking services) is statistically significant. These findings also imply that banks seeking to boost the usage of electronic banking services should concentrate on improving consumers' perceptions of performance and effort expectations, as well as perceived usefulness.
Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images

In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in the automated assessment of CRC histopathology whole-slide images. We present an AI-based method to segment multiple ([Formula: see text]) tissue compartments in the H &E-stained whole-slide image, which provides a different, more perceptible picture of tissue morphology and composition. We test and compare a panel of state-of-the-art loss functions available for segmentation models, and provide indications about their use in histopathology image segmentation, based on the analysis of (a) a multi-centric cohort of CRC cases from five medical centers in the Netherlands and Germany, and (b) two publicly available datasets on segmentation in CRC. We used the best performing AI model as the basis for a computer-aided diagnosis system that classifies colon biopsies into four main categories that are relevant pathologically. We report the performance of this system on an independent cohort of more than 1000 patients. The results show that with a good segmentation network as a base, a tool can be developed which can support pathologists in the risk stratification of colorectal cancer patients, among other possible uses. We have made the segmentation model available for research use on https://grand-challenge.org/algorithms/colon-tissue-segmentation/ .
Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI)

Abstract Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. However, current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems, resulting in slow convergence or pigeonholing into a local minimum. In this paper, we present a Zooming Memory-Based Initialization algorithm, entitled ZoMBI, that builds on conventional Bayesian optimization principles to quickly and efficiently optimize Needle-in-a-Haystack problems in both less time and fewer experiments. The ZoMBI algorithm demonstrates compute time speed-ups of 400× compared to traditional Bayesian optimization as well as efficiently discovering optima in under 100 experiments that are up to 3× more highly optimized than those discovered by similar methods.
The Impact of Supply Chain Management Practices on Competitive Advantage: The Moderating Role of Big Data Analytics

Purpose: The current study aims to investigate the moderating role of Big Data Analytics (BDA) on the relationship between supply chain management practices (SCMPs) and the competitive advantage (CA) in the Jordanian manufacturing firms. Design/methodology/approach: Quantitative method was used to collect data from 156 Jordanian manufacturing companies. And hierarchical linear multiple regression using SPSS technique was used to test the study hypotheses. Findings: The results show significant positive impact of SCMPs on CA. Specifically, a significant positive impact is found between information quality (IQ), and information sharing (ISh) on CA. However, strategic supplier partnership (SSP), and customer relationship management (CRM) had no impact on CA. However, the study found that BDA does not enhance the impact of SCMPs on CA. Research, Practical &amp; Social implications: This study provides an inventory of knowledge about the reality of BDA and its moderating role on the relationship between SCMPs and CA, which contributes to enriching the library in overall and Jordanian in specific in this subject. Originality/value: This paper is one of the first papers in the Jordanian context to address the moderating effect of BDA between SCMPs and CA.
Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm

Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. The preliminary aim is to reduce the overall power consumption which was ignored in the early days of cloud development. This was due to the performance expectations from cloud servers as they were supposed to provide all the services through their services layers IaaS, PaaS, and SaaS. As time passed and researchers came up with new terminologies and algorithmic architecture for the reduction of power consumption and sustainability, other algorithmic anarchies were also introduced, such as statistical oriented learning and bioinspired algorithms. In this paper, an indepth focus has been done on multiple approaches for migration among virtual machines and find out various issues among existing approaches. The proposed work utilizes elastic scheduling inspired by the smart elastic scheduling algorithm (SESA) to develop a more energy-efficient VM allocation and migration algorithm. The proposed work uses cosine similarity and bandwidth utilization as additional utilities to improve the current performance in terms of QoS. The proposed work is evaluated for overall power consumption and service level agreement violation (SLA-V) and is compared with related state of art techniques. A proposed algorithm is also presented in order to solve problems found during the survey.
Minimal reporting guideline for research involving eye tracking (2023 edition)

A guideline is proposed that comprises the minimum items to be reported in research studies involving an eye tracker and human or non-human primate participant(s). This guideline was developed over a 3-year period using a consensus-based process via an open invitation to the international eye tracking community. This guideline will be reviewed at maximum intervals of 4 years.
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models

Vision-language pre-training (VLP) has shown impressive performance on a wide range of cross-modal tasks, where VLP models without reliance on object detectors are becoming the mainstream due to their superior computation efficiency and competitive performance.However, the removal of object detectors also deprives the capability of VLP models in explicit object modeling, which is essential to various position-sensitive vision-language (VL) tasks, such as referring expression comprehension and visual commonsense reasoning.To address the challenge, we introduce PEVL that enhances the pre-training and prompt tuning of VLP models with explicit object position modeling.Specifically, PEVL reformulates discretized object positions and language in a unified language modeling framework, which facilitates explicit VL alignment during pretraining, and also enables flexible prompt tuning for various downstream tasks.We show that PEVL enables state-of-the-art performance of detector-free VLP models on position-sensitive tasks such as referring expression comprehension and phrase grounding, and also improves the performance on position-insensitive tasks with grounded inputs.We make the data and code for this paper publicly available at https://github.com/thunlp/PEVL.
Large Dual Encoders Are Generalizable Retrievers

It has been shown that dual encoders trained on one domain often fail to generalize to other domains for retrieval tasks.One widespread belief is that the bottleneck layer of a dual encoder, where the final score is simply a dotproduct between a query vector and a passage vector, is too limited compared to models with fine-grained interactions between the query and the passage.In this paper, we challenge this belief by scaling up the size of the dual encoder model while keeping the bottleneck layer as a single dot-product with a fixed size.With multi-stage training, scaling up the model size brings significant improvement on a variety of retrieval tasks, especially for outof-domain generalization.We further analyze the impact of the bottleneck layer and demonstrate diminishing improvement when scaling up the embedding size.Experimental results show that our dual encoders, Generalizable T5-based dense Retrievers (GTR), outperform previous sparse and dense retrievers on the BEIR dataset (Thakur et al., 2021) significantly.Most surprisingly, our ablation study finds that GTR is very data efficient, as it only needs 10% of MS Marco supervised data to match the out-of-domain performance of using all supervised data.
Recommending Related Microblogs: A Comparison Between Topic and WordNet based Approaches

Computing similarity between short microblogs is an important step in microblog recommendation. In this paper, we investigate a topic based approach and a WordNet based approach to estimate similarity scores between microblogs and recommend top related ones to users. Empirical study is conducted to compare their recommendation effectiveness using two evaluation measures. The results show that the WordNet based approach has relatively higher precision than that of the topic based approach using 548 tweets as dataset. In addition, the Kendall tau distance between two lists recommended by WordNet and topic approaches is calculated. Its average of all the 548 pair lists tells us the two approaches have the relative high disaccord in the ranking of related tweets.
Motion-Appearance Interactive Encoding for Object Segmentation in Unconstrained Videos

We present a novel method of integrating motion and appearance cues for foreground object segmentation in unconstrained videos.Unlike conventional methods encoding motion and appearance patterns individually, our method puts particular emphasis on their mutual assistance.Specifically, we propose using an interactively constrained encoding (ICE) scheme to incorporate motion and appearance patterns into a graph that leads to a spatiotemporal energy optimization.The reason of utilizing ICE is that both motion and appearance cues for the same target share underlying correlative structure, thus can be exploited in a deeply collaborative manner.We perform ICE not only in the initialization but also in the refinement stage of a two-layer framework for object segmentation.This scheme allows our method to consistently capture structural patterns about object perceptions throughout the whole framework.Our method can be operated on superpixels instead of raw pixels to reduce the number of graph nodes by two orders of magnitude.Moreover, we propose to partially explore the multiobject localization problem with inter-occlusion by weighted bipartite graph matching.Comprehensive experiments on three benchmark datasets (i.e., SegTrack, MOViCS, and GaTech) demonstrate the effectiveness of our approach compared with extensive state-of-the-art methods.
Unambiguous Sparse Recovery of Migrating Targets With a Robustified Bayesian Model

The problem considered is that of estimating unambiguously migrating targets observed with a wideband radar.We extend a previously described sparse Bayesian algorithm to the presence of diffuse clutter and off-grid targets.A hybrid-Gibbs sampler is formulated to jointly estimate the sparse target amplitude vector, the grid mismatch and the (assumed) autoregressive noise.Results on synthetic and fully experimental data show that targets can be actually unambiguously estimated even if located in blind speeds.