Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Machine Learning and Deep Learning Models for Text Classification

Text classification is the task of forming semantic groups of text documents by assigning predefined class labels. It has wide range of real-life applications in various domains such as engineering, medical science, life science, social sciences and humanities, marketing, governance. Currently, machine learning and deep learning algorithms became popular and effective methods to address text classification problems with labelled data. In this work, we analyse the performance of different machine learning and deep learning algorithms for text classification. For this purpose, we selected six machine learning algorithms using three different vectorization techniques and five deep learning algorithms for the performance evaluation which is evaluated based on classification accuracy. All experiments are conducted on the 20 newsgroups dataset. Results indicate that Logistic Regression outperforms over other ML algorithms and a Bi-channel Convolution Neural Network model gains exciting results compared to other deep learning models.
Transfer Reinforcement Learning Across Homotopy Classes

The ability for robots to transfer their learned knowledge to new tasks-where data is scarce-is a fundamental challenge for successful robot learning. While fine-tuning has been well-studied as a simple but effective transfer approach in the context of supervised learning, it is not as well-explored in the context of reinforcement learning. In this work, we study the problem of fine-tuning in transfer reinforcement learning when tasks are parameterized by their reward functions, which are known beforehand. We conjecture that fine-tuning drastically underperforms when source and target trajectories are part of different homotopy classes: We demonstrate that fine-tuning policy parameters across homotopy classes compared to fine-tuning within a homotopy class requires more interaction with the environment, and in certain cases is impossible. We propose a novel fine-tuning algorithm, Ease-In-Ease-Out fine-tuning, that consists of a relaxing stage and a curriculum learning stage to enable transfer learning across homotopy classes. Finally, we evaluate our approach on several robotics-inspired simulated environments and empirically verify that the Ease-In-Ease-Out fine-tuning method can successfully fine-tune in a sample-efficient way compared to existing baselines.
Improved Butterfly Optimizer-Configured Extreme Learning Machine for Fault Diagnosis

An efficient intelligent fault diagnosis model was proposed in this paper to timely and accurately offer a dependable basis for identifying the rolling bearing condition in the actual production application. The model is mainly based on an improved butterfly optimizer algorithm- (BOA-) optimized kernel extreme learning machine (KELM) model. Firstly, the roller bearing’s vibration signals in the four states that contain normal state, outer race failure, inner race failure, and rolling ball failure are decomposed into several intrinsic mode functions (IMFs) using the complete ensemble empirical mode decomposition based on adaptive noise (CEEMDAN). Then, the amplitude energy entropies of IMFs are designated as the features of the rolling bearing. In order to eliminate redundant features, a random forest was used to receive the contributions of features to the accuracy of results, and subsets of features were set up by removing one feature in the descending order, using the classification accuracy of the SBOA-KELM model as the criterion to obtain the optimal feature subset. The salp swarm algorithm (SSA) was introduced to BOA to improve optimization ability, obtain optimal KELM parameters, and avoid the BOA deteriorating into local optimization. Finally, an optimal SBOA-KELM model was constructed for the identification of rolling bearings. In the experiment, SBOA was validated against ten other competitive optimization algorithms on 30 IEEE CEC2017 benchmark functions. The experimental results validated that the SBOA was evident over existing algorithms for most function problems. SBOA-KELM employed for diagnosing the fault diagnosis of rolling bearings obtained improved classification performance and higher stability. Therefore, the proposed SBOA-KELM model can be effectively used to diagnose faults of rolling bearings.
Natural Language Processing and Its Applications in Machine Translation: A Diachronic Review

As an essential part of artificial intelligence technology, natural language processing is rooted in multiple disciplines such as linguistics, computer science, and mathematics. The rapid advancements in natural language processing provides strong support for machine translation research. This paper first introduces the key concepts and main content of natural language processing, and briefly reviews the history and progress of NLP research at home and abroad. Then, the paper summarizes the three stages of machine translation and its research status. In history, the progress curve of natural language processing almost accords with that of machine translation, and the two complement each other. Based on this, the paper analyzes the applications of natural language processing in machine translation, and points out the challenges and trends in the field of natural language processing. Finally, the author discusses the relationship between machine translation and human translation in the age of artificial intelligence, and visualizes the future prospect of machine translation.
A Systematic Review of Augmented Reality in Multimedia Learning Outcomes in Education

This research involved a systematic and thematic review of literature of studies involving the use of Augmented Reality (AR) in the educational context. Specifically, the review was performed by searching and identifying relevant and recent articles on several leading online databases, which were published from 2014 to 2020. The review yielded 40 articles that discuss various applications of this novel technology. Based on the analysis of their contents, 15 articles are related to multimedia learning involving augmented reality in primary classroom settings. On the other hand, 25 articles discuss studies involving the use of AR in university settings. In terms of content, 18 articles articulate the applications of AR from the explorative perspective, while another 18 articles elaborate the use of AR books in education. By contrast, only 4 articles highlight the use of game-based AR applications in education. The findings of the content analysis carried out suggest that student learning can be improved in AR-enabled learning environments with multimedia elements that provide high interactivity and immersion in which not only students can see and visualize learning objects and contents but also they can readily interact with such three-dimensional visual objects. Arguably, in exploring or navigating and interacting in such environments, students can enhance their cognitive and spatial skills as well. Overall, the above findings provide a greater insight into the understanding of AR characteristics that have a profound impact on the teaching and learning process.
An empirical comparison and characterisation of nine popular clustering methods

Nine popular clustering methods are applied to 42 real data sets. The aim is to give a detailed characterisation of the methods by means of several cluster validation indexes that measure various individual aspects of the resulting clusters such as small within-cluster distances, separation of clusters, closeness to a Gaussian distribution etc. as introduced in Hennig (in: Data analysis and applications 1: clustering and regression, modeling—estimating, forecasting and data mining, ISTE Ltd., London, 2019). 30 of the data sets come with a “true” clustering. On these data sets the similarity of the clusterings from the nine methods to the “true” clusterings is explored. Furthermore, a mixed effects regression relates the observable individual aspects of the clusters to the similarity with the “true” clusterings, which in real clustering problems is unobservable. The study gives new insight not only into the ability of the methods to discover “true” clusterings, but also into properties of clusterings that can be expected from the methods, which is crucial for the choice of a method in a real situation without a given “true” clustering.
Multi-Scale Context Aggregation Network with Attention-Guided for Crowd Counting

Crowd counting aims to predict the number of people and generate the density map in the image. There are many challenges, including varying head scales, the diversity of crowd distribution across images and cluttered backgrounds. In this paper, we propose a multi-scale context aggregation network (MSCANet) based on single column encoder-decoder architecture for crowd counting, which consists of an encoder based on dense context-aware module (DCAM) and a hierarchical attention-guided decoder. To handle the issue of scale variation, we construct the DCAM to aggregate multi-scale contextual information through densely connecting the dilated convolution with varying receptive fields. The proposed DCAM can capture rich contextual information of crowd areas due to its long-range receptive fields and dense scale sampling. Moreover, to suppress the background noise and generate a high-quality density map, we adopt a hierarchical attention-guided mechanism in the decoder. This helps to integrate more useful spatial information from shallow feature maps of the encoder by introducing multiple supervision based on semantic attention module (SAM). Extensive experiments demonstrate that the proposed approach achieves better performance than other similar state-of-the-art methods on three challenging benchmark datasets for crowd counting. The code is available at https://github.com/KingMV/MSCANet.
Exploiting Quantum Teleportation in Quantum Circuit Mapping

Quantum computers are constantly growing in their number of qubits, but continue to suffer from restrictions such as the limited pairs of qubits that may interact with each other. Thus far, this problem is addressed by mapping and moving qubits to suitable positions for the interaction (known as quantum circuit mapping). However, this movement requires additional gates to be incorporated into the circuit, whose number should be kept as small as possible since each gate increases the likelihood of errors and decoherence. State-of-the-art mapping methods utilize swapping and bridging to move the qubits along the static paths of the coupling map---solving this problem without exploiting all means the quantum domain has to offer. In this paper, we propose to additionally exploit quantum teleportation as a possible complementary method. Quantum teleportation conceptually allows to move the state of a qubit over arbitrary long distances with constant overhead---providing the potential of determining cheaper mappings. The potential is demonstrated by a case study on the IBM Q Tokyo architecture which already shows promising improvements. With the emergence of larger quantum computing architectures, quantum teleportation will become more effective in generating cheaper mappings.
Identifying technological development trajectories in blockchain domain: a patent citation network analysis

In the past few years, blockchain technology has attracted more and more attention, and it has been widely used in various fields. But there is still a lack of relevant research reports on the innovation path identification of blockchain technology from a patent perspective. By downloading a total of 14,560 patents from Derwent Innovations Index and conducting main path analysis, this paper illuminates the overall picture of the development of blockchain for experts in this domain from a technology perspective. As an objective and quantitative network-based method, main path analysis identifies important patent, structural backbone and development trajectories in the blockchain development. This provides a reference for the scientific communities to understand the current state of blockchain technology, thereby illuminating future research.
BCH Codes with Minimum Distance Proportional to Code Length

BCH codes are among the best practical cyclic codes widely used in consumer electronics, communication systems, and storage devices. However, not much is known about BCH codes with large minimum distance. In this paper, we consider narrow-sense BCH codes of length $n = \frac{q^m-1}{N}$ with designed distance $\delta = \frac{s}{q-1}n$ proportional to $n$, where $N$ divides $\frac{q^m-1}{q-1}$ and $1 \le s \le q-1$. We determine both their dimensions and minimum distances. In particular, when $N=1$, the codes are primitive, with minimum distance $d=\frac{s}{q-1}(q^m-1)$ and dimension $k = (q-s)^m$. The general result on code dimensions is achieved by applying generating functions and inverse discrete Fourier transforms to an enumeration problem.
Further Study of 2-to-1 Mappings Over F<sub>2<sup>n</sup> </sub>

2-to-1 mappings over finite fields play an important role in symmetric cryptography, particularly in the constructions of APN functions, bent functions, and semi-bent functions. Very recently, Mesnager and Qu [IEEE Trans. Inf. Theory 65 (12): 7884-7895] provided a systematic study of 2-to-1 mappings over finite fields. In particular, they determined all 2-to-1 mappings of degree at most 4 over any finite field. Besides, another research direction is to consider 2-to-1 polynomials with few terms. Some results about 2-to-1 monomials and binomials have been obtained in [IEEE Trans. Inf. Theory 65 (12): 7884-7895]. Motivated by their work, in this present paper, we push further the study of 2-to-1 mappings, particularly over finite fields with characteristic 2 (binary case being the most interesting for applications). Firstly, we completely determine 2-to-1 polynomials with degree 5 over \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2<sup>n</sup></sub> using the well-known Hasse-Weil bound. Besides, we consider 2-to-1 mappings with few terms, mainly trinomials and quadrinomials. Using the multivariate method and the resultant of two polynomials, we present two classes of 2-to-1 trinomials, which explain all the examples of 2-to-1 trinomials of the form x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> +βx <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>l</i></sup> + αx ∈ \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2<sup>n</sup></sub> [x] with n ≤ 7. We derive twelve classes of 2-to-1 quadrinomials with trivial coefficients over \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2<sup>n</sup></sub> .
Attention neural collaboration filtering based on GRU for recommender systems

Abstract The collaborative filtering method is widely used in the traditional recommendation system. The collaborative filtering method based on matrix factorization treats the user’s preference for the item as a linear combination of the user and the item latent vectors, and cannot learn a deeper feature representation. In addition, the cold start and data sparsity remain major problems for collaborative filtering. To tackle these problems, some scholars have proposed to use deep neural network to extract text information, but did not consider the impact of long-distance dependent information and key information on their models. In this paper, we propose a neural collaborative filtering recommender method that integrates user and item auxiliary information. This method fully integrates user-item rating information, user assistance information and item text assistance information for feature extraction. First, Stacked Denoising Auto Encoder is used to extract user features, and Gated Recurrent Unit with auxiliary information is used to extract items’ latent vectors, respectively. The attention mechanism is used to learn key information when extracting text features. Second, the latent vectors learned by deep learning techniques are used in multi-layer nonlinear networks to learn more abstract and deeper feature representations to predict user preferences. According to the verification results on the MovieLens data set, the proposed model outperforms other traditional approaches and deep learning models making it state of the art.
APANet: Auto-Path Aggregation for Future Instance Segmentation Prediction

Despite the remarkable progress achieved in conventional instance segmentation, the problem of predicting instance segmentation results for unobserved future frames remains challenging due to the unobservability of future data. Existing methods mainly address this challenge by forecasting features of future frames. However, these methods always treat features of multiple levels (e.g., coarse-to-fine pyramid features) independently and do not exploit them collaboratively, which results in inaccurate prediction for future frames; and moreover, such a weakness can partially hinder self-adaption of a future segmentation prediction model for different input samples. To solve this problem, we propose an adaptive aggregation approach called Auto-Path Aggregation Network (APANet), where the spatio-temporal contextual information obtained in the features of each individual level is selectively aggregated using the developed "auto-path". The "auto-path" connects each pair of features extracted at different pyramid levels for task-specific hierarchical contextual information aggregation, which enables selective and adaptive aggregation of pyramid features in accordance with different videos/frames. Our APANet can be further optimized jointly with the Mask R-CNN head as a feature decoder and a Feature Pyramid Network (FPN) feature encoder, forming a joint learning system for future instance segmentation prediction. We experimentally show that the proposed method can achieve state-of-the-art performance on three video-based instance segmentation benchmarks for future instance segmentation prediction.
Multiple Face Detection Using Haar - AdaBoosting, LBP-AdaBoosting and Neural Networks

Abstract Multiple Face Detection may be a process of identifying or recognizing quite one face on a picture. Face detections have recently attracted increasing interests thanks to the multitude of applications that end in format. There are numerous methods for identifying the face on the image but here we are using ‘Haar-AdaBoost’, ‘LBP-AdaBoost’, and ‘Neural Networks’ for identifying the faces on the image. And also we are comparing each of the methods to urge which method is giving highly accurate results and which method is giving results very quickly. In humans, by seeing the positions of Eyes, Nose, and Mouth another person can identify the face. The neural mechanism within the brain controls these all and provides the result. We are implementing an equivalent concept on the machine so that the machine can identify the faces by using the positions of the Eyes, Nose, and Mouth. The machine will identify quite one face or multiple faces within the image by using this, and that we also can compare which algorithm will provide a good percentage of accurate results.
Fine-Grained Activity Recognition for Assembly Videos

In this letter we address the task of recognizing assembly actions as a structure (e.g. a piece of furniture or a toy block tower) is built up from a set of primitive objects. Recognizing the full range of assembly actions requires perception at a level of spatial detail that has not been attempted in the action recognition literature to date. We extend the fine-grained activity recognition setting to address the task of assembly action recognition in its full generality by unifying assembly actions and kinematic structures within a single framework. We use this framework to develop a general method for recognizing assembly actions from observation sequences, along with observation features that take advantage of a spatial assembly's special structure. Finally, we evaluate our method empirically on two application-driven data sources: 1) An IKEA furniture-assembly dataset, and 2) A block-building dataset. On the first, our system recognizes assembly actions with an average framewise accuracy of 70% and an average normalized edit distance of 10%. On the second, which requires fine-grained geometric reasoning to distinguish between assemblies, our system attains an average normalized edit distance of 23%-a relative improvement of 69% over prior work.
Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement

According to a recent article published by Forbes, the use of enterprise blockchain applications by companies is expanding. Private blockchain, such as enterprise blockchain, usually uses the Raft algorithm to achieve a consensus. However, the Raft algorithm can cause network split in unstable networks. When a network applying Raft split, the TPS(Transactions Per Second) is decreased, which results in decreased performance for the entire blockchain system. To reduce the probability of network split, we select a more stable node as the next leader. To select a better leader, we propose three criteria and suggest exploiting federated learning to evaluate them for network stability. As a result, we show that blockchain consensus performance is improved by lowering the probability of network split.
A benchmarking framework for cyber-attacks on autonomous vehicles

In this paper, a novel framework for a benchmark system for autonomous vehicles focusing on their security and reliability is proposed. Computer vision and networking technologies are improving offering solutions towards automation in connected autonomous vehicles. These systems are using sensor technologies, including vision and communication, providing information and measurements for the environment and other connected vehicles. As a result, unlike conventional vehicles, autonomous vehicles have to communicate with other vehicles as well as other external network infrastructure. However, such requirements make autonomous vulnerable to the attack. This may also motivate diverse types of cyber threats and attacks like traffic signs modification, GPS spoofing, and Vehicular Adhoc network distributed denial of service. Hence, this paper explores various aspects of security issues, vulnerabilities, exploitation methods and the adverse effect of them on connected autonomous vehicles and propose a novel benchmark framework focusing on physical and communication-based attack to evaluate and assets the state-of-the-art technologies that are currently use during cyber-attack.