Reviewerly/exHarmony huggingface dataset:
Multi-Task Learning and Gender-Aware Fashion Recommendation System Using Deep Learning

Many people wonder, when they look at fashion models on social media or on television, whether they could look like them by wearing similar products. Furthermore, many people suffer when they sometimes find fashion models in e-commerce, and they want to obtain similar products, but after clicking on the fashion model, they receive unwanted products or products for the opposite gender. To address these issues, in our work, we built a multi-task learning and gender-aware fashion recommendation system (MLGFRS). The proposed MLGFRS can increase the revenue of the e-commerce fashion market. Moreover, we realized that people are accustomed to clicking on that part of the fashion model, which includes the product they want to obtain. Therefore, we classified the query image into many cropped products to detect the user’s click. What makes this paper novel is that we contributed to improving the efficiency performance by detecting the gender from the query image to reduce the retrieving time. Second, we effectively improved the quality of results by retrieving similarities for each object in the query image to recommend the most relevant products. The MLGFRS consists of four components: gender detection, object detection, similarity generation, and recommendation results. The MLGFRS achieves better performance compared to the state-of-the-art baselines.
FEDRak: Federated Learning-Based Symmetric Code Statement Ranking Model for Software Fault Forecasting

Software Fault Forecasting (SFF) pertains to timely identifying sections in software projects that are prone to faults and may result in significant development expenses. Deep learning models have become widespread in software fault monitoring and management, and these models rely on the design metrics and the code pattern features for classifying the code as erroneous or safe. The proposed model works based on the collective formulation of the fault localization model, which acquires the model-specific metadata for building a global model that would perform software fault forecasting globally. The proposed model works by ranking the suspicious code blocks based on the symmetry of the semantic features of the erroneous code and the implementation code. The feature selection and scaling process is initially performed to precisely identify the features contributing to fault forecasting. The data extraction that is portrayed as the intermediate phase would assist in focusing on the code statements and ranking them based on the impact of the fault. A fine-tuned spectrum-based fault localization technique is used in ranking the statements. The FEDRak model facilitates ongoing adaptation in instances where there are adjustments in the feature contribution of data over time. The federated learning model would update the feature weights of the global model based on the weights synchronized by locally built fault forecasting approaches. FEDRak is statistically analyzed in relation to other contemporary techniques in fault localization in terms of metrics like sensitivity, specificity, accuracy, F1-score, and ROC curves. The proposed model’s performances are analyzed in terms of local and global models.
Part-of-Speech Tags Guide Low-Resource Machine Translation

Neural machine translation models are guided by loss function to select source sentence features and generate results close to human annotation. When the data resources are abundant, neural machine translation models can focus on the features used to produce high-quality translations. These features include POS or other grammatical features. However, models cannot focus precisely on these features when data resources are limited. The reason is that the lack of samples makes the model overfit before considering these features. Previous works have enriched the features by integrating source POS or multitask methods. However, these methods only utilize the source POS or produce translations by introducing the generated target POS. We propose introducing POS information based on multitask methods and reconstructors. We obtain the POS tags by the additional encoder and decoder and compute the corresponding loss function. These loss functions are used with the loss function of machine translation to optimize the parameters of the entire model, which makes the model pay attention to POS features. The POS features focused on by models will guide the translation process and alleviate the problem that models cannot focus on the POS features in the case of low resources. Experiments on multiple translation tasks show that the method improves 0.4∼1 BLEU compared with the baseline model on different translation tasks.
Software multi-fault localization via Chameleon clustering in parallel

Fault localization have proven to be useful in various software during the process of software testing and debugging. The presence of multiple faults simultaneously may deteriorate the effectiveness of existing fault-localization techniques. One good solution is to generate fault-focused clusters by the same bug into the same cluster in term of clustering analysis the failed execution traces. We first introduce improved Chameleon cluster analysis to locate multi-fault in parallel under a general framework. In order to make use of execution information, feature select strategy of the program execution trace is proposed. the failed program execution traces are divided into fault-focused clusters by Chameleon cluster analysis according to the likely faults that caused them, and each cluster may contain the same fault. Then, their suspiciousness is calculated after the clusters of failed program execution traces are merged with all successful program execution traces. Finally, multiple faults in the program are located simultaneously in parallel debugging mode by the suspicious ranking generated in each merged cluster. Experiments conducted on four real-world subjects shows that our approach can achieve better performance than the compared ones.
High-Performance Lightweight Fall Detection with an Improved YOLOv5s Algorithm

The aging population has drastically increased in the past two decades, stimulating the development of devices for healthcare and medical purposes. As one of the leading potential risks, the injuries caused by accidental falls at home are hazardous to the health (and even lifespan) of elderly people. In this paper, an improved YOLOv5s algorithm is proposed, aiming to improve the efficiency and accuracy of lightweight fall detection via the following modifications that elevate its accuracy and speed: first, a k-means++ clustering algorithm was applied to increase the accuracy of the anchor boxes; the backbone network was replaced with a lightweight ShuffleNetV2 network to embed simplified devices with limited computing ability; an SE attention mechanism module was added to the last layer of the backbone to improve the feature extraction capability; the GIOU loss function was replaced by a SIOU loss function to increase the accuracy of detection and the training speed. The results of testing show that the mAP of the improved algorithm was improved by 3.5%, the model size was reduced by 75%, and the time consumed for computation was reduced by 79.4% compared with the conventional YOLOv5s. The algorithm proposed in this paper has higher detection accuracy and detection speed. It is suitable for deployment in embedded devices with limited performance and with lower cost.
Provably superior accuracy in quantum stochastic modeling

In the design of stochastic models, there is a constant trade-off between model complexity and accuracy. Here we prove that quantum models enable a more favorable trade-off. We present a technique for identifying fundamental upper bounds on the predictive accuracy of dimensionality-constrained classical models. We identify quantum models that surpass this bound by creating an algorithm that learns quantum models given time-series data. We demonstrate that this quantum accuracy advantage is attainable in a present-day noisy quantum device. These results illustrate the immediate relevance of quantum technologies to time-series analysis and offer an instance where their resulting accuracy advantage can be provably established.
The Effect of Teaching English Vocabulary on Junior High School Students By Using Wordwall.net

The use of web in the teaching and learning process is one of the fun and challenging learning innovations such as wordwall.net. With this web students can memorize and remember vocabulary with interesting games. Wordwall.net offers a variety of interesting and original vocabulary-building games. The researchers discovered a problem through interview with a teacher: student scores are still low in one area of the assessment, so the purpose of this research was to investigate the impact of using wordwall.net on students’ English vocabulary in SMP Negeri 1 Wonoayu. This research used a true-experimental design with 35 students in the experimental group and 35 students in the control group. A vocabulary test was one of the research tools utilized in this study to gather data. The treatment was carried out for 3 weeks. The data analysis results revealed that there was difference between mean score of pre-test and post-test in both groups. The pre-test mean score was 65.00 (experimental group) and 57.73 (control group) and the post-test average score was 89.13 (experimental group) and 77.90 (control group). With SPSS version 26, an independent sample t-test was used to analyze the data. The result showed that the probability value of both groups is 0.001, so it indicates that wordwall influences students’ vocabulary improvement. Therefore, it might be suggested that applying Wordwall.net was beneficial in increasing students' vocabulary knowledge.
Enhancing Cross-Lingual Entity Alignment in Knowledge Graphs through Structure Similarity Rearrangement

Cross-lingual entity alignment in knowledge graphs is a crucial task in knowledge fusion. This task involves learning low-dimensional embeddings for nodes in different knowledge graphs and identifying equivalent entities across them by measuring the distances between their representation vectors. Existing alignment models use neural network modules and the nearest neighbors algorithm to find suitable entity pairs. However, these models often ignore the importance of local structural features of entities during the alignment stage, which may lead to reduced matching accuracy. Specifically, nodes that are poorly represented may not benefit from their surrounding context. In this article, we propose a novel alignment model called SSR, which leverages the node embedding algorithm in graphs to select candidate entities and then rearranges them by local structural similarity in the source and target knowledge graphs. Our approach improves the performance of existing approaches and is compatible with them. We demonstrate the effectiveness of our approach on the DBP15k dataset, showing that it outperforms existing methods while requiring less time.
YOLO-DCTI: Small Object Detection in Remote Sensing Base on Contextual Transformer Enhancement

Object detection for remote sensing is a fundamental task in image processing of remote sensing; as one of the core components, small or tiny object detection plays an important role. Despite the considerable advancements achieved in small object detection with the integration of CNN and transformer networks, there remains untapped potential for enhancing the extraction and utilization of information associated with small objects. Particularly within transformer structures, this potential arises from the disregard of the complex and the intertwined interplay between spatial context information and channel information during the global modeling of pixel-level information within small objects. As a result, valuable information is prone to being obfuscated and annihilated. To mitigate this limitation, we propose an innovative framework, YOLO-DCTI, that capitalizes on the Contextual Transformer (CoT) framework for the detection of small or tiny objects. Specifically, within CoT, we seamlessly incorporate global residuals and local fusion mechanisms throughout the entire input-to-output pipeline. This integration facilitates a profound investigation into the network’s intrinsic representations at deeper levels and fosters the fusion of spatial contextual attributes with channel characteristics. Moreover, we propose an improved decoupled contextual transformer detection head structure, denoted as DCTI, to effectively resolve the feature conflicts that ensue from the concurrent classification and regression tasks. The experimental results on the Dota, VISDrone, and NWPU VHR-10 datasets show that, on the powerful real-time detection network YOLOv7, the speed and accuracy of tiny targets are better balanced.
A novel method for obtaining gamma precoding values

Abstract The grating projection‐based three‐dimensional (3D) measurement technique has been widely used in various fields, such as industrial inspection, cultural relic protection, and reverse engineering. However, due to the nonlinear response between the input and output of the measurement system, phase measurement errors occur, which reduces the accuracy of 3D reconstruction, which is known as the gamma effect. To address this issue, a novel method has been proposed to reduce the nonlinear response of the system. Ultimately, this method reduces the impact of the gamma value on the phase accuracy in subsequent measurements. Experimental results have shown that this method can quickly calibrate the correct precoding gamma value, effectively reduce the gamma nonlinear response during the measurement process, and improve measurement accuracy.
Chatbots Benefications Towards the Education Sector

One of the many promising developments in data innovation is chatbots. They were created as a brand-new user interface that would let users communicate with services merely through chat, replacing or enhancing the need for applications or website visits. Since digitalisation is pacing up around the globe, usage of chatbots is prevalent in many domains for the interface between customers and entities. However, there is still another industry where chatbots have a considerable amount of prospect: education. In the educational sector, chatbots are equipped but to a certain extent only. The aim of the study is to create awareness about potential of chatbots in learning processes and its various applications in the academic sector. Chatbots various verticals promote digitalisation in the future of education support systems enhancing the productivity and boosting growth as the education sector of any country is the foundation of its growing future.
Exclusion principle for nonlocal advantage of quantum coherence

Coherences in mutually unbiased bases of states of an isolated quantum system follow a complementarity relation. The nonlocal advantage of quantum coherence (NAQC), defined in a bipartite scenario, is a situation in which the average quantum coherences of the ensembles of one subsystem, effected by a measurement performed on the other subsystem, violate the complementarity relation. We analyze two criteria to detect NAQC for bipartite quantum states. We construct a more generalized version of the criterion to detect NAQC that is better than the standard criterion as it can capture more states exhibiting NAQC. We prove the local unitary invariance of these NAQC criteria. Further on, we focus on investigating the monogamy properties of NAQC in the tripartite scenario. We check for monogamy of NAQC from two perspectives, differentiated by whether or not the nodal observer in the monogamy relation performs the measurement for the nonlocal advantage. We find in particular that in the case where the nodal observer does not perform the measurement, a strong monogamy relation---an exclusion principle---is exhibited by NAQC.
Influence and Philosophical Reflection on ChatGPT in the Media Industry

This paper explores ChatGPT’s influence and the philosophical reflections it has inspired in the media industry. As a state-of-the-art large language model with powerful text generation capabilities, ChatGPT can help people better understand and process large amounts of information, thus affecting the information ecology. This article points out that ChatGPT has a wide range of application values in areas such as language translation, which can promote international exchanges and cultural integration, and that it can have a positive impact on China’s strengthening of its international communication capacity. However, the application of ChatGPT also brings some problems, such as the spamming of false information and the spread of English-language data infiltration. In order to give full play to the value of ChatGPT and make full use of the unique advantages of human beings when using AI, we need to pay attention to the construction of counterfeit detection technology, strengthen media literacy training, and pay attention to the collaborative creations of human beings and machines that promote the realization of cultural value.
Cloud Shadow Detection via Ray Casting with Probability Analysis Refinement Using Sentinel-2 Satellite Data

Analysis of aerial images provided by satellites enables continuous monitoring and is a central component of many applications, including precision farming. Nonetheless, this analysis is often impeded by the presence of clouds and cloud shadows, which obscure the underlying region of interest and introduce incorrect values that bias analysis. In this paper, we outline a method for cloud shadow detection, and demonstrate our method using Canadian farmland data obtained from the Sentinel-2 satellite. Our approach builds on other object-based cloud and cloud shadow detection methods that generate preliminary shadow candidate masks which are refined by matching clouds to their respective shadows. We improve on these components by using ray-casting and inverse texture mapping methods to quickly identify cloud shadows, allowing for the immediate removal of false positives during image processing. Leveraging our ray-casting-based approach, we further improve our results by implementing a probability analysis based on the cloud probability layer provided by the Sentinel-2 satellite to account for missed shadow pixels. An evaluation of our method using the average producer (82.82%) and user accuracy (75.55%) both show a marked improvement over the performance of other object-based methods. Methodologically, our work demonstrates how incorporating probability analysis as a post-processing step can improve the generation of shadow masks.
Adaptive filtering and smoothing algorithm based on variable structure interactive multiple model

Abstract For maneuvering target tracking, a novel adaptive variable structure interactive multiple model filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. Firstly, an accurate model of the variable structure interactive multiple model algorithm is established. Secondly, by constructing a new model subset based on the original model subsets, the matching accuracy between the model subset and the actual maneuvering mode of the target is improved. Then, the AVSIMMFS algorithm is obtained by smoothing the filtered data of the new model subset. Because of the combination of forward filtering and backward smoothing, the target tracking accuracy is further improved. Finally, in order to verify the effectiveness of the algorithm, the simulation is carried out on two cases. The simulation results show that the tracking performance of AVSIMMFS algorithm is better than other methods and has lower calculation cost.
Dequantizing the Quantum Singular Value Transformation: Hardness and Applications to Quantum Chemistry and the Quantum PCP Conjecture

The Quantum Singular Value Transformation (QSVT) is a recent technique that gives a unified framework to describe most quantum algorithms discovered so far, and may lead to the development of novel quantum algorithms.In this paper we investigate the hardness of classically simulating the QSVT.A recent result by Chia, Gilyén, Li, Lin, Tang and Wang (STOC 2020) showed that the QSVT can be efficiently "dequantized" for low-rank matrices, and discussed its implication to quantum machine learning.In this work, motivated by establishing the superiority of quantum algorithms for quantum chemistry and making progress on the quantum PCP conjecture, we focus on the other main class of matrices considered in applications of the QSVT, sparse matrices.We first show how to efficiently "dequantize", with arbitrarily small constant precision, the QSVT associated with a low-degree polynomial.We apply this technique to design classical algorithms that estimate, with constant precision, the singular values of a sparse matrix.We show in particular that a central computational problem considered by quantum algorithms for quantum chemistry (estimating the ground state energy of a local Hamiltonian when given, as an additional input, a state sufficiently close to the ground state) can be solved efficiently with constant precision on a classical computer.As a complementary result, we prove that with inverse-polynomial precision, the same problem becomes BQP-complete.This gives theoretical evidence for the superiority of quantum algorithms for chemistry, and strongly suggests that said superiority stems from the improved precision achievable in the quantum setting.We also discuss how this dequantization technique may help make progress on the central quantum PCP conjecture.
Motion-aware and data-independent model based multi-view 3D pose refinement for volleyball spike analysis

Abstract In the volleyball game, estimating the 3D pose of the spiker is very valuable for training and analysis, because the spiker’s technique level determines the scoring or not of a round. The development of computer vision provides the possibility for the acquisition of the 3D pose. Most conventional pose estimation works are data-dependent methods, which mainly focus on reaching a high level on the dataset with the controllable scene, but fail to get good results in the wild real volleyball competition scene because of the lack of large labelled data, abnormal pose, occlusion and overlap. To refine the inaccurate estimated pose, this paper proposes a motion-aware and data-independent method based on a calibrated multi-camera system for a real volleyball competition scene. The proposed methods consist of three key components: 1) By utilizing the relationship of multi-views, an irrelevant projection based potential joint restore approach is proposed, which refines the wrong pose of one view with the other three views projected information to reduce the influence of occlusion and overlap. 2) Instead of training with a large amount labelled data, the proposed motion-aware method utilizes the similarity of specific motion in sports to achieve construct a spike model. Based on the spike model, joint and trajectory matching is proposed for coarse refinement. 3) To finely refine, a point distribution based posterior decision network is proposed. While expanding the receptive field, the pose estimation task is decomposed into a classification decision problem, which greatly avoids the dependence on a large amount of labelled data. The experimental dataset videos with four synchronous camera views are from a real game, the Game of 2014 Japan Inter High School of Men Volleyball. The experiment result achieves 76.25%, 81.89%, and 86.13% success rate at the 30mm, 50mm, and 70mm error range, respectively. Since the proposed refinement framework is based on a real volleyball competition, it is expected to be applied in the volleyball analysis.