Reviewerly/exHarmony huggingface dataset:
UFNRec: Utilizing False Negative Samples for Sequential Recommendation

Sequential recommendation models are primarily optimized to distinguish positive samples from negative ones during training. Thus, negative instances sampled from enormous unlabeled data are essential in learning the evolving user preferences through historical records. Except for randomly sampling negative samples from a uniformly distributed subset, many delicate methods have been proposed to mine negative samples with high quality. However, due to the inherent randomness of negative sampling, false negatives are inevitably collected in model training. Current strategies mainly focus on removing such false negatives, which leads to overlooking potential user interests, lack of recommendation diversity, less model robustness, and suffering from exposure bias. To this end, we propose a novel method that can Utilize False Negative samples for sequential Recommendation (UFNRec), which thoroughly explores the leverage of false negatives. We first devise a simple strategy to extract false negatives from true negatives and directly reverse the labels of false negatives. To avoid extra noise from reversed samples, we restrict false negatives in the output space by an EMA operation and a consistency regularization loss. To the best of our knowledge, this is the first work to utilize false negatives instead of simply removing them for sequential recommendation. Both offline and online experiment results demonstrate that UFNRec can effectively draw information from false negatives and further improve the performance of SOTA models. Recently, we have deployed UFNRec on real-world recommendation servings. The code is available at https://github.com/UFNRec-code/UFNRec.KeywordsSequential RecommendationNegative SamplingConsistency Training
A Single Image Dehazing Method Based on End-to-End CPAD-Net Network in Deep Learning Environment

To address the issues of blurred details and distortion of color in the images recovered by the original AOD-Net dehazing method, this paper proposes a CPAD-Net dehazing network model based on attention mechanism and dense residual blocks. The network is improved on the basis of AOD-Net, which can reduce the errors arising from the separately determined transmittance and atmospheric light values. A new dense residual block structure is designed to replace the traditional convolution method, which effectively improves the detail processing capability and the representation ability of the network model for image feature information. On this basis, the attention module determines how to learn the weights according to the feature importance of distinct channels and distinct pixels, and then obtain the recovery of images in terms of color and texture. The experiments showed that the dehazing efficiency of our method are richer in texture detail information and more natural in color recovery. Compared with other algorithms, the PSNR and SSIM indexes of our method are considerably superior to those listed algorithms, which definitively demonstrates that the dehazing effect of our method is more effective, and the recovered images are more realistic and natural.
Who evaluates the evaluators? On automatic metrics for assessing AI-based offensive code generators

AI-based code generators are an emerging solution for automatically writing programs starting from descriptions in natural language, by using deep neural networks (Neural Machine Translation, NMT). In particular, code generators have been used for ethical hacking and offensive security testing by generating proof-of-concept attacks. Unfortunately, the evaluation of code generators still faces several issues. The current practice uses output similarity metrics, i.e., automatic metrics that compute the textual similarity of generated code with ground-truth references. However, it is not clear what metric to use, and which metric is most suitable for specific contexts. This work analyzes a large set of output similarity metrics on offensive code generators. We apply the metrics on two state-of-the-art NMT models using two datasets containing offensive assembly and Python code with their descriptions in the English language. We compare the estimates from the automatic metrics with human evaluation and provide practical insights into their strengths and limitations.
Shifted Chunk Encoder for Transformer Based Streaming End-to-End ASR

Currently, there are mainly three kinds of Transformer encoder based streaming End to End (E2E) Automatic Speech Recognition (ASR) approaches, namely time-restricted methods, chunk-wise methods, and memory-based methods. Generally, all of them have limitations in aspects of linear computational complexity, global context modeling, and parallel training. In this work, we aim to build a model to take all these three advantages for streaming Transformer ASR. Particularly, we propose a shifted chunk mechanism for the chunk-wise Transformer which provides cross-chunk connections between chunks. Therefore, the global context modeling ability of chunk-wise models can be significantly enhanced while all the original merits inherited. We integrate this scheme with the chunk-wise Transformer and Conformer, and identify them as SChunk-Transformer and SChunk-Conformer, respectively. Experiments on AISHELL-1 show that the SChunk-Transformer and SChunk-Conformer can respectively achieve CER 6.43% and 5.77%. And the linear complexity makes them possible to train with large batches and infer more efficiently. Our models can significantly outperform their conventional chunk-wise counterparts, while being competitive, with only 0.22 absolute CER drop, when compared with U2 which has quadratic complexity. A better CER can be achieved if compared with existing chunk-wise or memory-based methods, such as HS-DACS and MMA. Code is released. (see https://github.com/wangfangyuan/SChunk-Encoder .).
Disentangled Contrastive Learning for Cross-Domain Recommendation

Cross-Domain Recommendation (CDR) has been proved helpful in dealing with two bottlenecks in recommendation scenarios: data sparsity and cold start. Recent research reveals that identifying domain-invariant and domain-specific features behind interactions aids in generating comprehensive user and item representations. However, we argue that existing methods fail to separate domain-invariant and domain-specific representations from each other, which may contain noise and redundancy when treating domain-invariant representations as shared information across domains and harm recommendation performance. In this paper, we propose a novel Disentangled Contrastive Learning for Cross-Domain Recommendation framework (DCCDR) to disentangle domain-invariant and domain-specific representations to make them more informative. Specifically, we propose a separate representation generation component to generate separate domain-invariant and domain-specific representations for each domain. Next, We enrich the representations through multi-order collaborative information with GNNs. Moreover, we design a mutual-information-based contrastive learning objective to produce additional supervision signals for disentanglement and enhance the informativeness of disentangled representations by reducing noise and redundancy. Extensive experiments on two real-world datasets show that our proposed DCCDR model outperforms state-of-the-art single-domain and cross-domain recommendation approaches.
Potential Implementation of Android-Based Interactive Multimedia for Student Learning Activities

The development of technology in the field of 21st century education has become the main complement in the teaching and learning process. Various innovations were created to support learning activities such as the use of android technology. The emergence of COVID-19 makes interactive multimedia based on Android very important to help students and teachers achieve maximum learning. The purpose of this study was to determine students' perceptions of the use of Android-based interactive multimedia in learning mathematics during the covid-19 period. This study used a survey research method using a questionnaire which involved 53 randomly selected fifth grade elementary school students. The data collection technique was carried out using a questionnaire which was distributed to a sample of the fifth grade elementary school student population. From the results of the study, it showed that 91% of students had never used interactive multimedia based on Android while 9% had used it. Data on student perceptions of the use of media during online learning shows that 85% of students agree to adopt interactive multimedia, 9% of students agree to adopt audio-visual media, and 6% agree to adopt visual media. The results of this research succeeded in identifying that most students had never used interactive multimedia during learning activities, even though in fact and interest, it turned out that students were willing to carry out the learning process using interactive multimedia, as evidenced by the results of a survey conducted regarding the types of media they agreed to adopt. The results of this research can be used as a basis for developing an instructional product in an interactive multimedia format.
The Postprint Pledge – Toward a Culture of Researcher‐Driven Initiatives: A Commentary on “(Why) Are Open Research Practices the Future for the Study of Language Learning?”

Language LearningEarly View OPEN PEER COMMENTARY The Postprint Pledge – Toward a Culture of Researcher-Driven Initiatives: A Commentary on “(Why) Are Open Research Practices the Future for the Study of Language Learning?” This article relates to: (Why) Are Open Research Practices the Future for the Study of Language Learning? Emma Marsden, Kara Morgan-Short, Language Learning First Published online: April 17, 2023 Ali H. Al-Hoorie, Corresponding Author Ali H. Al-Hoorie [email protected] orcid.org/0000-0003-3810-5978 Royal Commission for Jubail and Yanbu, Saudi Arabia Correspondence concerning this article should be addressed to Ali H. Al-Hoorie, Royal Commission for Jubail and Yanbu, PO Box 10099, Jubail Industrial City, Saudi Arabia. Email: [email protected].Search for more papers by this authorPhil Hiver, Phil Hiver orcid.org/0000-0002-2004-7960 Florida State UniversitySearch for more papers by this author Ali H. Al-Hoorie, Corresponding Author Ali H. Al-Hoorie [email protected] orcid.org/0000-0003-3810-5978 Royal Commission for Jubail and Yanbu, Saudi Arabia Correspondence concerning this article should be addressed to Ali H. Al-Hoorie, Royal Commission for Jubail and Yanbu, PO Box 10099, Jubail Industrial City, Saudi Arabia. Email: [email protected].Search for more papers by this authorPhil Hiver, Phil Hiver orcid.org/0000-0002-2004-7960 Florida State UniversitySearch for more papers by this author First published: 17 April 2023 https://doi.org/10.1111/lang.12577 CRediT author statement – Ali H. Al–Hoorie: conceptualization (lead); investigation (equal); writing – original draft preparation (equal); writing – review & editing (equal). Phil Hiver: conceptualization (supporting); investigation (equal); writing – original draft preparation (equal); writing – review & editing (equal). The handling editor for this article was Pavel Trofimovich. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
The significance of the convolutional deep learning model in the intelligent collaborative correction of English writing

To ensure the normal operation of English composition grammar correction and avoid inaccurate detection caused by faults, it is of great significance to detect abnormal working conditions in time and diagnose them accurately. Aiming at the complexity of grammar correction, this paper proposes a PLSTM-CNN model for fault detection in the grammar correction process. The model effectively combines the global feature extraction ability of LSTM for time series data and the ability of the CNN model to extract local features, which reduces the loss of feature information and achieves a higher fault detection rate. A one-dimensional dense CNN is used as the main body of the CNN, and the LSTM network is sensitive to changes in sequence information to avoid model overfitting while building a deeper network. The maximum mutual information coefficient (MMIC) data preprocessing method is adopted to improve the local correlation of the data and improve the efficiency of the PLSTM-CNN model to detect faults from different initial conditions. The research results show that the parallel PLSTM-CNN has better prediction performance than the serial PLSTM-CNN, and its FDR and FPR are 90.5% and 0.051, respectively. It shows that the use of convolutional deep learning models for the prediction of writing grammar correction faults has strong application prospects.
A novel approach for automatic segmentation of prostate and its lesion regions on magnetic resonance imaging

Objective To develop an accurate and automatic segmentation model based on convolution neural network to segment the prostate and its lesion regions. Methods Of all 180 subjects, 122 healthy individuals and 58 patients with prostate cancer were included. For each subject, all slices of the prostate were comprised in the DWIs. A novel DCNN is proposed to automatically segment the prostate and its lesion regions. This model is inspired by the U-Net model with the encoding-decoding path as the backbone, importing dense block, attention mechanism techniques, and group norm-Atrous Spatial Pyramidal Pooling. Data augmentation was used to avoid overfitting in training. In the experimental phase, the data set was randomly divided into a training (70%), testing set (30%). four-fold cross-validation methods were used to obtain results for each metric. Results The proposed model achieved in terms of Iou, Dice score, accuracy, sensitivity, 95% Hausdorff Distance, 86.82%,93.90%, 94.11%, 93.8%,7.84 for the prostate, 79.2%, 89.51%, 88.43%,89.31%,8.39 for lesion region in segmentation. Compared to the state-of-the-art models, FCN, U-Net, U-Net++, and ResU-Net, the segmentation model achieved more promising results. Conclusion The proposed model yielded excellent performance in accurate and automatic segmentation of the prostate and lesion regions, revealing that the novel deep convolutional neural network could be used in clinical disease treatment and diagnosis.
The Cambridge Handbook of Computational Cognitive Sciences

The Cambridge Handbook of Computational Cognitive Sciences is a comprehensive reference for this rapidly developing and highly interdisciplinary field. Written with both newcomers and experts in mind, it provides an accessible introduction of paradigms, methodologies, approaches, and models, with ample detail and illustrated by examples. It should appeal to researchers and students working within the computational cognitive sciences, as well as those working in adjacent fields including philosophy, psychology, linguistics, anthropology, education, neuroscience, artificial intelligence, computer science, and more.
Blockchain Data Structures and Integrated Adaptive Learning: Features and Futures

Artificial Intelligence (AI) is gaining lots of interest across multiple domains, such as Consumer Electronics, the Internet of Things (IoT), Blockchain, Edge Computing, Remote Sensing, etc. Bringing adaptiveness to AI-based learning makes the overall system autonomous and robust. In parallel, Blockchain is one of the vital solutions of current age computing to make the end-to-end process decentralize. It is unavoidable to integrate AI with Blockchain when the system designing focuses on decentralized approaches, such as decentralized storage, security, and processing. Blockchain is the only solution in the current age to make the end-to-end system decentralized. This article gives a close view of Blockchain properties, data structures, IoT use cases, AI integration, and the potential future aspects.
Stack-Aware Hyperproperties

Abstract A hyperproperty relates executions of a program and is used to formalize security objectives such as confidentiality, non-interference, privacy, and anonymity. Formally, a hyperproperty is a collection of allowable sets of executions. A program violates a hyperproperty if the set of its executions is not in the collection specified by the hyperproperty. The logic HyperCTL* has been proposed in the literature to formally specify and verify hyperproperties. The problem of checking whether a finite-state program satisfies a HyperCTL* formula is known to be decidable. However, the problem turns out to be undecidable for procedural (recursive) programs. Surprisingly, we show that decidability can be restored if we consider restricted classes of hyperproperties, namely those that relate only those executions of a program which have the same call-stack access pattern. We call such hyperproperties, stack-aware hyperproperties. Our decision procedure can be used as a proof method for establishing security objectives such as noninference for recursive programs, and also for refuting security objectives such as observational determinism. Further, if the call stack size is observable to the attacker, the decision procedure provides exact verification.
Multimodal medical tensor fusion network-based DL framework for abnormality prediction from the radiology CXRs and clinical text reports

Pulmonary disease is a commonly occurring abnormality throughout this world. The pulmonary diseases include Tuberculosis, Pneumothorax, Cardiomegaly, Pulmonary atelectasis, Pneumonia, etc. A timely prognosis of pulmonary disease is essential. Increasing progress in Deep Learning (DL) techniques has significantly impacted and contributed to the medical domain, specifically in leveraging medical imaging for analysis, prognosis, and therapeutic decisions for clinicians. Many contemporary DL strategies for radiology focus on a single modality of data utilizing imaging features without considering the clinical context that provides more valuable complementary information for clinically consistent prognostic decisions. Also, the selection of the best data fusion strategy is crucial when performing Machine Learning (ML) or DL operation on multimodal heterogeneous data. We investigated multimodal medical fusion strategies leveraging DL techniques to predict pulmonary abnormality from the heterogeneous radiology Chest X-Rays (CXRs) and clinical text reports. In this research, we have proposed two effective unimodal and multimodal subnetworks to predict pulmonary abnormality from the CXR and clinical reports. We have conducted a comprehensive analysis and compared the performance of unimodal and multimodal models. The proposed models were applied to standard augmented data and the synthetic data generated to check the model's ability to predict from the new and unseen data. The proposed models were thoroughly assessed and examined against the publicly available Indiana university dataset and the data collected from the private medical hospital. The proposed multimodal models have given superior results compared to the unimodal models.
MBFQuant: A Multiplier-Bitwidth-Fixed, Mixed-Precision Quantization Method for Mobile CNN-Based Applications

Deploying Convolutional Neural Network (CNN)-based applications to mobile platforms can be challenging due to the conflict between the restricted computing capacity of mobile devices and the heavy computational overhead of running a CNN. Network quantization is a promising way of alleviating this problem. However, network quantization can result in accuracy degradation and this is especially the case with the compact CNN architectures that are designed for mobile applications. This paper presents a novel and efficient mixed-precision quantization pipeline, called MBFQuant. It redefines the design space for mixed-precision quantization by keeping the bitwidth of the multiplier fixed, unlike other existing methods, because we have found that the quantized model can maintain almost the same running efficiency, so long as the sum of the quantization bitwidth of the weight and the input activation of a layer is a constant. To maximize the accuracy of a quantized CNN model, we have developed a Simulated Annealing (SA)-based optimizer that can automatically explore the design space, and rapidly find the optimal bitwidth assignment. Comprehensive evaluations applying ten CNN architectures to four datasets have served to demonstrate that MBFQuant can achieve improvements in accuracy of up to 19.34% for image classification and 1.12% for object detection, with respect to a corresponding uniform bitwidth quantized model.
UMP-MG: A Uni-directed Message-Passing Multi-label Generation Model for Hierarchical Text Classification

Abstract Hierarchical Text Classification (HTC) is a formidable task which involves classifying textual descriptions into a taxonomic hierarchy. Existing methods, however, have difficulty in adequately modeling the hierarchical label structures, because they tend to focus on employing graph embedding methods to encode the hierarchical structure while disregarding the fact that the HTC labels are rooted in a tree structure. This is significant because, unlike a graph, the tree structure inherently has a directive that ordains information flow from one node to another—a critical factor when applying graph embedding to the HTC task. But in the graph structure, message-passing is undirected, which will lead to the imbalance of message transmission between nodes when applied to HTC. To this end, we propose a unidirectional message-passing multi-label generation model for HTC, referred to as UMP-MG. Instead of viewing HTC as a classification problem as previous methods have done, this novel approach conceptualizes it as a sequence generation task, introducing prior hierarchical information during the decoding process. This further enables the blocking of information flow in one direction to ensure that the graph embedding method is better suited for the HTC task and thus resulted in the enhanced tree structure representation. Results obtained through experimentation on both the public WOS dataset and an E-commerce user intent classification dataset demonstrate that our proposed model can achieve superlative results.
Research on Model of Teacher Education Resource Sharing Platform Based on Consortium Blockchain

Education is the foundation of a country's scientific and technological development. Improving the comprehensive quality of teachers through education is crucial to the cultivation of high-quality talents. Educational resource sharing can provide more learning resources and support organizations for teachers' education. Thus, we propose a model of the teacher education resource sharing platform based on a consortium blockchain. The characteristics such as identity authentication, secured information, traceability, and automatic execution of transactions by smart contracts in the blockchain protect resource property rights, improve information security, enhance trust between users, and construct the teacher education resource sharing platform.