Reviewerly/exHarmony huggingface dataset:
Study of a lifelong learning scenario for question answering

Question Answering (QA) systems have witnessed a significant advance in the last years due to the development of neural architectures employing pre-trained large models like BERT. However, once the QA model is fine-tuned for a task (e.g., a particular type of questions over a particular domain), system performance drops when new tasks are added along time, (e.g., new types of questions or new domains). Therefore, the system requires a retraining but, since the data distribution has shifted away from the previous learning, performance over previous tasks drops significantly. Hence, we need strategies to make our systems resistant to the passage of time. Lifelong Learning (LL) aims to study how systems can take advantage of the previous learning and the knowledge acquired to maintain or improve performance over time. In this article, we explore a scenario where the same LL based QA system suffers along time several shifts in the data distribution, represented as the addition of new different QA datasets. In this setup, the following research questions arise: (i) How LL based QA systems can benefit from previously learned tasks? (ii) Is there any strategy general enough to maintain or improve the performance over time when new tasks are added? and finally, (iii) How to detect a lack of knowledge that impedes the answering of questions and must trigger a new learning process? To answer these questions, we systematically try all possible training sequences over three well known QA datasets. Our results show how the learning of a new dataset is sensitive to previous training sequences and that we can find a strategy general enough to avoid the combinatorial explosion of testing all possible training sequences. Thus, when a new dataset is added to the system, the best way to retrain the system without dropping performance over the previous datasets is to randomly merge the new training material with the previous one.
Research on reasoning method of multi-relational knowledge graph based on translation

Abstract In the knowledge reasoning filed, algorithms based on translation have achieved great results in recent years. However, when translation-based models fit multi-map attribute relations, different relations will compete for the same embedding vector. At the same time, many knowledge graphs can automatically extract new triples from the Internet now. This further reduces the frequency of occurrence of a single relation in the knowledge graph, which creates more difficulty for translation-based models. Thus, the TransE_rel model is proposed by relaxing the constraints on relation vectors in the low-dimensional space. This model focuses more on relation reasoning and, when combined with other models, can also be used for entity reasoning. We conduct relation prediction and entity prediction on benchmark dataset and one dataset extracted by ourselves. By comparing with recent work, experimental results show that our method has achieved better performance on two tasks especially on relation prediction.
Visual relation detection based on pyramidal convolution and gated recurrent unit

Abstract Visual relationship detection, not only needs to identify the targets in the image and their position, but also to identify the interrelationship between the targets, is a comprehensive task including object detection, positioning, image classification. In this paper, pyramid convolutional networks are embedded in the image feature extraction module, and three convolutional kernels of different scales and depths are used to increase the sensing field of the network and ensure multi-scale feature extraction. At the same time, a recurrent neural network is introduced, which combines the background information in the image to improve the accuracy of semantic relationship detection.
Unknown network attack detection method based on reinforcement zero-shot learning

Abstract With the increasing growth of zero-day attacks, traditional machine learning-based network intrusion detection systems (NIDS) are difficult to cope with a large amount of unknown network attacks without labeled data. To this end, this paper proposes a new unknown network attack detection method, which combines zero-shot learning algorithm with reinforcement learning algorithm. First, the feature vector in traffic data and the semantic vector in threat intelligence are encoded in the hidden space by variational autoencoder, so that the two modalities are matched in the hidden space, and then the hidden features of known class and unknown class are used to input into the training classifier to get the predicted of the labeling results, and then use asynchronous advantage actor-critic based algorithm to learn and correct the attack results that are misclassified by the variational autoencoder to improve the accuracy of detection. Experiments prove that the proposed method in this paper achieves more than 95% accuracy on four unknown network attack classes that are difficult to identify, namely MAILBOMB, U2R, POD, and SNAPATTACK, which illustrates the effectiveness and feasibility of the method.
Improved FairMOT multi-target tracking method for complex scenes

Abstract At present, the popularization of augmented reality technology on personal terminals is no longer hindered by inherent computing devices due to the substantial improvement in the performance of intelligent terminal processors. However, in the practical application of augmented reality technology, there are still various external disturbances in the real environment, such as perspective changes, motion poses and other potential variables, so it will cause unstable target detection, target registration distortion, virtual and real space superposition of varying degrees of severity. Using TBD strategy as the basis of multi-target tracking technology can further improve the efficiency of multi-target tracking method if it is effectively combined with neural network algorithm. Based on the improved FairMOT neural network multi-target tracking algorithm, this paper mainly studies the specific application of deep learning algorithms in the field of multi-target tracking. Experiments have shown that the improved multi-scale feature fusion network and the optimized loss function of the FairMOT tracking algorithm have good real-time performance and high accuracy for multi-category target detection. The method of stable tracking of multi-category targets is completed in the scene of this paper, which improves the tracking performance, and finally achieves stable tracking through neural network learning and training.
Research on Named Entity Recognition Based on Model Fusion

Abstract In view of the defects of using deep learning method to identify named entities, which can’t represent polysemy and doesn’t contain context information, this paper proposes a new fusion model, which organically combines the bidirectional gated recurring neural network (BiGRU) and the dynamic graph edge evolutionary network (DGCNN) which are improved with attention mechanism. The time sequence features of the vector are captured by the improved BiGRU-ATT, and the local spatial features of the vector are enriched by DGCNN. Finally, fused output results are corrected and output by the constraint rules of CRF model, and the global optimal sequence is obtained by calculating the probability. The experimental results demonstrate that the proposed model is improved compared with the single model, which further highlights the superiority of the proposed fusion model.
Bi-LSTM-attention Based on ACNN Model for Disfluency Detection

Abstract Disfluencies are self-corrections in spontaneous speech, including filled pauses, repetitions, repairs and false starts. The task of disfluency detection is to identify these disfluencies phenomena in spoken speech and make them consistent with written text. Recent research has applied machine learning and deep learning approaches to disfluency recognition and classification. In addition, long distance dependence is one of the core issues in disfluency detection. In order to solve this problem, most existing approaches had combined plenty of hand-crafted features and words as input. However, hand-crafted model features need lots of time and energy. Some studies reduced the dependence on hand-crafted features, but they ignored the dependence between long sentences. In this article, we consider disfluency detection as a problem of sequence labeling, then apply Bi-LSTM and attention mechanisms to disfluency detection. In particular, on the basis of obtaining the dependency of rough copy by Auto-correlational neural network (ACNN), we improve the ACNN model to handle the long-term dependency, so that we can better capture dependency between words and words. In other words, our method can not only find the rough copy relationship between sentences without additional hand-crafted features, but also obtain the dependency relationship between long sentences. Experiments on commonly used English Switchboard test sets show that our approach achieves good performance compared to previous models that used texts only without other hand-crafted features.
Next basket recommendation based on graph attention network and transformer

Abstract To solve the problem that when using recurrent neural network will forget the previously learned basket information and cannot reflect the user’s real purchase intention. A next basket recommendation based on graph attention network (GAT) and transformer model is proposed. Construct the item-basket relationship graph and use GAT to learn the basket interactive characteristics, then model the user interest representation, and finally obtain the item probability distribution through deep neural network. The experimental results on two real world datasets show that the proposed model outperforms state-of-the-art existing basket recommendation models. Through ablation experiments and hyperparameter experiments, the effectiveness of each module and the influence of model parameters are proved.
HE-HMTC: A hybrid embedding-based text representation for Hierarchical multi-label text classification

Hierarchical multi-label text classification (HMTC) has become rather challenging when it requires handling large sets of closely related categories. We present a novel software that provides a hybrid embedding-based text representation for HMTC, shortened as HE-HMTC. It made full use of categories’ structure and their labels semantics to enrich the text representation, and therefore attempted to improve the classification performance of the text. In addition, HE-HMTC facilitates improving the accuracy of HMTC tasks. Moreover, our HE-HMTC can easily be generalized in other hierarchical classification tasks and achieve superior performance.
Model optimization strategies based on deep neural networks Learning and application of pruning optimization algorithms

Abstract Deep learning is becoming increasingly important in the context of machine learning, with notable performance achieved in many experiments and attempts. However, if one wants to port a deep learning network from a computationally powerful CPU platform to a small embedded mobile device, it will be constrained by various aspects such as power consumption storage. This paper addresses this problem by simplifying the model, i.e. by model compression. Specifically, by collating work on compression pruning techniques from recent years, the importance of the parameter weights of the data model in the training phase is analysed and then the model is tested with new data in the inference phase to reduce the parameter weights and avoid over-parameterisation, thus achieving model optimisation. Ultimately, an effective idea for model pruning was derived: using the absolute magnitude of the parameters and feature outputs to measure their importance, while analysing the effect of parameter clipping on the loss function. This paper provides a summary distillation of relevant model compression work in recent years to provide a reference for research enthusiasts in the field of pruning and to pave the way for further research.
Unordered Image Stitching Based on the Registration Index Table

Abstract In order to solve the problem of large amount of calculation and time-consuming in the process of unordered image stitching, we propose an image stitching algorithm based on the image registration index table in this paper. Firstly, we construct the matching index sequence based on the similarity of the matching area. Then we search for image matching pairs according to the matching index sequence, carry out image registration and stitch directly, and generate the registration index table. For image stitching, we build a binary tree model assisted by the image registration index table, we need to search for matching pairs and carry out registration and stitching according to the registration index table generated by each layer. Compare to the current research on disordered image stitching, our method significantly reduces the times of feature extraction and matching by at least half, therefore, it greatly improves the efficiency of image stitching.
Pedestrian re-recognition method based on dual attention and GRU

Abstract In view of the existing problems of pedestrian re-recognition in real scenes, such as camera Angle change, pedestrian posture change and object occlusion, in order to improve the accuracy of pedestrian re-recognition and enhance the ability of pedestrian feature representation, a pedestrian re-recognition method combining double attention and gated cycle unit (GRU) was established based on ResNet-50. The global structure information of pedestrian images can be better captured by channel attention and spatial attention, and the temporal information of pedestrian images can be obtained by GRU. Combined with the triad loss training network of difficult sample sampling and the cross-entropy loss training network based on label smoothing, the experiments were carried out on dataset Market1501 and CUHK03, and the accuracy of rank-1 reached 96.2% and 76.7%, respectively, which increased by 2% and 2.9% compared with the original ResNet-50 backbone network. Experimental results show that the proposed model can effectively improve the accuracy of pedestrian re-recognition.
Kelayakan dan Pratikalitas Web Kudolar Berbasis Android terhadap Kemampuan Siswa Menganalisis Dinamika Atmosfer

The function and use of information and communication technology (ICT) for education has become an irresistible need, information and communication technology in education will function as a storehouse of knowledge. This study aims to determine the analysis, feasibility and practicality of the android-based kudolar web learning media on atmospheric dynamics material and its impact on the life of class X IPS SMAN 2 Bayang. Types of research and development (Research and Development). Using a 4D model consisting of 4 stages, namely 1) Define 2) Design, 3) Development, 4) Desiminate. The subjects in this study were 116 students of class X Social Sciences. The results of the study found that: a) The appropriate learning media based on Android was declared very feasible after analyzing data processing using Microsoft Excel so that a total of 392 were obtained with an average level of 4.56 and a percentage level of 91% in the "very good" category. b) The appropriate learning media based on Android is declared practical after processing the research obtained from the questionnaire, and a practicality test is carried out so that a score of 2786 is obtained with an average level of 4.65 and a percentage of 93% in the "Practical" category.
Ensemble machine learning model for classification of handwritten digit recognition

Recognizing handwritten numbers is a commonly used application in machine learning. It is used in various applications, including postal mail sorting zip code recognition, writer identification and verification, diacritical processing, and recognition of handwritten digits on a bank check. Each of the 10 digits (0-9) is designated as a category in the context of machine learning-based classification tasks. However, due to the variety of handwriting styles from one person to another, it difficult for a person to distinguish. In addition, each learning algorithm may have its own set of benefits and drawbacks, implying that one algorithm may be able to learn some but not all of the special aspects of handwritten numbers. In order to enhance the effectiveness of the categorization process, a technique for reading handwritten numbers has been presented. Using a convolutional neural network architecture, the image properties of handwritten numerals are retrieved (CNN). Additionally, to train the classifiers, Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression Model were utilized (LRM). The group classifier was able to achieve a recognition accuracy of 98 percent in the experiments using the MNISET dataset.
Studying of an Adaptive Unscented Kalman Filter as a Target Tracking Algorithm with Matlab Simulation

The radar is considered as one of tracking application which we need to tracking unknown targets. This application is using a kalman filter. Also the extended and unscented kalman filter has been created to deal with nonlinear systems such as multidimensional radar tracking. If wideband radar is used, multiple return signals may be obtained by scattering on a single target in a single pulse. It is important to monitor multiple dispersions on an undefined number of artifacts in the specific problem considered, with a standard wideband radar model, whose motion involves a nonlinear rotational velocity component. The measurements used are scattered range, range-rate (in analogy to Doppler), nonlinear functions of the scattered position and velocity, with parameters in the state-space model. In order to evaluate the scene and observe its progression over time, the tracker design uses a Kalman Filter type to model the nonlinear dynamics and measurement items and a multi-pronged association scheme.
Multi-Start, Random Reselection of Algorithms or Both?

Most state-of-the-art optimization algorithms utilize restart to resample new initial solutions to avoid the premature convergence problem. However, resampling is not the only way to avoid this problem. Moreover, initial solutions are not the only cause for premature convergence. Starting from the same set of initial solutions cannot always lead to the same local optimum even when using the same stochastic search method. Here, using a different search algorithm may help to escape local optima.This paper investigates the effectiveness of random selection of a new search algorithm instead of resampling a new initial solution to overcome the premature convergence problem. Selecting a new search algorithm randomly and keeping the same initial solution is compared with sampling a new solution and keeping the same algorithm. The effectiveness of random selection a new algorithm to free solutions that are trapped in local optima is also studied. A number of experiments were conducted to evaluate the success of different random selection approaches in reaching a global optimum. The noise-free BBOB-2010 test suite was used to benchmark different sampling approaches. The results demonstrate the effectiveness of random selection of new algorithms over resampling new initial solutions on a range of optimization problems. Random selection of a new algorithm can improve the success rate by more than 10% compared with that of the best algorithm.
Distributed Nonlinear State Estimation using SDRE Kalman Filter

In this paper two sequential distributed nonlinear estimators are proposed to estimate all subsystems states of a nonlinear large scale system. These estimation methods are based on the State-Dependent--Riccati-Equation (SDRE) Kalman filter. The power plant system considered in this work is naturally distributed systems and has various subsystems that can communicate with each other through a communication network. The objective is to implement a distributed state estimation method for the interconnected subsystems in order to reduce the computation effort required in the centralized estimation scheme. Simulation results show that the performance of the suggested distributed nonlinear estimator is close to the centralized nonlinear estimator, but it is computationally much more efficient.
Using a Multi-Agent System to Better Simultaneous Collaboration Software Systems

In each field, there is a need for better collaborative efforts among team members. The software development community is currently concentrating its efforts on developing tools that will make this endeavor easier. As a result, the two major purposes of a collaborative system are to solve the lack of community consciousness (since group members must be aware of one another) and to maintain flooring directive (i.e. provide means to manage access to shared resource in synchronous groupware systems). The goal of this study is to design a dynamic, extendable, and adaptable framework capable of handling challenges in synchronous community consciousness systems in order to solve these concerns. The major purpose of this study is to improve Simultaneous Collaboration Software Systems utilizing the structure analysis real-time approach (SART), which is based on a multiagent system (MAS)).
Cryptography using Affine Hill Cipher Combining with Hybrid Edge Detection (Canny-LoG) and LSB for Data Hiding

In our time the rapid growth of internet and digital communications has been required to be protected from illegal users. It is important to secure the information transmitted between the sender and receiver over the communication channels such as the internet, since it is a public environment. Cryptography and Steganography are the most popular techniques used for sending data in secrete way. In this paper, we are proposing a new algorithm that combines both cryptography and steganography in order to increase the level of data security against attackers. In cryptography, we are using affine hill cipher method; while in steganography we are using Hybrid edge detection with LSB to hide the message. Our paper shows how we can use image edges to hide text message. Grayscale images are used for our experiments and a comparison is developed based on using different edge detection operators such as (canny-LoG ) and (Canny-Sobel). Their performance is measured using PSNR (Peak Signal to Noise ratio), MSE (Mean Squared Error) and EC (Embedding Capacity). The results indicate that, using hybrid edge detection (canny- LoG) with LSB for hiding data could provide high embedding capacity than using hybrid edge detection (canny- Sobel) with LSB. We could prove that hiding in the image edge area could preserve the imperceptibility of the Stego-image. This paper has also proved that the secrete message was extracted successfully without any distortion.
A Novel Method for Estimate the Structure Design Complication of Multi-Agents Based Systems

Making a design decision in architecture design (AD) is challenging, especially when the system has many components and interactions. The complication is evident in the design phase of agents-based systems that are assigned various responsibilities, according to most works of literature. The study's purpose is to assist system designers in developing effective architectures for agents systems that are not complicated and easy to understand. Complex Task Measurement is a novel estimation of entanglement between agents' tasks presented in this paper (CTM). This method is built on the use-case point perspective, however with several adjustments to meet the demands of agents. Before making a design decision, this innovative measurement can be utilized to estimate the architecture design. The findings will be put to use in a variety of situations. As a case study, the research will be applied to the book recommendations system, and the results will be presented after the method has been implemented.