Reviewerly/exHarmony huggingface dataset:
Shallow Neural Network and Ontology-Based Novel Semantic Document Indexing for Information Retrieval

Information Retrieval (IR) systems are developed to fetch the most relevant content matching the user's information needs from a pool of information. A user expects to get IR results based on the conceptual contents of the query rather than keywords. But traditional IR approaches index documents based on the terms that they contain and ignore semantic descriptions of document contents. This results in a vocabulary gap when queries and documents use different terms to describe the same concept. As a solution to this problem and to improve the performance of IR systems, we have designed a Shallow Neural Network and ontology-based novel approach for semantic document indexing (SNNOntoSDI). The SNNOntoSDI approach identifies the concepts representing a document using the word2vec model (a Shallow Neural Network) and domain ontology. The relevance of a concept in the document is measured by assigning weight to the concept based on its statistical, semantic, and scientific Named Entity features. The parameters of these feature weights are calculated using the Analytic Hierarchy Process (AHP). Finally, concepts are ranked in order of relevance. To empirically evaluate the SNNOntoSDI approach, a series of experiments were carried out on five standard publicly available datasets. The results of experiments demonstrate that the SNNOntoSDI approach outperformed state-of-the-art methods, with an average improvement of 29% and 25% in average accuracy and F-measure respectively.
Cross-Domain Attention Network for Unsupervised Domain Adaptation Crowd Counting

Unsupervised domain adaptation crowd counting (UDACC) has been studied with practical research utility by getting rid of the labeling burden on large-scale dense crowds in the target domain. Current methods generalize well within the specific domain gap by directly aligning domain distributions or translating synthetic data to realistic images. However, it is difficult to define domain gaps among complex real-world datasets, in which the images vary greatly in style, density level and/or content. To tackle this problem, in this paper, we propose a Cross-Domain Attention Network (CDANet), which can effectively generalize the model to the unlabeled domain on both unsupervised synthetic-to-realistic and realistic-to-realistic crowd counting. Specifically, we propose a Cross-Domain Attention Module (CDAM) to learn domain-related information between the source and target domain, which extracts relations in cross-domain attentive information, thus enhancing crowd-informative features. Moreover, to make our CDAM invariant to domain shifts, we introduce a consistency penalty to ensure that the attention maps are consistent before and after the domain shifting. Thus our CDANet can pay attention to the shared counting information across domains, while remaining its invariant ability during domain adaptation. Extensive experiments on several common benchmarks for UDACC demonstrate that our CDANet gets competitive results on both unsupervised synthetic-to-realistic and realistic-to-realistic UDACC tasks.
Quaternion Integers Based Higher Length Cyclic Codes and Their Decoding Algorithm

The decoding algorithm for the correction of errors of arbitrary Mannheim weight has discussed for Lattice constellations and codes from quadratic number fields. Following these lines, the decoding algorithms for the correction of errors of length cyclic codes over quaternion integers of Quaternion Mannheim weight one up to two coordinates have considered. In continuation, the case of cyclic codes of lengths and has studied to improve the error correction efficiency. In this study, we present the decoding of cyclic codes of length and length 2 (where is prime integer and is Euler phi function) over Hamilton Quaternion integers of Quaternion Mannheim weight for the correction of errors. Furthermore, the error correction capability and code rate tradeoff of these codes are also discussed. Thus, an increase in the length of the cyclic code is achieved along with its better code rate and an adequate error correction capability.
Disentangled Modeling of Social Homophily and Influence for Social Recommendation

Social recommendation leverages social information to alleviate data sparsity and cold-start issues of collaborative filtering&#x00A0;(CF) methods. Most existing works model user interests following the assumption of <i>social homophily</i> based on social-relation data. The explicit modeling of <i>social influence</i>, which also largely affects user behaviors, has not been well explored. Considering user behaviors may be driven by social factors in today&#x2019;s information services&#x00A0;(<i>e.g.</i>, purchasing products shared by close friends on social e-commerce applications), these methods will be suboptimal. In this work, we propose a method modeling both social homophily-aware user interests and social influence as two essential effects on user behaviors for social recommendation, named as DISGCN (short for <b>DIS</b>entangled modeling of Social homophily and influence with <b>G</b>raph <b>C</b>onvolutional <b>N</b>etwork). Specifically, we devise a disentangled embedding layer to encode these two effects. Furthermore, two tailored graph convolutional layers are developed to disentangle them refinedly, leveraging the high-order embedding propagation in social-network graph from two aspects. Technically, first, the operation of attentive embedding propagation is adopted for capturing personalized social homophily-aware interests, and second, the item-gate-based embedding propagation is proposed for capturing item-specific social influence. In addition, to ensure the disentanglement of social influence, we propose a contrastive learning framework that endows corresponding embeddings with explicit semantics. Extensive experiments on two real-world datasets demonstrate the effectiveness of our proposed model. Further studies also verify the rationality and necessity of our designs. We have released the datasets and codes at this link: <uri>https://github.com/tsinghua-fib-lab/DISGCN</uri>.
Modular and Parameter-Efficient Multimodal Fusion with Prompting

Recent research has made impressive progress in large-scale multimodal pre-training.In the context of the rapid growth of model size, it is necessary to seek efficient and flexible methods other than finetuning.In this paper, we propose to use prompt vectors to align the modalities.Our method achieves comparable performance to several other multimodal fusion methods in low-resource settings.We further show that our method is modular and parameter-efficient for processing tasks involving two or more data modalities.
PE-former: Pose Estimation Transformer

Vision transformer architectures have been demonstrated to work very effectively for image classification tasks. Efforts to solve more challenging vision tasks with transformers rely on convolutional backbones for feature extraction. In this paper we investigate the use of a pure transformer architecture (i.e., one with no CNN backbone) for the problem of 2D body pose estimation. We evaluate two ViT architectures on the COCO dataset. We demonstrate that using an encoder-decoder transformer architecture yields state of the art results on this estimation problem.
An Useful Survey on Supervised Machine Learning Algorithms: Comparisons and Classifications

The look for methodologies that can make inferences from externally supplied data develop broad hypotheses that are subsequently used to create forecasts concerning future events is known as supervised machine learning (SML). This study examine machine learning (ML) classification strategies, compares supervised learning algorithms, and determines foremost efficient classification algorithm based on the data set, number of instances, and variables (features). ML with the Waikato Environment for Knowledge Analysis (WEKA) tool, 7 different machine learning algorithms were considered: Decision Table, Random Forest (RF), Naive Bayes (NB), support vector machine (SVM), neural networks (Perceptron), JRip, and Decision Tree (J48). Time it takes to make a design and be concise (accuracy) are factors on the one end, and the kappa statistic and Mean Absolute Error (MAE) are factors on the other. For supervised predictive machine learning to work, machine learning algorithms must be accurate, and error-free.
Trust-Aware Detection of Malicious Users in Dating Social Networks

Online dating is an increasingly thriving business which boosts billion-dollar revenues and attracts users in the tens of millions. Despite its popularity, internet dating is not exempt from the concerns about privacy and trust posed by the revelation of potentially sensitive data as well as the exposure to self-reported (and hence potentially distorted) information. The increasing popularity of online dating networks leads to an increase in security concerns and challenges, as well as harmful actions and attacks, such as creating fake accounts, phishing on these networks. To maintain the safety of legitimate online dating users, it is critical to recognize and isolate criminal people as soon as possible. However, researchers concerning malicious user detection in dating social networks are merely a few. To address some key challenges in this space, we propose a trust-aware detection framework to detect malicious users based on different kinds of data from a real dating site. In particular, we develop a user trust model to distinguish between malicious and legitimate users. Furthermore, we propose a novel data-balancing method to improve the recall rate of malicious user detection. Extensive experiments have been conducted over real-world datasets. The results show that the proposed approach yields a precision of up to 59.16% and a recall rate of up to 73%, which is significantly higher than other baseline algorithms.
Following Natural Language Instructions for Household Tasks With Landmark Guided Search and Reinforced Pose Adjustment

We study the challenging problem of following natural language instructions on a mobile manipulator robot. This task is challenging because it requires the robot to integrate the semantics of the unconstrained natural language instructions with the robot’s egocentric visual observations of the environment which are typically incomplete and noisy. To address these challenges, we propose a method that is able to use visible landmarks to more efficiently explore the environment in search of the objects described by the natural language instructions. Additionally, we propose using a pose adjustment policy during manipulation planning to help the robot recover from noisy visual observations. We show that this policy can be trained through experience with reinforcement learning as well as with human-in-the-loop feedback. We evaluate our approach on the popular ALFRED instruction following benchmark and show that these methods achieve state-of-the-art performance (35.41%) with a substantial (8.92% absolute) gap from prior work.
Security Prioritized Heterogeneous Earliest Finish Time Workflow Allocation Algorithm for Cloud Computing

Cloud computing, hurriedly, has become an essential platform for many scientific applications. In the domain, many existing works have been developed for optimizing the parameters from the quality of service (QoS) of the cloud system. However, effective workflow allocation with security requirements is emerging as a challenging issue in the cloud system. Consequently, security requirements satisfaction also becomes an essential in the mapping of workflow tasks having high priorities onto the virtual machines. Hence, secure and efficient workflow tasks execution entertaining their priorities are the need of the hours. In this chapter, a Security Prioritized Heterogeneous Earliest Finish Time (SPHEFT) algorithm has been proposed to optimize the security overhead and guarantee ratio of the workflow tasks in cloud system. Here, SPHEFT offers higher priorities to the tasks having more security requirements and, therefore, assigned on the more reliable virtual machines. In experimental evaluation, SPHEFT is compared with the standard HEFT algorithm for varying set of tasks. The experimental results show that SPHEFT has better performance on security overhead and more excellent efficiencies on improving the tasks guarantee ratio.
Ecopedagogy Disrupting Postdigital Divides of (Neo)Coloniality, (Eco)Racism, and Anthropocentricism: A Case Study

Looking at the growth of social media and ourselves being affected/absorbed by it, especially by digital monopolies, it becomes apparent that focusing on teaching critical media literacies is increasingly essential for socio-environmental justice and planetary sustainability and must continuously evolve along with technological ‘developments’. In this chapter we discuss the needs, possibilities, and challenges of teaching ecopedagogical literacies reinvented from Freirean pedagogies to critically read technologies through lenses of postdigitalism, media culture theories, globalizations, (de)coloniality, (eco)racism, (eco)feminism, queer theories, and Southern/Indigenous epistemologies, among others. After discussing oppressive and dominant divides, we argue that educators must teach through diverse models of ecopedagogies for praxis to disrupt unjust and unsustainable divides that other us from one another and from the rest of Nature. Through a case study of a higher education ecopedagogical course in which students created a mass literacy campaign to disrupt these divides, we discuss first-hand the key ecopedagogical needs, challenges and possibilities for disrupting and sometimes bridging socio-environmental divides. We conclude by arguing that understanding of and fighting for justice and sustainability (within and beyond the anthroposphere) must occur both inside and outside of digital spaces.
An Efficient Transfer Learning Model for Predicting Forged (Handwritten) Signature

Signature fraud around the world is increasing at an alarming rate. Fraud in the signature may harm a person or an organization by false transactions and false document authorization, which may lead to an irreversible loss. Thus, the purpose of this research is to predict forged signature using machine learning techniques. To attain the objective, different state-of-the-art machine learning models, including Neural Network, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest Classifier, were developed to classify between fraud and real signatures. The VGG-16 pre-trained model was used to improve the Neural Network's performance. As outcome, the transfer learning based Neural Network model showed the highest accuracy-96.7%, followed by Support Vector Machine (81.7%), K-Nearest Neighbors (71.7%), Random Forest (70.0%), and Decision Tree (68.3%).
Physics-informed reservoir computing with autonomously switching readouts: a case study in pneumatic artificial muscles

We introduce an approach based on physics-informed neural networks to predict the length of a McKibben pneumatic artificial muscle (PAM) from a series of pressure measurements. We implemented an echo state network, which is a type of recurrent neural network with autonomously switching readouts corresponding to the different physical states of the PAM. The physical state we focus on in the current study is the direction of motion affected by hysteresis. The switching is realized by introducing gate architecture, whose states are also controlled by using the same recurrent network that outputs the length of the PAM. We demonstrated that handling the different physical states of the PAM by switching readouts will robustly yield performance in predicting the length of the PAM. We also demonstrated that Gaussian mixture models as a classifier for clustering the reservoir state autonomously and the results in classification are consistent with the physical state of the PAM.
ALSTM: An Attention-based LSTM Model for Multi-Scenario Bandwidth Prediction

Bandwidth-sensitive applications rely on the accurate estimation of the bottleneck bandwidth. The real-time bandwidth prediction enables the application to cope with bandwidth fluctuation and adjust the transmission strategy to improve the Quality of Experience (QoE) of user. The traditional bandwidth prediction model hardly considers the bandwidth characteristics in various scenarios, making it challenging to achieve high accuracy. In this paper, we propose ALSTM model, which is based on the Long Short Term Memory (LSTM) recurrent neural network and the attention mechanism for multi-scenario bandwidth prediction. Firstly, we conduct the bandwidth trajectories feature analysis, and then we adopt the Support Vector Machine (SVM) to classify scenarios based on the bandwidth characteristics. Secondly, we apply an attention mechanism to assign weights to the input of the bandwidth series, and the attention feature is utilized to effectively select the feature sequences as input to the LSTM model for the prediction. The experimental results show that the ALSTM reduces the Root Mean Square Error (RMSE) by 20%, and the Mean Average Error (MAE) is improved by 26%. For practical applications, we adopt the pre-trained SVM model for real-time scenario detection, dynamic switch the corresponding ALSTM model, and the switching success rate is up to 86%. In addition, by deploying the proposed bandwidth prediction model ALSTM, the DASH's QoE has increased by more than 25%.
Clustered Federated Multi-Task Learning with Non-IID Data

Federated Learning enables the collaborative learning in cross-client scenarios while keeping the clients' data local for privacy. The presence of non-IID data is one of major challenges in federated learning. To deal with this statistic challenge, federated multi-task learning considers the local training for each client as a single task. However, all the clients must participate in each training round, and it is inapplicable to mobile or IOT devices with constrained communication capability. To achieve the communication-efficiency and high accuracy with non-IID data, we propose a clustered federated multi-task learning by exploring client clustering and multi-task learning. We measure the similarities of local data among clients indirectly through their models' parameters, and design a client clustering strategy to enable clients with similar data distribution into a same group. The limitation of full-participation can be eliminated through the way of model training for groups instead of individual clients. The convergence analysis and experimental evaluation on real-world datasets shows that our work outperforms the basic federated learning in accuracy and is also more communication-efficient than the existing federated multi-task learning.
Document Similarity Detection using Rabin-Karp and Cosine Similarity Algorithms

Plagiarism action often occurs in the world of education. One example of plagiarism in education, especially in the university environment, is the frequent occurrence of similarities between theses made by students. One solution is to develop an application that can detect similarities in thesis documents that can be used by all groups, especially to facilitate students. Natural Language Processing (NLP) is one of the fields that can be a solution in developing document similarity detection applications. In this study, testing focused on Indonesian documents. In addition, this study aims to build a system or application to detect the similarity of Indonesian documents using the Rabin-Karp algorithm with the Cosine Similarity method as a Distance-Based Similarity Measure and using the Stemming technique. The experiment results using 15 documents of scientific publications in Indonesian show that the Rabin-Karp algorithm with Cosine Similarity can be used to detect the similarity of published manuscripts, especially in Indonesian. With the fastest processing time of seconds and the longest around. seconds, this algorithm quickly detects document similarities, especially in documents. Further research is recommended to optimize from the algorithm side so that the processing time is faster with a much larger number of words.
Improved YOLOv5 with BiFPN on PCB Defect Detection

As an image classification technology, target detection also needs to identify specific locations of predefined categories. Therefore, target detection is not only to solve the problem of recognizing what objects exactly, but also points out locations. The development of YOLO series has greatly improved speed and accuracy in target detection technology. However, it performs not so good as detecting normal objects when targets are quite small. Thus, this paper proposes to fuse BIFPN network and set smaller and denser anchors to improve this, and then public PCB defect detection data set is used to test and verify effect of refined method. Experimental results show that improved method shifts mAP@0.5 from 0.968 to 0.979 and mAP@0.5:0.95 from 0.494 to 0.501, as well as deducts confusion rate. With the improved method, the training cost also decrases significantly.
One-Dimensional Convolutional Neural Network Model for Abnormal Driving Behaviors Detection Using Smartphone Sensors

Abnormal driving behavior detection (ADBD) is essential to improving driving safety. Traditional approaches usually extract features manually, resulting in insufficient exploration of deep features of driving behavior. To address the limitation of shallow feature-based approaches, we propose a one-dimensional convolutional neural network (1D CNN) model for ADBD and transforms it into the time series multi-classification. Firstly, we construct a dataset of driving behavior using smartphone sensors, and five fine-grained abnormal driving behaviors (Hard braking, Weaving, Swerving, Quick turn, Quick U-turn) are defined and labeled by analyzing the abnormal driving feature patterns. Then, we need to map the smartphone coordinate to the vehicle coordinate, and apply the low-pass filtering on the input data for high-frequency noise reduction. Finally, we train the dataset with the 1D CNN model for feature extraction and classification. The experimental results show that the proposed 1D CNN model efficiently achieves multi-classification of abnormal driving behaviors with an average accuracy of 97%, significantly better than the traditional algorithm of k-nearest neighbor and support vector machine.
JavaBERT: Training a Transformer-Based Model for the Java Programming Language

Code quality is and will be a crucial factor while developing new software code, requiring appropriate tools to ensure functional and reliable code. Machine learning techniques are still rarely used for software engineering tools, missing out the potential benefits of its application. Natural language processing has shown the potential to process text data regarding a variety of tasks. We argue, that such models can also show similar benefits for software code processing. In this paper, we investigate how models used for natural language processing can be trained upon software code. We introduce a data retrieval pipeline for software code and train a model upon Java software code. The resulting model, JavaBERT, shows a high accuracy on the masked language modeling task showing its potential for software engineering tools.