Reviewerly/exHarmony huggingface dataset:
A recent survey on instance-dependent positive and unlabeled learning

Training with confident positive-labeled instances has received a lot of attention in Positive and Unlabeled (PU) learning tasks, and this is formally termed “Instance-Dependent PU learning”. In instance-dependent PU learning, whether a positive instance is labeled depends on its labeling confidence. In other words, it is assumed that not all positive instances have the same probability to be included by the positive set. Instead, the instances that are far from the potential decision boundary are with larger probability to be labeled than those that are close to the decision boundary. This setting has practical importance in many real-world applications such as medical diagnosis, outlier detection, object detection, etc. In this survey, we first present the preliminary knowledge of PU learning, and then review the representative instance-dependent PU learning settings and methods. After that, we thoroughly compare them with typical PU learning methods on various benchmark datasets and analyze their performances. Finally, we discuss the potential directions for future research.
Developing a Large Benchmark Corpus for Urdu Semantic Word Similarity

The semantic word similarity task aims to quantify the degree of similarity between a pair of words. In literature, efforts have been made to create standard evaluation resources to develop, evaluate, and compare various methods for semantic word similarity. The majority of these efforts focused on English and some other languages. However, the problem of semantic word similarity has not been thoroughly explored for South Asian languages, particularly Urdu. To fill this gap, this study presents a large benchmark corpus of 518 word pairs for the Urdu semantic word similarity task, which were manually annotated by 12 annotators. To demonstrate how our proposed corpus can be used for the development and evaluation of Urdu semantic word similarity systems, we applied two state-of-the-art methods: (1) a word embedding–based method and (2) a Sentence Transformer–based method. As another major contribution, we proposed a feature fusion method based on Sentence Transformers and word embedding methods. The best results were obtained using our proposed feature fusion method (the combination of best features of both methods) with a Pearson correlation score of 0.67. To foster research in Urdu (an under-resourced language), our proposed corpus will be free and publicly available for research purposes.
A Semantically Driven Hybrid Network for Unsupervised Entity Alignment

The major challenge in the task of entity alignment (EA) lies in the heterogeneity of the knowledge graph. The traditional solution to EA is to first map entities to the same space via knowledge embedding and then calculate the similarity between entities from different knowledge graphs. However, these methods mainly rely on manually labeled seeds of EA, which limits their applicability. Some researchers have begun using pseudo-labels rather than seeds for unsupervised EA. However, directly using pseudo-labels causes new problems, such as noise in the pseudo-labels. In this article, we propose a model called the Semantically Driven Hybrid Network (SDHN) to reduce the impact of noise in the pseudo-labels on the performance of EA models. The SDHN consists of two modules: a Teacher–Student Network (TSN) and a Rotation and Penalty (RAP) module. The TSN module reduces the impact of noise in two ways: (1) The TSN’s teacher network guides its student network to construct pseudo-labels based on semantic information instead of directly creating pseudo-labels. (2) It adaptively fuses semantic information into student networks to improve the final representation of entity embedding. Finally, the TSN enhances the performance of models of entity alignment via the RAP module. The results of experiments on multiple benchmark datasets showed that the SDHN outperforms state-of-the-art models.
Energy-efficient canonical Huffman decoders on many-core processor arrays and FPGAs

Data compression is essential to reduce high storage and communication costs for a wide range of systems and applications. Canonical Huffman coding plays a pivotal role for several compression standards. This paper presents bit-parallel static and dynamic canonical Huffman decoder implementations using an optimized lookup table approach on a fine-grain many-core processor array and an Intel FPGA. The decoder implementation results are compared with an Intel i7-4850HQ and a massively parallel Nvidia GT 750M GPU executing the corpus benchmarks: Calgary, Canterbury, Artificial, and Large. The many-core implementations achieve a scaled throughput per chip area that is 891× and 7× greater on average than the i7 and GT 750M respectively. Moreover, the many-core implementations result in a scaled energy efficiency (compressed bits decoded per energy) that is 149.5×, 3.9×, and 2.5× greater on average than the i7, GT 750M, and Intel FPGA respectively. In addition, the optimized lookup-table-based static canonical Huffman decoder on the Intel FPGA yields performance and energy efficiency improvements of 2.1× and 3.68× respectively on average compared to a dynamic canonical Huffman decoder at a 17% cost in compression ratio.
Simulation of a laser-driven three-level system by a noisy quantum computer

By adopting the variational quantum simulator method on noisy quantum computers, we simulate the temporal evolution of the population transfer in a laser-driven three-level system, representing N2+ suddenly exposed to an intense laser field. We have conducted experiments using an IBM Quantum computer. In order to mitigate the error caused by the execution of the Hadamard test circuits, the Clifford data regression method is adopted. The characteristic features in the temporal evolution of the population transfer, leading to the population inversion and the air lasing, have been well reproduced.
Some notes on disjunctive short sum: polychromatic nim

polychromatic nim is a version of the classic game nim, played with colored stones, in which each pile has stones of a single color, and the player who successfully extinguishes a color wins the game. This game is closely related to the concept of short rule, the ending condition that states that a disjunctive sum ends as soon as any one of the components ends. Here, we discuss that rule, namely when applied to impartial games, and prove that the Grundy-values of polychromatic nim present an arithmetic-periodic behavior.
An Illumination-Robust Face Recognition Approach Based on Convolutional Neural Network

In the last decade, several research papers have been published to overcome the limitation of uncontrolled environments in automatic face recognition. Despite that, the problem of illumination variation has not yet been completely resolved. This paper proposed an illumination-robust face recognition approach based on four blocks of the pre-trained VGG16 followed by an Average Polling layer. After extracting features, our approach used PCA to reduce dimensionality and LinearSVC in the classification task. Our approach has been evaluated on the Extended Yale B and the AR databases collected under illumination variation. Compared with some popular Deep CNN models trained from scratch and with Transfer Learning on both databases, our proposed approach proved its robustness and effectiveness against variation and poor illumination. The proposed approach has achieved an average accuracy of 99.32% and 99.79% on the Extended Yale B and AR databases, respectively. The accuracies mentioned above surpassed the state-of-the-art in the face recognition field on both databases.
Survey of the Arabic Machine Translation Corpora

Machine translation (henceforward referred to as MT) is one of the important areas of Natural language processing (NLP) that is necessary for cracking the language obstacle and easing inter-lingual communication. This paper sheds light on the approaches used in MT, available in the literature, to encourage researchers to study these techniques. In the last years, the neural approach is dominating the field of MT. Such a technique is based on datasets with a large number of parallel sentences, that, contrarily, Arabic MT is lacking such prestige. Thus, this paper summarizes the major Arabic MT corpora, with both sides the Standard Arabic and Dialectal Arabic, and discusses their characteristics, which we feel is a key concept in MT and may provide a better solution to these open challenges in Arabic MT.
Impact of Normalization and Data Augmentation in NER for Algerian Arabic Dialect

Today, social media incorporate a considerable volume and variety of textual data constituting a first source for several NLP applications to take advantage of it in order to better understand and apprehend social phenomena. For this purpose, several approaches have been applied to exploit this mass of data, among them the deep learning approach, which has demonstrated its performance against classical machine learning algorithms since 2006. However, this approach inherited the same problems as those of classical machine learning, namely the heterogeneity of the resources (most of the time, the data are not directly exploitable, they must be sorted, filtered and normalized) dedicated to learning, but also in most cases the available resources are reduced in size for some languages and dialects which affects the performance of the models in training. The aim of this paper is to study the impact of normalization and data augmentation on named entity recognition (NER) task on Algerian text, using Arabic pre-trained models. For training and evaluation, we built a corpus dedicated to the recognition of named entities, called DzNER, based on Facebook’s comments. To evaluate the models, we set up 4 scenarios, the first one without normalization and data augmentation, in this case, the ARBERT model outperformed the other models with an F1 score of 84.4%. The second scenario is to use normalization, which enabled the Dziribert model to get the highest F1 Score of 81.9%. The third scenario with data augmentation, where the Arabert v0.2 model yielded the best F1 score with 85.1%. The Arabert v0.2 model again obtained the best F1 Score with 86.2% in the last scenario combining normalization and data augmentation.
Auto-Diversified Ameliorated MultiPopulation-Based Ensemble Differential Evolution

Stagnation and premature convergence are considered the most known problems of differential evolution (DE). To address this issue, we propose a multipopulation differential evolution with an automatic re-diversification mechanism that consists of combining our previously proposed approach (AMPEDE) with a detection/re-diversification process. In the proposed AD-AMPEDE, the objective function values’ mean and the coefficient of variance are used to detect whether the population has been stagnant or has converged to probably a local optimum. After confirming the existence of stagnation and premature convergence, a re-diversification mechanism, with the strategy proposed for AEPD [4], is applied on the population. A set of 24 Black-Box Optimization Benchmarking (BBOB2009) functions is used to evaluate the AD-AMPEDE efficiency. Results display that AD-AMPEDE is very competitive and provides an excellent performance in dealing with some categories of optimization problems.
Knowledge Graph Completion based on Multi-task Learning and Extractive Text Summarization

Knowledge completion graph is an important technology to supplement knowledge graph and improve data quality. However, existing knowledge graph completion methods ignored the characteristics of triple's relation and introduced redundant entity description information. In order to improve the above problems, this paper proposed an ALBERT-KGC model. The key contexts were extracted from redundant entity descriptions by the extraction summarization technology. Then ALBERT was used as an encoder to reduce the number of model parameters, and the multi-task learning method was applied to fine-tune the model, effectively integrating entity and relation features. The results of linking prediction experiments on data sets WN18RR and FB15k-237 showed that the proposed method can improve the MeanRank (MR) index by 31 and 2, and the top 10 hit ratio ([email protected]) index by 1.2% and 5.8% compared with the traditional methods, verifying the validity of the model.
MUI-VB: Malicious URL Identification Model Combining VGG and Bi-LSTM

Internet fraud has become one of the issues with great influence and people's greatest concern, which seriously threatens citizens' property rights and interests. Intelligent analysis of websites through deep learning, artificial intelligence, and other technologies is of great value in combating illegal activities such as online fraud. In this paper, a malicious URL identification model combining VGG and Bi-LSTM is proposed. Firstly, the knowledge of URL data is extracted from three dimensions to form the input of the model; Secondly, Bi-LSTM is integrated into the VGG network to learn the bidirectional text information of URL, and the malicious URL identification model of multi-dimensional feature learning is further trained. This method does not need manual tagging and has a good recognition effect. Experimental results show that compared with the traditional malicious URL recognition model, the VGG model with Bi-LSTM can effectively improve the malicious URL recognition performance of the model, with the accuracy rate and recall rate reaching 96.84% and 96.34% respectively, which are higher than related methods. In addition, the multi-dimensional feature extraction of URL data also provides a certain degree of interpretability for neural networks, which has important reference significance for the subsequent research of malicious URL identification.
An end-to-end information fusion model for task-oriented dialogue systems

There are many aspects to consider when building an end-to-end task-oriented dialogue system, but recent studies have ignored the importance of dialogue history and textual representation. Although existing neural networks can effectively construct a linear relationship between dialogue history and knowledge base information, it is difficult to improve the dialogue quality. In this paper, we propose an end-to-end neural network model that reuses dialogue history information for task-oriented dialogue systems. We also reconstructed a large-scale word vector data set for use in NLP research. Our model integrates the implicit forgetting information and selection information of the dialogue history into the dialogue generation results through LSTM cell units, and uses Bert pre-trained word vectors for feature transformation to enhance entity representation capabilities. Finally, our model achieved a maximum increase of 2.4% in F1 value and a maximum 4% increase in Bleu value on the public data sets Incar and CamRest.
A method to generate a ground truth distributed network traffic dataset

Network traffic dataset is the basis for studying the properties of network traffic and training network traffic classification model. In order to solve the problems of Insufficient authenticity, inaccuracy, limited traffic size and privacy security risks of various network traffic datasets, a method to generate a truth distributed network traffic dataset is presented. This method obtains a true distributed network traffic dataset by collaborating with the server to complete the ground truth traffic collection, data processing, data labeling and other steps. Experiments show that the distributed network traffic dataset obtained by this method has the advantages of High authenticity of traffic data, accurate type labeling, scalable traffic size, and privacy security. The validity of the dataset is verified by model training and traffic classification based on the Federated Learning.
A Method of Chinese NER Based on BERT Model and Coarse-Grained Features

Named entity recognition (NER) is a fundamental task and an important aspect in the fields of information extraction, natural language understanding and retrieval systems. Currently, NER based on deep learning is better than traditional feature and kernel function-based approaches in the field of feature extraction depth and modeling accuracy. The traditional word-based feature approach tends to ignore the coarse-grained word features, and the recognition of named entities in Chinese requires a comprehensive consideration of word-level and word-level features. To address the above issues, the combination of the BERT pre-training model and the Lattice network structure integrates coarse-grained features and word features, and CRF is used to decode sequence labels. The BERT-Lattice-CRF model is proposed in this paper, we use the public data set Resume for testing. Then we can conclude that the F1 value of the model on the data set is significantly improved.
MMCN: Multi-Modal Co-attention Network for Medical Visual Question Answering

Medical visual question Answering (MedVQA) attempts to answer medical questions posed by a correlative medical image. Although it has vast potential in the medical domain, this technology is still difficult to apply in real-life. It has not been widely adopted because accurate answer prediction requires a refined understanding of medical images and question text. Existing methods directly use the whole image and the whole question for multi-modal fusion to predict the answer. However, for a question, important information only exists in a small part of the whole image and a few critical words in the question, and extra information may interfere with the answer prediction. To this end, we introduce an effective multi-modal co-attention network (MMCN) for learning essential words in the question and essential regions in the image. Each word and region is scored by the attention weighting method, which is used to indicate the importance of each word and region in the process of model reasoning. Experimental comparisons show that our MMCN is superior to the most advanced methods of the public RAD-VQA dataset.
Differentiate Visual Features with Guidance Signals for Video Captioning

The task of video captioning is to generate comprehensible and grammatically correct sentences which describe the main visual content of videos. Existing neural modules based methods improve the model interpretability by separately predicting words of different part-of-speech. However, the separation of different modules may lead to confusing semantics. In this work, a video captioning method referred to as Differentiate Visual Features with Guidance Signals (DVFGS) is proposed, which enhances the semantic consistency of the neural modules based method through guidance signals. This process is similar to the cell differentiation process, producing differences and having different effects on the whole. Extensive experiments performed on MSVD and MSR-VTT show that DVFGS pushes the limit of neural modules based video captioning methods forward.
A Guided Task and Obstacle Alert Robot System Based on TinyML and Augmented Reality

Currently, the robot system has been widely applied and rapidly developed. It applies to diverse fields such as service, patrol, life, etc. Most patrol robot systems are based on augmented reality and SLAM to complete guided navigation tasks. The AI system is embedded in the robot for objects detection during the navigation tasks. Since the traditional AI model is computationally intensive at system deployment, it requests special computing devices to support, such as GPUs and TPUs. However, edge devices have too limited memory to run traditional AI models, which significantly limits the application of edge devices. This paper proposed an embedded system based on TinyML approach, which combined an augmented reality robot system with a lightweight AI model so that it can be deployed on edge device or MCU. The goal of this system is to detect the objects during the navigation task and give warning to users to remove obstacle for continuously operating.
Multi-view Click-through Rate Prediction based on Multi-layer Deep Interest Network

With the continuous expansion of Internet coverage, the number of items on e-commerce platforms has exploded. The types of information faced by users are becoming more and more diverse, and so are their interests. How to effectively capture user interests and recommend interesting content to users is the main problem of click-through rate(CTR) prediction task. This paper proposes an interest model for multi-view CTR prediction: MDIN (Multi-layer Deep Interest Networks), which learns the representation of users’ complex interests through a multi-dimensional interest evolution network and a multi-dimensional interest extraction network. This paper evaluates the model approach on real datasets and compares it with multiple state-of-the-art models. Experimental results show that the proposed method outperforms other model methods in the CTR prediction task.
Dual-Modality Feature Extraction Network Based on Graph Attention for RGBT Tracking

The RGBT target tracking method has recently gained popularity owing to the complementarity of RGB images and thermal images information. Although numerous RGBT tracking methods have been proposed, effectively utilizing dual-modality information is still challenging. To solve this problem, we design a dual-modality feature extraction network to extract common and specific modality features. For specific modality features, we design two unique feature extraction networks to learn the independent dual-modality information respectively. For common modality features, we propose a common feature extraction network based on the graph attention method, which could learn the shared modality information of dual-modality images. According to experiments on the RGBT234 and LasHeR datasets, our suggested method performs sufficiently.
Prediction of User Ratings of Dianping Based on K-BERT Model

In the Internet era, people usually consider the ratings and reviews of stores on review platforms when choosing travel locations. Today, the mainstream rating scheme for total store scores is weighted by review scores, but this scoring system can be negatively affected by malicious scoring and uneven scoring. Problems such as incompleteness and other issues will affect the authenticity of the store's rating. To this end, this paper designs a K-BERT Dianping user rating prediction based on K-BERT model to reflect real review ratings. Compared with the traditional BERT pre-training model, the K-BERT model can solve knowledge-driven problems faster through knowledge graph injection. In this paper, a Dianping knowledge map is established. Through the steps of text preprocessing, text pre-training, and Dianping dataset fine-tuning, it is found that the accuracy rate of the K-BERT model in the Dianping rating classification is about 95%. By comparing the model with BERT, Logistic Regression , it is found that the predicted effect of the K-BERT model is significantly better than the above two models.