Reviewerly/exHarmony huggingface dataset:
Leveraging Automation and Data-driven Logistics for Sustainable Farming of High-value Crops in Emerging Economies

Technology innovations present an opportunity for the agricultural sector to leverage in-field data and inform the resource-demanding operations to ultimately promote Sustainable Development Goals (SDGs), particularly upstream of the food supply chains. The need for data-driven innovations in farming is particularly pertinent to resource-scarce regions, such as the Indian Punjab, where an amalgam of obscure policies and lack of real-time visibility of crops typically leads to the excessive use of farming inputs like freshwater. To this end, this research investigates the use of Internet of Things (IoT) implementations to cultivate Kinnow (a high-value citrus fruit) for assessing the impact of data-informed irrigation practices on the appropriation of natural sources, farming operations efficiency, and the well-being of smallholder farmers. First, a literature taxonomy demonstrates that studies on agri-field logistics often do not consider operations’ environmental and energy impact. In addition, the application of IoT and automated guided vehicles (AGVs) for informing farmers about precision irrigation planning has not been sufficiently explored. Second, an empirical-driven numerical investigation explores four alternative irrigation scenarios for cultivating Kinnow, namely: (i) flood irrigation; (ii) manual irrigation; (iii) AGV-informed manual irrigation; and (iv) AGV-assisted irrigation, which was cast as a Capacitated Vehicle Routing Problem. The analysis results compare the overall sustainability impact of the investigated practices on the water-energy nexus. This research is innovative as it focuses on data-driven logistics operations on the environmental, energy and farmers’ well-being impact associated with irrigation practices in agronomy. This study further supports the role of data-driven technology innovations towards the transition to SDG-centric food supply chains by providing guiding principles for community-led in-field logistics planning.
Deep Learning Pipeline for Spotting Macro- and Micro-expressions in Long Video Sequences Based on Action Units and Optical Flow

This paper is an extension of our previously published ACM Multimedia 2022 paper, which was ranked 3rd in the macro-expressions (MaEs) and micro-expressions (MEs) spotting task of the FME challenge 2021. In our earlier work, a deep learning framework based on facial action units (AUs) was proposed to emphasize both local and global features to deal with the MaEs and MEs spotting tasks. In this paper, an advanced Concat-CNN model is proposed to not only utilize facial action units (AU) features, which our previous work proved were more effective in detecting MaEs, but also to fuse the optical flow features to improve the detection performance of MEs. The advanced Concat-CNN proposed in this paper not only considers the intra-features correlation of a single frame but also the inter-features correlation between frames. Further, we devise a new adaptive re-labeling method by labeling the emotional frames with distinctive scores. This method takes into account the dynamic changes in expressions to further improve the overall detection performance. Compared with our earlier work and several existing works, the newly proposed deep learning pipeline is able to achieve a better performance in terms of the overall F1-scores: 0.2623 on CAS(ME)2, 0.2839 on CAS(ME)2-cropped, and 0.3241 on SAMM-LV, respectively.
Image encryption algorithm based on the density and 6D logistic map

One of the most difficult issues in the history of communication technology is the transmission of secure images. On the internet, photos are used and shared by millions of individuals for both private and business reasons. Utilizing encryption methods to change the original image into an unintelligible or scrambled version is one way to achieve safe image transfer over the network. Cryptographic approaches based on chaotic logistic theory provide several new and promising options for developing secure Image encryption methods. The main aim of this paper is to build a secure system for encrypting gray and color images. The proposed system consists of two stages, the first stage is the encryption process, in which the keys are generated depending on the chaotic logistic with the image density to encrypt the gray and color images, and the second stage is the decryption, which is the opposite of the encryption process to obtain the original image. The proposed method has been tested on two standard gray and color images publicly available. The test results indicate to the highest value of peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI), number of pixel change rate (NPCR) are 7.7268, 50.2011 and 100, respectively. While the encryption and decryption speed up to 0.6319 and 0.5305 second respectively.
Algorithms for the adaptive assessment of procedural knowledge and skills

Abstract Procedural knowledge space theory (PKST) was recently proposed by Stefanutti ( British Journal of Mathematical and Statistical Psychology, 72 (2) 185–218, 2019) for the assessment of human problem-solving skills. In PKST, the problem space formally represents how a family of problems can be solved and the knowledge space represents the skills required for solving those problems. The Markov solution process model (MSPM) by Stefanutti et al. ( Journal of Mathematical Psychology, 103 , 102552, 2021) provides a probabilistic framework for modeling the solution process of a task, via PKST. In this article, three adaptive procedures for the assessment of problem-solving skills are proposed that are based on the MSPM. Beside execution correctness, they also consider the sequence of moves observed in the solution of a problem with the aim of increasing efficiency and accuracy of assessments. The three procedures differ from one another in the assumption underlying the solution process, named pre-planning, interim-planning, and mixed-planning. In two simulation studies, the three adaptive procedures have been compared to one another and to the continuous Markov procedure (CMP) by Doignon and Falmagne (1988a). The last one accounts for dichotomous correct/wrong answers only. Results show that all the MSP-based adaptive procedures outperform the CMP in both accuracy and efficiency. These results have been obtained in the framework of the Tower of London test but the procedures can also be applied to all psychological and neuropsychological tests that have a problem space. Thus, the adaptive procedures presented in this paper pave the way to the adaptive assessment in the area of neuropsychological tests.
Towards improving e-commerce customer review analysis for sentiment detection

Abstract According to a report published by Business Wire, the market value of e-commerce reached US$ 13 trillion and is expected to reach US$ 55.6 trillion by 2027. In this rapidly growing market, product and service reviews can influence our purchasing decisions. It is challenging to manually evaluate reviews to make decisions and examine business models. However, users can examine and automate this process with Natural Language Processing (NLP). NLP is a well-known technique for evaluating and extracting information from written or audible texts. NLP research investigates the social architecture of societies. This article analyses the Amazon dataset using various combinations of voice components and deep learning. The suggested module focuses on identifying sentences as ‘Positive‘, ‘Neutral‘, ‘Negative‘, or ‘Indifferent‘. It analyses the data and labels the ‘better’ and ‘worse’ assumptions as positive and negative, respectively. With the expansion of the internet and e-commerce websites over the past decade, consumers now have a vast selection of products within the same domain, and NLP plays a vital part in classifying products based on evaluations. It is possible to predict sponsored and unpaid reviews using NLP with Machine Learning. This article examined various Machine Learning algorithms for predicting the sentiment of e-commerce website reviews. The automation achieves a maximum validation accuracy of 79.83% when using Fast Text as word embedding and the Multi-channel Convolution Neural Network.
An automatic classification algorithm for software vulnerability based on weighted word vector and fusion neural network

To address the problem that the traditional vectored representation of software vulnerability data has high-dimensional sparsity and leads to unsatisfactory automatic classification, this paper proposes an automatic classification algorithm for software vulnerabilities based on weighted word vectors and fusion neural network. Firstly, the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm is improved to generate the weighted word vector with low dimension and density according to the category distribution. Secondly, the vulnerability classification model TCNN-BiGRU consists of TextCNN (TCNN) and Bidirectional GRU (BiGRU) is constructed, which has made full use of the advantages of convolutional neural network (CNN) and gate recurrent unit neural network (GRU). TextCNN is used to extract local features of vulnerability description text, BiGRU is used to extract global features of vulnerability description text, and the output feature vectors are fused to achieve dimensionality reduction. Finally, the Dropout method and Early Stopping method are introduced to suppress overfitting, and a Softmax classifier is used to classify the vulnerability category. The classification performance of the proposed algorithm is verified by ablation experiments, sparsity problem expriments and comparative experiments on the vulnerability data from NVD dataset on the indicators of accuracy, macro precision rate, macro recall rate and macro F1-score.
The impact of digital transformation towards blockchain technology application in banks to improve accounting information quality and corporate governance effectiveness

By combining two types of studies: theoretical and field, the study aims to explore the impact of digital transformation on the quality of accounting information and the efficacy of corporate governance through the deployment of blockchain technology in banks. The study’s dimensions and variables were examined using a descriptive-analytical technique. To collect data from Jordanian banks’ financial analysts, auditors of shareholders’ accounts, and financial managers in the field research community, data lists were developed. For the analysis of the field research data, SPSS was used. Both the first and third hypotheses were rejected, indicating that the null hypothesis was rejected and the alternative hypothesis was accepted. Neither the second nor the fourth hypotheses were accepted. In order to reap the benefits of blockchain technology in increasing the quality of accounting information and strengthening corporate governance, a digital transformation toward its application in commercial operations is suggested.
A quantitative approach for the comparison of additive local explanation methods

Local additive explanation methods are increasingly used to understand the predictions of complex Machine Learning (ML) models. The most used additive methods, SHAP and LIME, suffer from limitations that are rarely measured in the literature. This paper aims to measure these limitations on a wide range (304) of OpenML datasets using six quantitative metrics, and also evaluate emergent coalitional-based methods to tackle the weaknesses of other methods. We illustrate and validate results on a specific medical dataset, SA-Heart. Our findings reveal that LIME and SHAP's approximations are particularly efficient in high dimension and generate intelligible global explanations, but they suffer from a lack of precision regarding local explanations and possibly unwanted behavior when changing the method's parameters. Coalitional-based methods are computationally expensive in high dimension, but offer higher quality local explanations. Finally, we present a roadmap summarizing our work by pointing out the most appropriate method depending on dataset dimensionality and user's objectives.
SAGA: Stochastic Whole-Body Grasping with Contact

The synthesis of human grasping has numerous applications including AR/VR, video games and robotics. While methods have been proposed to generate realistic hand–object interaction for object grasping and manipulation, these typically only consider interacting hand alone. Our goal is to synthesize whole-body grasping motions. Starting from an arbitrary initial pose, we aim to generate diverse and natural whole-body human motions to approach and grasp a target object in 3D space. This task is challenging as it requires modeling both whole-body dynamics and dexterous finger movements. To this end, we propose SAGA (StochAstic whole-body Grasping with contAct), a framework which consists of two key components: (a) Static whole-body grasping pose generation. Specifically, we propose a multi-task generative model, to jointly learn static whole-body grasping poses and human-object contacts. (b) Grasping motion infilling. Given an initial pose and the generated whole-body grasping pose as the start and end of the motion respectively, we design a novel contact-aware generative motion infilling module to generate a diverse set of grasp-oriented motions. We demonstrate the effectiveness of our method, which is a novel generative framework to synthesize realistic and expressive whole-body motions that approach and grasp randomly placed unseen objects. Code and models are available at https://jiahaoplus.github.io/SAGA/saga.html .
Recent Advances and Applications of Fractional-Order Neural Networks

This paper focuses on the growth, development, and future of various forms of fractional-order neural networks.Multiple advances in structure, learning algorithms, and methods have been critically investigated and summarized.This also includes the recent trends in the dynamics of various fractional-order neural networks.The multiple forms of fractional-order neural networks considered in this study are Hopfield, cellular, memristive, complex, and quaternion-valued based networks.Further, the application of fractionalorder neural networks in various computational fields such as system identification, control, optimization, and stability have been critically analyzed and discussed.
SensePred: Guiding Video Prediction by Wearable Sensors

Video prediction has been studied in several earlier works. However, there are two inherent limitations in existing solutions, one being the ambiguity concern and the other being the degradation problem in lengthy predictions. To overcome these limitations, this work studies using wearable inertial sensors to guide video prediction. We create a data set called Pedestrians with IMU (Ped-IMU) that records people walking around with the wearable devices to collect the relation between inertial measurement unit (IMU) and video data and propose a SensePred model to conquer these limitations. The model takes full wearable sensor information and partial video information as inputs and predicts the missing video information. Simulation results validate the effectiveness of our model.
Application of the Collaborative Unbiased Rank List Integration Method to Select the Materials

For a machining processes, the material selection that is very important, greatly affect both on the economic and technical efficiency of these processes.The selection of each material often was considered many criteria, which have different values in different materials.Multi-criteria decision making that is a technique is used to slove with situations of this type.The collaborative unbiased rank list integration that is a recently proposed multi-criteria decision making method and has only used in a handful of published studies.In this study, this method was used to select the materials for the industrial fields.Material selection in three different areas was performed.In each case, the ranking results of the solutions by the collaborative unbiased rank list integration method were compared with those ones of other methods.The results showed that this method is completely reliable when using for material selection with the considering of multiple criteria.Moreover, the strength of this method is that it does not occur any ranking reversion phenomenon even when considered in different scenarios.
Few-Shot Video Object Detection

We introduce Few-Shot Video Object Detection (FSVOD) with three contributions to real-world visual learning challenge in our highly diverse and dynamic world: 1) a large-scale video dataset FSVOD-500 comprising of 500 classes with class-balanced videos in each category for few-shot learning; 2) a novel Tube Proposal Network (TPN) to generate high-quality video tube proposals for aggregating feature representation for the target video object which can be highly dynamic; 3) a strategically improved Temporal Matching Network (TMN+) for matching representative query tube features with better discriminative ability thus achieving higher diversity. Our TPN and TMN+ are jointly and end-to-end trained. Extensive experiments demonstrate that our method produces significantly better detection results on two few-shot video object detection datasets compared to image-based methods and other naive video-based extensions. Codes and datasets are released at https://github.com/fanq15/FewX .
Enhancing Big Social Media Data Quality for Use in Short-Text Topic Modeling

With the emergence of microblogging platforms and social media applications, large amounts of user-generated data in the form of comments, reviews, and brief text messages are produced every day.Microblog data is typically of poor quality; hence improving the quality of the data is a significant scientific and practical challenge.In spite of the relevance of the problem, there has been not much work so far, especially in regard to microblog data quality for Short-Text Topic Modelling (STTM) purposes.This paper addresses this problem and proposes an approach called the social media data cleansing model (SMDCM) to improve data quality for STTM.We evaluate SMDCM using six topic modelling methods, namely the Latent Dirichlet Allocation (LDA), Word-Network Topic Model (WNTM), Pseudo-document-based Topic Modelling (PTM), Biterm Topic Model (BTM), Global and Local word embedding-based Topic Modeling (GLTM), and Fuzzy Topic modelling (FTM).We used the Real-world Cyberbullying Twitter (RW-CB-Twitter) and the Cyberbullying Mendeley (CB-MNDLY) datasets in the evaluation.The results proved the efficiency of the GLTM and WNTM over the other STTM models when applying the SMDCM techniques, which achieved optimum topic coherence and high accuracy values on RW-CB-Twitter and CB-MNDLY datasets.
Domain Adversarial Training for Aspect-Based Sentiment Analysis

The continuously expanding digital possibilities, increasing number of social media platforms, and growing interest of companies in online marketing increase the importance of Aspect-Based Sentiment Analysis (ABSA). ABSA focuses on predicting the sentiment of an aspect in a text. In an ideal scenario, we would have labeled data for every existing domain, but acquiring annotated training data is costly. Transfer learning resolves this issue by building models that can be employed in different domains. The proposed work extends the state-of-the-art LCR-Rot-hop++ model for ABSA with the methodology of Domain Adversarial Training (DAT) in order to create a deep learning adaptable cross-domain structure, called the DAT-LCR-Rot-hop++. The major advantage of the DAT-LCR-Rot-hop++ is the fact that it does not require any labeled target domain data. The results are obtained for six different domain combinations with testing accuracies ranging from 37% up until 77%, showing both the limitations and benefits of this approach. Once DAT is able to find the similarities between domains, it produces good results, but if the domains are too distant, it is not capable of generating domain-invariant features.
MSAGNet: Multi-Stream Attribute-Guided Network for Occluded Pedestrian Detection

Pedestrian detection plays an indispensable role in human-centric applications. Although having enjoyed the merits of generic object detectors based on deep learning frameworks, pedestrian detection is still a persistent crucial task since the pedestrians often gather together and occlude each other. In this study, we propose a simple yet effective Multi-Stream Attribute-Guided Network (MSAGNet) to regard occluded pedestrian detection as a standard central point and height estimation problem. Specifically, we focus on searching for the central points of the pedestrians and predicting the scales and offsets of the corresponding pedestrians. Meanwhile, an adaptive weighting parameter, i.e., Intersection over the Visible part region of ground truth (IoV), is utilized to conduct accurate bounding box regression. Furthermore, a novel nonlinear Non-Maximum Suppression (NMS) is proposed to flexibly prune false positives and decrease the miss rate of adjacent overlapping pedestrians. Experimental results on Caltech-USA, CityPersons, CrowdHuman and WiderPerson pedestrian datasets show that the proposed MSAGNet can obtain significant performance boosts, while maintaining a reasonable run-time speed.
LSDNet: A Lightweight Self-Attentional Distillation Network for Visual Place Recognition

Visual Place Recognition (VPR) has become an indispensable capacity for mobile robots to operate in large-scale environments. Existing methods in this field mostly focus on exploring high-performance encoding strategies, while few attempts are devoted to lightweight models that balance per-formance and computational cost. In this work, we propose a Lightweight Self-attentional Distillation Network (LSDNet) aiming to obtain advantages of both performance and efficiency. (1) From a performance perspective, an attentional encoding strategy is proposed to integrate crucial information in the scene. It extends the NetVlad architecture with a self-attention module to facilitate non-local information interaction between local features. Through further visual word vector rescaling, the final image representation can benefit from both non-local spatial integration and cluster-wise weighting. (2) From an efficiency perspective, LSDNet is built upon a lightweight back-bone. To maintain comparable performance to large backbone models, a dual distillation strategy is introduced. It prompts LSDNet to learn both encoding patterns in the hidden space and feature distributions in the encoding space from the teacher model. Through distillation-augmented training, LSDNet is able to rival the teacher model and outperform SOTA global representations with the same lightweight backbone.
Few-Shot Object Detection with Model Calibration

Few-shot object detection (FSOD) targets at transferring knowledge from known to unknown classes to detect objects of novel classes. However, previous works ignore the model bias problem inherent in the transfer learning paradigm. Such model bias causes overfitting toward the training classes and destructs the well-learned transferable knowledge. In this paper, we pinpoint and comprehensively investigate the model bias problem in FSOD models and propose a simple yet effective method to address the model bias problem with the facilitation of model calibrations in three levels: 1) Backbone calibration to preserve the well-learned prior knowledge and relieve the model bias toward base classes, 2) RPN calibration to rescue unlabeled objects of novel classes and, 3) Detector calibration to prevent the model bias toward a few training samples for novel classes. Specifically, we leverage the overlooked classification dataset to facilitate our model calibration procedure, which has only been used for pre-training in other related works. We validate the effectiveness of our model calibration method on the popular Pascal VOC and MS COCO datasets, where our method achieves very promising performance. Codes are released at https://github.com/fanq15/FewX .