Reviewerly/exHarmony huggingface dataset:
Speaker Turn Modeling for Dialogue Act Classification

Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue.Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout the dialogue, therefore treating it no different than non-interactive written text.In this paper, we propose to integrate the turn changes in conversations among speakers when modeling DAs.Specifically, we learn conversation-invariant speaker turn embeddings to represent the speaker turns in a conversation; the learned speaker turn embeddings are then merged with the utterance embeddings for the downstream task of DA classification.With this simple yet effective mechanism, our model is able to capture the semantics from the dialogue content while accounting for different speaker turns in a conversation.Validation on three benchmark public datasets demonstrates superior performance of our model. 1
An Improved Extreme Learning Machine for Imbalanced Data Classification

In the field of machine learning, Extreme Learning Machine (ELM) has been widely used in classification and regression tasks. However, like many traditional machine learning algorithms, the classification results of ELM are often not good enough when facing imbalanced data. For this reason, we proposed an extreme learning machine algorithm with output weight adjustment called OWA-ELM, which can make the decision boundary of ELM move to majority classes, and improve the classification performance of imbalanced data. Specifically, in ELM, we add a reasonable increment <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> to the connection weights between hidden layer neurons and minority output neurons, so that the output value of minority output neuron increases. Finally, the classification accuracy of the minority class samples can be improved without significantly affecting the classification of the majority class samples. The performance of OWA-ELM was compared with ELM, WELM, CS-ELM and CCR-ELM. In the experiments on 22 data sets, the OWA-ELM algorithm has achieved 9 times optimal and 4 times suboptimal results on G-mean. In F-measure, 13 times optimal and one suboptimal results were obtained. Therefore, the OWA-ELM algorithm is effective to deal with imbalanced data classification.
Reputation-Driven Dynamic Node Consensus and Reliability Sharding Model in IoT Blockchain

The Internet of Things that links the cyber and physical worlds brings revolutionary changes to society, however, its security and efficiency problems have not been solved. The Consortium Blockchain + IoT is considered to be an effective solution. The IoT blockchain network’s demand for transaction processing speed is gradually increasing. The throughput problem of the blockchain needs to be solved urgently and the security problem of transaction processing that comes with it. To solve the above problems, this paper proposes a reputation-driven dynamic node security sharding consensus model (RDSCM) in the blockchain, which consists of two parts: a reputation-driven node to eliminate PBFT (RE-PBFT) and a reputation-driven node cross reconfiguration sharding scheme (NCRS). The RE-PBFT eliminates abnormal nodes in the consensus network and reduces the probability of abnormal nodes becoming master nodes. NCRS improves the blockchain throughput while ensuring sharding reliability. Finally, the experiment proves that RE-PBFT can identify abnormal nodes and remove them in a short time. NCRS can effectively guarantee the reliability of sharding, and the transaction processing efficiency has been greatly improved after sharding.
Iterative GNN-based Decoder for Question Generation

Natural question generation (QG) aims to generate questions from a passage, and generated questions are answered from the passage.Most models with state-of-the-art performance model the previously generated text at each decoding step.However, (1) they ignore the rich structure information that is hidden in the previously generated text.(2) they ignore the impact of copied words on the passage.We perceive that information in previously generated words serves as auxiliary information in subsequent generation.To address these problems, we design the Iterative Graph Network-based Decoder (IGND) to model the previous generation using a Graph Neural Network at each decoding step.Moreover, our graph model captures dependency relations in the passage that boost the generation.Experimental results demonstrate that our model outperforms the state-of-the-art models with sentence-level QG tasks on SQuAD and MARCO datasets.
Multi-modal Hate Speech Detection using Machine Learning

With the continuous growth of internet users and media content, it is very hard to track down hateful speech in audio and video. Converting video or audio into text does not detect hate speech accurately as human sometimes uses hateful words as humorous or pleasant in sense and also uses different voice tones or show different action in the video. The state-ofthe-art hate speech detection models were mostly developed on a single modality. In this research, a combined approach of multimodal system has been proposed to detect hate speech from video contents by extracting feature images, feature values extracted from the audio, text and used machine learning and Natural language processing.
Backtranslation in Neural Morphological Inflection

Backtranslation is a common technique for leveraging unlabeled data in low-resource scenarios in machine translation.The method is directly applicable to morphological inflection generation if unlabeled word forms are available.This paper evaluates the potential of backtranslation for morphological inflection using data from six languages with labeled data drawn from the SIGMORPHON shared task resource and unlabeled data from different sources.Our core finding is that backtranslation can offer modest improvements in lowresource scenarios, but only if the unlabeled data is very clean and has been filtered by the same annotation standards as the labeled data.
Resilient Dynamic Channel Access via Robust Deep Reinforcement Learning

As the applications of deep reinforcement learning (DRL) in wireless communications grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has started to draw increasing attention. In order to address such sensitivity and alleviate the resulting security concerns, we in this paper consider a victim user that performs DRL-based dynamic channel access, and an attacker that executes DRLbased jamming attacks to disrupt the victim. Hence, both the victim and attacker are DRL agents and can interact with each other, retrain their models, and adapt to opponents' policies. In this setting, we initially develop an adversarial jamming attack policy that aims at minimizing the accuracy of victim's decision making on dynamic channel access. Subsequently, we devise defense strategies against such an attacker, and propose three defense strategies, namely diversified defense with proportional-integral-derivative (PID) control, diversified defense with an imitation attacker, and defense via orthogonal policies. We design these strategies to maximize the attacked victim's accuracy and evaluate their performances.
Decoupled Pose and Similarity Based Graph Neural Network for Video Person Re-Identification

Significant development of video person re-identification has been witnessed in recent years with deep learning technologies. Due to the complexity of human pose changes and the similarity between different individuals, learning discriminative features is still a challenging part of the video person re-identification task. To get rid of the effects of pose misalignment while keep the similarity of human appearance, in this paper, we propose a Pose and Similarity based Graph Neural Network in a decoupled manner, which consists of three independent branches to emphasize the respective roles of pose, local similarity and global similarity in the final descriptions. Compared to traditional Convolutional Neural Networks which tend to output similar global features in the case of highly similar pedestrians, the developed Graph Neural Networks are able to explore local semantic relationships between body parts, resulting in more discriminative features. To further eliminate the pose variation, we incorporate human skeleton information for feature map segmentation. Specifically, we propose to take a tree structure as the pose-aware adjacency graph of blocks in a person frame, which reveals the inherent connections within a human body. Experimental results on four widely used datasets demonstrate the effectiveness of our method.
A Framework of Personality Cues for Conversational Agents

Conversational agents (CAs)-software systems emulating conversations with humans through natural language-reshape our communication environment.As CAs have been widely used for applications requiring human-like interactions, a key goal in information systems (IS) research and practice is to be able to create CAs that exhibit a particular personality.However, existing research on CA personality is scattered across different fields and researchers and practitioners face difficulty in understanding the current state of the art on the design of CA personality.To address this gap, we systematically analyze existing studies and develop a framework on how to imbue CAs with personality cues and how to organize the underlying range of expressive variation regarding the Big Five personality traits.Our framework contributes to IS research by providing an overview of CA personality cues in verbal and non-verbal language and supports practitioners in designing CAs with a particular personality.
Multimodal Imaging of Target Detection Algorithm under Artificial Intelligence in the Diagnosis of Early Breast Cancer

This study aimed to analyze the diagnostic value of multimodal images based on artificial intelligence target detection algorithms for early breast cancer, so as to provide help for clinical imaging examinations of breast cancer. This article combined residual block with inception block, constructed a new target detection algorithm to detect breast lumps, used deep convolutional neural network and ultrasound imaging in diagnosing benign and malignant breast lumps, took breast density grading with mammography, compared the convolutional neural network (CNN) algorithm with the proposed algorithm, and then applied the proposed algorithm to the diagnosis of 120 female patients with breast lumps. According to the results, accuracy rates of breast lump detection (94.76%), benign and malignant breast lumps diagnosis (98.22%), and breast grading (93.65%) with the algorithm applied in this study were significantly higher than those (75.67%, 87.23%, and 79.54%) with CNN algorithm, and the difference was statistically significant (P < 0.05); among 62 patients with malignant breast lumps of the 120 patients with breast lumps, 37 were patients with invasive ductal carcinoma, 8 with lobular carcinoma in situ, 16 with intraductal carcinoma, and 4 with mucinous carcinoma; among the remaining 58 patients with benign breast lumps, 28 were patients with fibrocystic breast disease, 17 with intraductal papilloma, 4 with breast hyperplasia, and 9 with adenopathy; the differences in shape, growth direction, edge, and internal echo of multimodal ultrasound imaging of patients with benign and malignant breast lumps had statistical significance (P < 0.05); the malignant constituent ratios of patients with breast density grades I to IV were 0%, 7.10%, 80.40%, and 100%, respectively. In short, the multimodal imaging diagnosis under the algorithm in this article was superior to CNN algorithm in all aspects; according to the judgment on benign and malignant breast lumps and breast density with multimodal imaging features, the higher the breast density, the higher the probability of breast cancer.
Guiding Labelling Effort for Efficient Learning With Georeferenced Images

We describe a novel semi-supervised learning method that reduces the labelling effort needed to train convolutional neural networks (CNNs) when processing georeferenced imagery. This allows deep learning CNNs to be trained on a per-dataset basis, which is useful in domains where there is limited learning transferability across datasets. The method identifies representative subsets of images from an unlabelled dataset based on the latent representation of a location guided autoencoder. We assess the method's sensitivities to design options using four different ground-truthed datasets of georeferenced environmental monitoring images, where these include various scenes in aerial and seafloor imagery. Efficiency gains are achieved for all the aerial and seafloor image datasets analysed in our experiments, demonstrating the benefit of the method across application domains. Compared to CNNs of the same architecture trained using conventional transfer and active learning, the method achieves equivalent accuracy with an order of magnitude fewer annotations, and 85 % of the accuracy of CNNs trained conventionally with approximately 10,000 human annotations using just 40 prioritised annotations. The biggest gains in efficiency are seen in datasets with unbalanced class distributions and rare classes that have a relatively small number of observations.
A Generalized Zero-Shot Quantization of Deep Convolutional Neural Networks Via Learned Weights Statistics

Quantizing the floating-point weights and activations of deep convolutional neural networks to fixed-point representation yields reduced memory footprints and inference time. Recently, efforts have been afoot towards zero-shot quantization that does not require original unlabelled training samples of a given task. These best-published works heavily rely on the learned batch normalization (BN) parameters to infer the range of the activations for quantization. In particular, these methods are built upon either empirical estimation framework or the data distillation approach, for computing the range of the activations. However, the performance of such schemes severely degrades when presented with a network that does not accommodate BN layers. In this line of thought, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generalized zero-shot quantization</i> (GZSQ) framework that neither requires original data nor relies on BN layer statistics. We have utilized the data distillation approach and leveraged only the pre-trained weights of the model to estimate enriched data for range calibration of the activations. To the best of our knowledge, this is the first work that utilizes the distribution of the pre-trained weights to assist the process of zero-shot quantization. The proposed scheme has significantly outperformed the existing zero-shot works, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , an improvement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 33% in classification accuracy for MobileNetV2 and several other models that are w & w/o BN layers, for a variety of tasks. We have also demonstrated the efficacy of the proposed work across multiple open-source quantization frameworks. Importantly, our work is the first attempt towards the post-training zero-shot quantization of futuristic unnormalized deep neural networks.
I-CNet: leveraging Involution and Convolution for Image Classification

Convolution is widely adapted in deep learning models on image classification tasks for extracting hidden spatial-domain representations. However, as convolution is channel-specific, the potential cross-channel correlations in images are often neglected. This paper proposes a novel model, namely I-CNet, which leverages involution and convolution for improving the accuracy of image classification tasks, by extracting feature representations on both channel domain and spatial domain. The proposed I-CNet has been experimented on three image classification datasets. The experimental results show that involution component employed in I-CNet model can effectively represent cross-channel features in images and I-CNet is superior to other comparatives with higher classification accuracy achieved on all the three datasets.
Application of the Waterfall Method on a Web-Based Job Training Management Information System at Trunojoyo University Madura

With the development of information technology, practical work at Trunojoyo University Madura needs improvement in applied management. Practical work is a mandatory activity for Information Systems undergraduate students which aims to test and apply theory as well as scientific proof of the courses that have been obtained. In the practical work management process is still done manually, it will take up a lot of time and frequent errors. Therefore, the Job Training Management Information System will be implemented using the waterfall model, the PHP programming language with Codeingniter as its framework. The waterfall model provides a sequential approach to software life phases starting from analysis, design, code implementation, testing, and maintenance. This research produces the results of the analysis of hardware and software requirements. The design produces Use case diagrams and Entity Diagrams. Testing produces a system by testing with black box testing with valid test results. This research produces a management system that is used to manage data related to practical work activities.
A Comparison of Hybrid and End-to-End ASR Systems for the IberSpeech-RTVE 2020 Speech-to-Text Transcription Challenge

This paper describes a comparison between hybrid and end-to-end Automatic Speech Recognition (ASR) systems, which were evaluated on the IberSpeech-RTVE 2020 Speech-to-Text Transcription Challenge. Deep Neural Networks (DNNs) are becoming the most promising technology for ASR at present. In the last few years, traditional hybrid models have been evaluated and compared to other end-to-end ASR systems in terms of accuracy and efficiency. We contribute two different approaches: a hybrid ASR system based on a DNN-HMM and two state-of-the-art end-to-end ASR systems, based on Lattice-Free Maximum Mutual Information (LF-MMI). To address the high difficulty in the speech-to-text transcription of recordings with different speaking styles and acoustic conditions from TV studios to live recordings, data augmentation and Domain Adversarial Training (DAT) techniques were studied. Multi-condition data augmentation applied to our hybrid DNN-HMM demonstrated WER improvements in noisy scenarios (about 10% relatively). In contrast, the results obtained using an end-to-end PyChain-based ASR system were far from our expectations. Nevertheless, we found that when including DAT techniques, a relative WER improvement of 2.87% was obtained as compared to the PyChain-based system.
Match or Mismatch? How Matching Personality and Gender between Voice Assistants and Users Affects Trust in Voice Commerce

Despite the ubiquity of voice assistants (VAs), they see limited adoption in the form of voice commerce, an online sales channel using natural language.A key barrier to the widespread use of voice commerce is the lack of user trust.To address this problem, we draw on similarity-attraction theory to investigate how trust is affected when VAs match the user's personality and gender.We conducted a scenario-based experiment (N = 380) with four VAs designed to have different personalities and genders by customizing only the auditory cues in their voices.The results indicate that a personality match increases trust, while the effect of a gender match on trust is non-significant.Our findings contribute to research by demonstrating that some types of matches between VAs and users are more effective than others.Moreover, we reveal that it is important for practitioners to consider auditory cues when designing VAs for voice commerce.
Statistical H.264 Double Compression Detection Method Based on DCT Coefficients

With the 2019 Coronavirus pandemic, we have seen an increasing use of remote technologies such has remote identity verification.The authentication of the user identity is often performed through a biometric matching of a selfie and a video of an official identity document.In such a scenario, it is essential to verify the integrity of both the selfie and the video.In this article, we propose a method to detect double video compression in order to verify the video integrity.We will focus on the H.264 compression which is one of the mandatory video codecs in the WebRTC Requests For Comments.H.264 uses an integer approximation of the Discrete Cosine Transform (DCT).Our method focuses on the DCT coefficients to detect a double compression.The coefficients roughly follow a Laplacian distribution, we will show that the distribution parameters vary with respect to the quantisation parameter used to compress the video.We thus propose a statistical hypothesis test to determine whether or not a video has been compressed twice.
Augmented Reality dalam Media Pembelajaran Tata Cara Berwudhu dan Tayamum

In Islamic religious education, especially in the procedures for purification where ablution and tayammum are very important because these are mandatory requirements in carrying out prayers. Often the procedures for ablution and tayammum are not understood properly and correctly because the process of delivering the material is still using conventional methods with media in the form of books that are less interesting and interactive. The purpose of making an application for purification procedures using Augmented Reality as a visual learning medium that can be understood by users, namely early childhood and emphasizes the importance of ablution and tayammum in Islam. The Multimedia Development Life Cycle method is used in making this application with six stages carried out starting from concept generation, design, material collection, manufacture, alpha and beta testing and the last stage in the form of distribution of applications that have been built. The results of this study can be used as an alternative learning media with interactive features using augmented reality which discusses how to perform ablution and tayammum properly and correctly in worship according to Islamic law.
Citizenship and COVID-19: Syndemic Effects

Abstract This article begins the task of outlining the impacts of the COVID-19 pandemic in relation to matters of citizenship, using what is termed a “syndemic analysis.” This type of analysis places both the pandemic and citizenship in their wider contexts. The synergistic or intersectional thinking encouraged by the characterization of the pandemic as a syndemic, which links together health, socio-economic issues, and political questions, is useful for highlighting how much more vulnerable to many of the negative impacts of the pandemic in the sphere of citizenship are those who are also more vulnerable both to catching and suffering more seriously from the virus and to experiencing negatively the externalities of the measures taken to restrict social contact by shutting down economies. While the scope of the review is relatively broad and encompasses many different domains of “pandemic life,” what emerges from the analysis are important insights into how many of the impacts of the pandemic in fact operate at the intersection of citizenship and constitutional law and thus play out in the form of changes in relation to constitutional citizenship, both as ideal and as practice. The article takes an important step towards developing the use of constitutional citizenship as a framing device for understanding citizenship as putative full membership in a given society.