Reviewerly/exHarmony huggingface dataset:
Ask and Ye shall be Answered: Bayesian tag-based collaborative recommendation of trustworthy experts over time in community question answering

Several challenging issues have yet to be jointly addressed in the recommendation of experts for community question answering, including dynamicity, comprehensive profiling, the incorporation of auxiliary data, and the manipulation of heterogeneous information. We argue that a unified treatment of these issues is beneficial in improving recommendation effectiveness. In this paper, we introduce and formalize a new and more thorough instance of the expert-recommendation task for community question answering, which is conceived to suitably account for the connections among the targeted issues. Moreover, in order to carry out the devised task, we present an innovative Bayesian tag-based approach that systematically handles all of the targeted issues in a coherent and seamlessly unified manner. At the heart of the proposed approach is an unprecedented principled fusion of various types of information. The integrated information enables a peculiar characterization of community members in terms of three inherent properties, i.e., their tag-based temporally-discounted interest, expertise, and willingness to respond. The first property is determined by looking into questions, while the last two are determined from answers. Within a generic question answering community, the three properties of its members allow for selectively routing any question to a specifically addressed set of responders. These are recommended as trustworthy repliers, who are not only actually experts in the themes of the routed question as per its tags, but also still interested in such themes and willing to answer at routing time. An extensive empirical assessment involving real-world benchmark data from heterogeneous domains reveals the higher recommendation effectiveness of the presented approach compared to state-of-the-art competitors.
Sentiment Analysis Based on Tweet Reply at Public Figure Account using Machine Learning and Latent Semantic Analysis

Twitter is a social media platform that enables its user to communicate or see various running events online. It also enables everyone to share information. Public figures, such as celebrities, artists, or politicians often dominate the talk and trending topics. The pros and cons among the public figure accounts cause either positive or negative sentiments. Thus this condition urges a system that can classify each of the user's replies to a public figure account as a consideration to change to a better communication pattern. Some of the possible methods are the naive Bayes, SVM, and logistic regression, all of these methods are combined with the Latent Semantic Analysis (LSA). The classification system will be based on 1500 dataset that has been labeled and divided into 80% training data and 20% testing data. The result of the confusion matrix showed the highest accuracy for SVM 80.4%, logistic regression 80.6%, and multinomial Naive Bayes 78.6%.
Interpretation of Bahasa Isyarat Malaysia (BIM) Using SSD-MobileNet-V2 FPNLite and COCO mAP

This research proposes a study on two-way communication between deaf/mute and normal people using an Android application. Despite advancements in technology, there is still a lack of mobile applications that facilitate two-way communication between deaf/mute and normal people, especially by using Bahasa Isyarat Malaysia (BIM). This project consists of three parts: First, we use BIM letters, which enables the recognition of BIM letters and BIM combined letters to form a word. In this part, a MobileNet pre-trained model is implemented to train the model with a total of 87,000 images for 29 classes, with a 10% test size and a 90% training size. The second part is BIM word hand gestures, which consists of five classes that are trained with the SSD-MobileNet-V2 FPNLite 320 × 320 pre-trained model with a speed of 22 s/frame rate and COCO mAP of 22.2, with a total of 500 images for all five classes and first-time training set to 2000 steps, while the second- and third-time training are set to 2500 steps. The third part is Android application development using Android Studio, which contains the features of the BIM letters and BIM word hand gestures, with the trained models converted into TensorFlow Lite. This feature also includes the conversion of speech to text, whereby this feature allows converting speech to text through the Android application. Thus, BIM letters obtain 99.75% accuracy after training the models, while BIM word hand gestures obtain 61.60% accuracy. The suggested system is validated as a result of these simulations and tests.
YOLO Publics: The Potential for Creative Subversion of an Online Trading Community

Digitally mediated publics are often discussed in terms of extremism and radicalization, but it remains possible that digital communication technologies can engender new connections and conversations through “creative subversion.” This article explores the potentials of one specific instance of such creative subversion: the “GameStop rescue” as let by members of the subreddit forum “WallStreetBets” (WSB) in the early months of 2021. From a communicative perspective, what is interesting about this series of events is not only the digital platforms and affordances that enabled it, but also the reckless behavior of WSB members and the ways in which this behavior was communicated—and continues to be celebrated and facilitated by the online trading community. Members share “loss porn,” praise each other for having “diamond hands” when holding on to investments that are losing value, and celebrate the principle of YOLO (you only live once). We conceptualize WSB as a YOLO public, an online community that is loosely and temporarily formed through the common action of seizing the opportunity to wreak havoc around power. Furthermore, we understand the events of the GameStop rescue as a controversial encounter that, we argue, offers hope for digitally mediated publics to develop in dynamic relations of difference rather than as stabilized oppositions.
Node Selection Algorithm for Federated Learning Based on Deep Reinforcement Learning for Edge Computing in IoT

The Internet of Things (IoT) and edge computing technologies have been rapidly developing in recent years, leading to the emergence of new challenges in privacy and security. Personal privacy and data leakage have become major concerns in IoT edge computing environments. Federated learning has been proposed as a solution to address these privacy issues, but the heterogeneity of devices in IoT edge computing environments poses a significant challenge to the implementation of federated learning. To overcome this challenge, this paper proposes a novel node selection strategy based on deep reinforcement learning to optimize federated learning in heterogeneous device IoT environments. Additionally, a metric model for IoT devices is proposed to evaluate the performance of different devices. The experimental results demonstrate that the proposed method can improve training accuracy by 30% in a heterogeneous device IoT environment.
Echo State Neural Network Based on an Improved Gray Wolf Algorithm Predicts Porosity through Logging Data

In oil exploration and development, many reservoir parameters are very essential for reservoir description, especially porosity. The porosity obtained by indoor experiments is reliable, but human and material resources will be greatly invested. Experts have introduced machine learning into the field of porosity prediction but with the shortcomings of traditional machine learning models, such as hyperparameter abuse and poor network structure. In this paper, a meta-heuristic algorithm (Gray Wolf Optimization algorithm) is introduced to optimize the ESN (echo state neural) network for logging porosity prediction. Tent mapping, a nonlinear control parameter strategy, and PSO (particle swarm optimization) thought are introduced to optimize the Gray Wolf Optimization algorithm to improve the global search accuracy and avoid local optimal solutions. The database is constructed by using logging data and porosity values measured in the laboratory. Five logging curves are used as model input parameters, and porosity is used as the model output parameter. At the same time, three other prediction models (BP neural network, least squares support vector machine, and linear regression) are introduced to compare with the optimized models. The research results show that the improved Gray Wolf Optimization algorithm has more advantages than the ordinary Gray Wolf Optimization algorithm in terms of super parameter adjustment. The IGWO-ESN neural network is better than all machine learning models mentioned in this paper (GWO-ESN, ESN, BP neural network, least squares support vector machine, and linear regression) in terms of porosity prediction accuracy.
Analysis of Histopathological Images for Early Diagnosis of Oral Squamous Cell Carcinoma by Hybrid Systems Based on CNN Fusion Features

Oral squamous cell carcinoma (OSCC) is one of the deadliest and most common types of cancer. The incidence of OSCC is increasing annually, which requires early diagnosis to receive appropriate treatment. The biopsy technique is one of the most important techniques for analyzing samples, but it takes a long time to get results. Manual diagnosis is still subject to errors and differences in doctors’ opinions, especially in the early stages. Thus, automated techniques can help doctors and patients to receive appropriate treatment. This study developed several hybrid models based on the fused CNN features for diagnosing OSCC-100x and OSCC-400x datasets for oral cancer, which have the ability to analyze medical images with a high level of precision and accuracy. They can detect subtle patterns, abnormalities, or indicators of diseases that may be difficult to recognize with the naked eye. The systems have the potential to significantly reduce human error and provide more consistent and reliable results, resulting in improved diagnostic accuracy. The systems also have the potential for early detection of OSCC for treatment success and improved patient outcomes. By detecting diseases at an early stage, clinicians can initiate interventions in a timely manner, potentially preventing OSCC progression and improving the chances of successful treatment. The first strategy was based on GoogLeNet, ResNet101, and VGG16 models pretrained, which did not achieve satisfactory results. The second strategy was based on GoogLeNet, ResNet101, and VGG16 models based on the adaptive region growing (ARG) segmentation algorithm. The third strategy is based on a mixed technique between GoogLeNet, ResNet101, and VGG16 models and ANN and XGBoost networks based on the ARG hashing algorithm. The fourth strategy for oral cancer diagnosis by ANN and XGBoost is based on features fused between CNN models. The ANN with fusion features of GoogLeNet-ResNet101-VGG16 yielded an AUC of 98.85%, accuracy of 99.3%, sensitivity of 98.2%, precision of 99.5%, and specificity of 98.35%.
Federated Blockchain Learning at the Edge

Machine learning, particularly using neural networks, is now widely adopted in practice even with the IoT paradigm; however, training neural networks at the edge, on IoT devices, remains elusive, mainly due to computational requirements. Furthermore, effective training requires large quantities of data and privacy concerns restrict accessible data. Therefore, in this paper, we propose a method leveraging a blockchain and federated learning to train neural networks at the edge effectively bypassing these issues and providing additional benefits such as distributing training across multiple devices. Federated learning trains networks without storing any data and aggregates multiple networks, trained on unique data, forming a global network via a centralized server. By leveraging the decentralized nature of a blockchain, this centralized server is replaced by a P2P network, removing the need for a trusted centralized server and enabling the learning process to be distributed across participating devices. Our results show that networks trained in such a manner have negligible differences in accuracy compared to traditionally trained networks on IoT devices and are less prone to overfitting. We conclude that not only is this a viable alternative to traditional paradigms but is an improvement that contains a wealth of benefits in an ecosystem such as a hospital.
Automatic detection of attachment style in married couples through conversation analysis

Abstract Analysis of couple interactions using speech processing techniques is an increasingly active multi-disciplinary field that poses challenges such as automatic relationship quality assessment and behavioral coding. Here, we focused on the prediction of individuals’ attachment style using interactions of recently married (1–15 months) couples. For low-level acoustic feature extraction, in addition to the frame-based acoustic features such as mel-frequency cepstral coefficients (MFCCs) and pitch, we used the turn-based i-vector features that are the commonly used in speaker verification systems. Sentiments, positive and negative, of the dialog turns were also automatically generated from transcribed text and used as features. Feature and score fusion algorithms were used for low-level acoustic features and text features. Even though score and feature fusion algorithms performed similar, predictions with score fusion were more consistent when couples have known each other for a longer period of time.
An Interpretable Fake News Detection Method Based on Commonsense Knowledge Graph

Existing deep learning-based methods for detecting fake news are uninterpretable, and they do not use external knowledge related to the news. As a result, the authors of the paper propose a graph matching-based approach combined with external knowledge to detect fake news. The approach focuses on extracting commonsense knowledge from news texts through knowledge extraction, extracting background knowledge related to news content from a commonsense knowledge graph through entity extraction and entity disambiguation, using external knowledge as evidence for news identification, and interpreting the final identification results through such evidence. To achieve the identification of fake news containing commonsense errors, the algorithm uses random walks graph matching and compares the commonsense knowledge embedded in the news content with the relevant external knowledge in the commonsense knowledge graph. The news is then discriminated as true or false based on the results of the comparative analysis. From the experimental results, the method can achieve 91.07%, 85.00%, and 89.47% accuracy, precision, and recall rates, respectively, in the task of identifying fake news containing commonsense errors.
Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic

Social media is a widely used platform that provides a huge amount of user-generated content that can be processed to extract information about users' emotions.This has numerous benefits, such as understanding how individuals feel about certain news or events.It can be challenging to categorize emotions from text created on social media, especially when trying to identify several different emotions from a short text length, as in a multi-label classification problem.Most previous work on emotion classification has focused on deep neural networks such as Convolutional Neural Networks and Recurrent Neural Networks.However, none of these networks have used semantic and syntactic knowledge to classify multiple emotions from a text.In this study, semantic and syntactic aware graph attention networks were proposed to classify emotions from a text with multiple labels.We integrated semantic information in the graph attention network in the form of Universal Conceptual Cognitive Annotation and syntactic information in the form of dependency trees.Our extensive experimental results showed that our two models, UCCA-GAT (accuracy = 71.2) and Dep-GAT (accuracy = 68.7),were able to outperform the state-of-the-art performance on both the challenging SemEval-2018 E-c: Detecting Emotions (multi-label classification) English dataset (accuracy = 58.8)and GoEmotions dataset (accuracy = 65.9).
SDSIOT: An SQL Injection Attack Detection and Stage Identification Method Based on Outbound Traffic

An SQL Injection Attack (SQLIA) is a major cyber security threat to Web services, and its different stages can cause different levels of damage to an information system. Attackers can construct complex and diverse SQLIA statements, which often cause most existing inbound-based detection methods to have a high false-negative rate when facing deformed or unknown SQLIA statements. Although some existing works have analyzed different features for the stages of SQLIA from the perspectives of attackers, they primarily focus on stage analysis rather than different stages’ identification. To detect SQLIA and identify its stages, we analyze the outbound traffic from the Web server and find that it can differentiate between SQLIA traffic and normal traffic, and the outbound traffic generated during the two stages of SQLIA exhibits distinct characteristics. By employing 13 features extracted from outbound traffic, we propose an SQLIA detection and stage identification method based on outbound traffic (SDSIOT), which is a two-phase method that detects SQLIAs in Phase I and identifies their stages in Phase II. Importantly, it does not need to analyze the complex and diverse malicious statements made by attackers. The experimental results show that SDSIOT achieves an accuracy of 98.57% for SQLIA detection and 94.01% for SQLIA stage identification. Notably, the accuracy of SDSIOT’s SQLIA detection is 8.22 percentage points higher than that of ModSecurity.
Depth Optimization of FLT-Based Quantum Inversion Circuit

Works on quantum computing and cryptanalysis have increased significantly in the past few years. Various constructions of quantum arithmetic circuits as one of the primary elements in the field have also been proposed. However, there have only been a few studies on finite field inversion despite its essential use in realizing quantum algorithms, such as in Shor’s algorithm for Elliptic Curve Discrete Logarithm Problem (ECDLP). In this study, we propose to reduce the depth of the existing quantum Fermat’s Little Theorem (FLT)-based inversion circuit for the binary finite field. In particular, we propose to follow a complete waterfall approach to translate the Itoh-Tsujii’s variant of FLT to the corresponding quantum circuit and remove the inverse squaring operations employed in the previous work by Banegas et al., lowering the number of CNOT gates (i.e., CNOT count) as well as slightly reducing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> depth, which contributes to a reduced overall depth and gate count. Furthermore, we concretely verify our method and compare it with the previous work in Qiskit, a quantum computer simulation environment, by constructing both our method and the previous work from scratch and performing the resource analysis. Additionally, we propose employing the relative-phase Toffoli gate by Gidney as opposed to the standard Toffoli implementation, which yields a significantly lower <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> depth while further reducing the overall depth. Our approach can serve as an alternative for a time-efficient implementation.
Transformer: A General Framework from Machine Translation to Others

Machine translation is an important and challenging task that aims at automatically translating natural language sentences from one language into another. Recently, Transformer-based neural machine translation (NMT) has achieved great break-throughs and has become a new mainstream method in both methodology and applications. In this article, we conduct an overview of Transformer-based NMT and its extension to other tasks. Specifically, we first introduce the framework of Transformer, discuss the main challenges in NMT and list the representative methods for each challenge. Then, the public resources and toolkits in NMT are listed. Meanwhile, the extensions of Transformer in other tasks, including the other natural language processing tasks, computer vision tasks, audio tasks and multi-modal tasks, are briefly presented. Finally, possible future research directions are suggested.
Deep representation learning for domain generalization with information bottleneck principle

Although deep neural networks have achieved superior performance on many classical tasks, they deteriorate in real applications due to the unpredictable distribution shift. Domain generalization (DG) focuses on improving the generalization ability of the predictive model in unseen domains by training on multiple available source domains. All these domains share the same categories but commonly obey different distributions. In this paper, we establish a new theoretical framework for domain generalization from the perspective of the information bottleneck (IB) principle, which links representation learning in DG with domain-invariant representation learning and maximizing feature entropy (MFE). Based on the theoretical framework, we provide a feasible solution by class-wise instance discrimination combined with inter-dimension decorrelation and intra-dimension uniformity to learn the desired representation for domain generalization, which achieves excellent performance on multiple datasets without knowing domain labels. Extensive experiments show that the proposed regularization rule (MFE) can improve invariance-based DG methods consistently. Moreover, as an extreme case of domain generalization, we also show that MFE is promising to improve adversarial robustness.
Obstacle Adaptive Smooth Path Planning for Mobile Data Collector in the Internet of Things

In the edge-based Internet of Things (IoT) era, wireless sensor networks (WSNs) are the prime source for data collection. In such WSNs, mobile edge nodes such as mobile sinks (MSs) are the superior means to collect sensed data by visiting rendezvous points (RPs). However, WSNs are often obstacle-ridden, which creates hurdles to the movement of the MSs. Most of the existing path planning works dealing with obstacles do not address optimal and smooth path construction. In other words, they have not considered a) optimizing the number of RPs and constructing a feasible path and b) smoothing the constructed path by considering sharp edges and convexity of the obstacle perimeter. In this paper, we address all such issues and develop an efficient scheme for determining an optimal number of RPs using a greedy approach to the set-cover problem and optimized path construction, both in polynomial time. Then, we apply the modified BUG2 algorithm to construct an obstacle-free path, which is then smoothed using the concept of the Bezier curve. Extensive simulations show the superiority of our proposed scheme over the existing algorithms in terms of energy consumption, latency, and so on.