Reviewerly/exHarmony huggingface dataset:
The Effectiveness of Multiplex Teaching Method in Mastering Vocabulary for Deaf Students

&lt;p style="text-align:justify"&gt;This study aims to measure the effectiveness of “multiplex teaching method” in mastering vocabulary for deaf students. Multiplex teaching method consists of picture language, sign language, printed-word language, written language, and spoken language. The research was designed as a single subject research (SSR) with baseline, intervention, and maintenance phase (A-B-A’ design). The research subjects consisted of two deaf students in special school of SLB Bhakti Luhur Malang, Indonesia. In addition, a special education teacher and an observer were involved in this study. The intervention instrument comprised five lesson plans (LP), each containing a vocabulary of four words. The data were analyzed using intra-condition and inter-condition graphical inspection with a focus on data stability, trends, and score changes. The effectiveness was determined by the Percentage of Non-Overlapping data (PND). The change of score from A'/A achieved by Subject-1 was 7.86 points, while Subject-2 obtained 7.68 points. Subject-1 obtained an average PND B/A of 100% and average PND A'/B of 82.5%. Subject-2 achieved an average PND B/A of 99% and PND A’/B of 90%. Thus, multiplex teaching method is very effective in helping deaf students master vocabulary.&lt;/p&gt;
PGMANet:Pose-Guided Mixed Attention Network for Occluded Person Re-Identification

Recently, the person re-identification task becomes increasingly crucial in crowded scenarios, e.g., airports and schools. Many methods with high performance have been proposed to solve this problem. However, the existence of occlusion still challenges the development of person re-identification. In this work, we present the novel Pose - Guided Mixed Attention Network (PGMANet), an end-to-end framework to deal with pedestrian reidentification under occluded situations by fusing posture and second-order information. Specially, we employ two models. The first is Human Part - level Attention Model. We use key point information of a pedestrian to generate a heat map to enhance the pedestrian body part's feature. Simultaneously, we design Second - order Information Attention Model to investigate the correlation among features of different parts. Experimental results show that our method achieves state-of-the-art person re-identification performance on two challenging occlusion datasets Occluded-DukeMTMC and Occluded-Reid.
A Systems Engineering Approach to the Design and Education of a Robotic Baby

Abstract In this paper, a novel architecture, Baby , for robotic babies is proposed with a systems engineering design approach. A robotic baby is an autonomous learning system with minimal structures at the beginning of its life, aiming to gradually learn languages and actions from interactions with human parents and the world. Throughout the paper, the philosophy and the reasoning behind a robotic baby with a systems engineering design approach is discussed. With the architecture, system verification and validation are conducted, which shows convincing language skills in parsing, similar to observed human cognitive behaviors. A proof of concept demonstration involving an integration with a 3D printer is also presented. With preliminary results, the roadmap for further development and the positioning of the architecture are presented.
Identifying Deceptive Reviews: Using Linguistic and Spammer Behaviour

As the number of e-commerce websites is increasing day by day, people rely on reviews to judge whether a product or a service is worthwhile. If the number of positive reviews is high, then sales of the product will be high and vice versa. If the reviews available on the platform are spam, it will have an immense effect on the business of sellers and also the customers are made to believe something, which is not true. Hence this issue needs to be addressed as both customers and service providers are concerned regarding spam review recognition. Identifying a spam review is a challenging task since the fake reviewer also follows a similar pattern as a genuine user. Earlier studies have concentrated on sentiment approach to determine spam reviews and have ignored the personal qualities of the reviewer. In this perspective, the proposed project presents two different methods: Deceptive review detection using the linguistic method, which works based on the content of the review, lexical diversity and feature selection; Deceptive review detection using spammer behaviour, where different behavioural features are available based on which the percentage of deceptiveness and spam users will be calculated. Using the dataset obtained from a bigger platform, where social interaction is high like Yelp or Amazon can lead to better accuracy with a wide range of opinions and behavioural patterns. Many websites as Amazon, Trip Advisor admitted that, a majority of their reviews are deceptive; this situation leads them to the requirement of project, which can identify such spam reviews.
Bayesian Nonparametric Modeling of Conditional Multidimensional Dependence Structures

In recent years, conditional copulas, that allow dependence between variables to vary according to the values of one or more covariates, have attracted increasing attention. However, the literature mainly focused on the bivariate case, since the constraints on the multivariate copulas correlation matrices would make the specifications of covariates arduous. In high dimension, vine copulas offer greater flexibility compared to multivariate copulas, since they are constructed using bivariate copulas as building blocks. We present a novel inferential approach for multivariate distributions, which combines the flexibility of vine constructions with the advantages of Bayesian nonparametrics, not requiring the specification of parametric families for each pair copula. Expressing multivariate copulas using vines allows us to easily account for covariate specifications driving the dependence between response variables. We specify the vine copula density as an infinite mixture of Gaussian copulas, defining a Dirichlet process prior on the mixing measure, and performing posterior inference via Markov chain Monte Carlo sampling. Our approach is successful as for clustering as well as for density estimation. We carry out simulation studies and apply the proposed approach to analyze a veterinary dataset and to investigate the impact of natural disasters on financial development. Supplementary materials for this article are available online.
A Method of Relation Extraction: Integrating Graph Convolutional Networks, Relative Entity Position Attention and Back-Multi-Head-Attention Mechanism

Relation extraction is the task of identifying entity pairs and relationship between entity pairs. Moreover, it is a key method for knowledge discovery. It has been proven effective to run the graph convolutional networks on the syntactic dependency tree to extract the syntactic features of sentences. The entities in the sentence and position of entities have a great influence on the results of relation extraction. Based on graph convolutional networks, we propose a relation extraction model that combines relative entity position attention and Back-Multi-Head-Attention mechanism. On the one hand, we design a network structure based on the relative entity position attention mechanism, so that the model can focus on the position of the entity pair in the sentence. On the other hand, we propose the Back-Multi-Head-Attention mechanism, which enables the model to maintain the memory of the original features while extracting features. It prevents the degradation of the network. We integrate the outputs of the relative entity attention mechanism network and the outputs of GCNs, then process the feature by Back-Multi-Head-Attention mechanism and classifier to extract the relation. The experiments are conducted on the sentence-level relation extraction. Experiments show that our approach achieves competitive F1 value compared with previous dependency-based models.
Structure-Aware Hierarchical Graph Pooling using Information Bottleneck

Graph pooling is an essential ingredient of Graph Neural Networks (GNNs) in graph classification and regression tasks. For these tasks, different pooling strategies have been proposed to generate a graph-level representation by downsampling and summarizing nodes' features in a graph. However, most existing pooling methods are unable to capture distinguishable structural information effectively. Besides, they are prone to adversarial attacks. In this work, we propose a novel pooling method named as HIBPool where we leverage the Information Bottleneck (IB) principle that optimally balances the expressiveness and robustness of a model to learn representations of input data. Furthermore, we introduce a novel structure-aware Discriminative Pooling Readout (DiP-Readout) function to capture the informative local subgraph structures in the graph. Finally, our experimental results show that our model significantly outperforms other state-of-art methods on several graph classification benchmarks and more resilient to feature-perturbation attack than existing pooling methods <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Source code at: https://github.com/forkkr/HIBPool.
Metaheuristic-based optimization applied to GAPID controller

This work presents a comparative study of the performance of two bioinspired metaheuristics - Whale Optimization Algorithm (WOA) and Artificial Bee Colony (ABC) to find the parameters of an Adaptive Gaussian PID controller (GAPID), comparing the results with a known benchmark, the Particle Swarm Optimization (PSO). The GAPID was tested with a Buck step-down converter, and the overall system exhibits multimodal characteristics where different solutions that can achieve similar performances. The GAPID gains are defined to be relative to the PID gains in order to inherit the same design requiremets. This results a total of six parameters to be defined using the optimization algorithms. To determine the quality of the results obtained by the optimizers, evaluation metrics based on the Integral of Absolute Error (IAE) were used to determine the fitness of the solutions found. The computational results were obtained through simulations using julia® (V1.5.3), which follows that all the methods used are capable of solving the proposed problem. In the overall evaluation, the PSO obtained the best results and comparing the two bioinspired metaheuristics, the WOA obtained the best result.
Outlier Detection by Privacy-Preserving Ensemble Decision Tree U sing Homomorphic Encryption

One of the most important processes in big data analysis is outlier detection, where anomalies of observed data are detected and eliminated to improve the system performance. Many approaches to outlier detection have been proposed so far for classification and prediction purposes. This paper focus on the outlier detection under a practical circumstance such that multiple organizations possess different data sets of a specific task, while they cannot directly share with each other from a privacy point of view; that is, each organization is not allowed to provide their sensitive data to other entities but they are keen to cooperate in data analysis for some reasons. To address this issue, we present a new outlier detection approach to the data analysis for multiple organizations using a decision tree ensemble based on the so-called federated learning scheme. In this paper, we extend an existing outlier detection mode called Isolation Forest to the federated learning concept so that data protection of each entity can be achieved by introducing an additive homomorphic encryption. The experimental results for several benchmark data sets demonstrate that the proposed privacy-preserving Isolation Forest (pp-iForest) achieves stable classification performance, which is almost the same as that under a single-organization setting, even when the number of organization is increased.
Reinshard: An Optimally Sharded Dual-Blockchain for Concurrency Resolution

Decentralized control, low-complexity, flexible and efficient communications are the requirements of an architecture that aims to scale blockchains beyond the current state. Such properties are attainable by reducing ledger size and providing parallel operations in the blockchain. Sharding is one of the approaches that lower the burden of the nodes and enhance performance. However, the current solutions lack the features for resolving concurrency during cross-shard communications. With multiple participants belonging to different shards, handling concurrent operations is essential for optimal sharding. This issue becomes prominent due to the lack of architectural support and requires additional consensus for cross-shard communications. Relying on the advantages of hybrid Proof-of-Work/Proof-of-Stake (PoW/PoS), like Ethereum , hybrid consensus and 2-hop blockchain , we propose Reinshard , a new blockchain that inherits the properties of hybrid consensus for optimal sharding. Reinshard uses PoW and PoS chain-pairs with PoS sub-chains for all the valid chain-pairs where the hybrid consensus is attained through Verifiable Delay Function (VDF). Our architecture provides a secure method of arranging nodes in shards and resolves concurrency conflicts using the delay factor of VDF. The applicability of Reinshard is demonstrated through security and experimental evaluations. A practical concurrency problem is considered to show the efficacy of Reinshard in providing optimal sharding.
Attention-guided Progressive Partition Network for Human Parsing

Human parsing emphasizes on human-oriented semantic segmentation for understanding the fine-grained body parts. In this paper, we make the first attempt to utilize saliency guided information to address the single human parsing task. Existing methods that explore the fusion of information from other related tasks mainly consider refining pose structure or edge features in a human parsing model. However, the intrinsic incompatibility of multiple tasks due to discrepant optimization targets brings the absence of global semantic information. To overcome this problem, considering the tight inter-task correlation against incompatibility, we propose an Attention-guided Progressive Partition Network (APPNet) to exploit the benefits of combining saliency detection and human parsing into a unified network. In APPNet, a Global Attention Module (GAM) is designed to capture weighted features in the spatial dimension from a global view and fuse multi-level complementary attention weights. Simultaneously, to capture appropriate receptive-field multi-scale features, we present a simple yet effective Feature Extraction Module (FEM) to adapt to this attention model. A series of experiments on four datasets demonstrate that APPNet outperforms several remarkable methods including the best model guided by other domain information and several attention-based approaches for human parsing. Code is available at http://github.com/huangxi6/APPNet.
Intelligent Storage System of Machine Learning Model Based on Task Similarity

With the closer integration of database and machine learning, machine learning task in database can reduce the data transmission, thus dramatically boosting the runtime performance of the whole task. Moreover, if there is a chance of storing machine learning models involved in similar tasks in the system intelligently, the computation resource and time cost of repeated training will be greatly reduced. However, the intelligent storage system of machine learning model has not been developed yet. In order to achieve this goal, a method is proposed to measure the similarity of machine learning tasks. Second, the intelligent storage system of machine learning model was designed to manage models. Finally, it introduced the overall architecture and key technologies of intelligent storage system of machine learning model based on task similarity (ISSMLM), and describe three demonstration scenarios of the system. The results show the validity of the proposed method.
A novel software defect prediction method based on hierarchical neural network

To ensure software reliability, software defect prediction (SDP) techniques are employed to help developers effectively allocate the testing resources. Recently, researchers utilized deep learning models to extract semantic features from abstract syntax tree (AST) of source code which showed a better prediction performance over metric-based methods. However, the existing file-level SDP models representing the AST as a flattened sequence could jeopardize the preservation of long-term dependency. In this paper, we propose a new Defect Prediction framework based on the Hierarchical Neural Network (DP-HNN). Our method makes use of the hierarchical structure of AST by splitting the large file-level AST into several subtrees according to certain AST nodes crucial to SDP task. These subtrees represented by node-level sequences are encoded separately and then serve as the elements of the subtree-level sequence. Finally, a multi-granularity fusion approach is performed in the subtree-level encoder to obtain the crucial features that represent the code file. Our proposed DP-HNN is aimed at capturing long-term dependency while preserving fine-grained local information. We conducted experiments on 11 open-source projects considering the cross-version and the mixed-version scenario of within-project SDP. Results show that on average, DP-HNN improves the state-of-the-art method by 14% and 3% on MCC and AUC scores respectively.
Motion Planning of 7-DOF Manipulator Based on Quintic B-Spline Curve

In order to improve the working efficiency and stability of the robot, a motion planning strategy of multi joint serial manipulator is proposed in this paper. In face of different time demands, two alternative motion planning methods is offered for robot selection to plan according to the established requirements. In one case, when the robot can make timely response to the environment, an effective motion planning method can be selected. Firstly, the improved RRT algorithm is used to quickly plan the robot path, and then the quintic quasi uniform B-spline function is used to fit the trajectory. According to this, the robot can respond quickly and timely. On the other hand, when the robot has enough time for motion planning, we can consider how to improve the quality of robot motion planning. The robot can use another method for motion planning. At this time, we also need to plan the robot path first, then use the quintic non-uniform B-spline curve fitting, and finally use the improved particle swarm optimization algorithm to optimize the trajectory of the robot. The robot can have a stable and efficient trajectory in the working environment. By using the motion planning method proposed in this paper, the robot can adapt to most of the work requirements and objectives.
Quantum walks on regular graphs with realizations in a system of anyons

We build interacting Fock spaces from association schemes and set up quantum walks on the resulting regular graphs (distance-regular and distance-transitive). The construction is valid for growing graphs and the interacting Fock space is well defined asymptotically for the growing graph. To realize the quantum walks defined on the spaces in terms of anyons we switch to the dual view of the association schemes and identify the corresponding modular tensor categories from the Bose–Mesner algebra. Informally, the fusion ring induced by the association scheme and a topological twist can be the basis for developing a modular tensor category and thus a system of anyons. Finally, we demonstrate the framework in the case of Grover quantum walk on distance-regular graph in terms of anyon systems for the graphs considered. In the dual perspective interacting Fock spaces gather a new meaning in terms of anyon collisions for the case of distance-regular graphs.
Enumeration of self-reciprocal irreducible monic polynomials with prescribed leading coefficients over a finite field

A polynomial is called self-reciprocal (or palindromic) if the sequence of its coefficients is palindromic. In this paper we enumerate self-reciprocal irreducible monic polynomials over a finite field with prescribed leading coefficients. Asymptotic expression with explicit error bound is derived, which implies that such polynomials of degree 2n always exist provided that the number of prescribed leading coefficients is slightly less than n/4. Exact expressions are also obtained for fields with two or three elements and with up to two prescribed leading coefficients.
Design and Implementation of CNN Traffic Lights Classification Based on FPGA

Convolutional Neural Networks (CNN) has acquired remarkable achievements in image classification and target detection. It is of great significance to the field of unmanned driving. As a professional accelerating device that is good at parallel computing, FPGA can fully exploit the parallel processing capacity of CNN. Deploying CNN on Field Programmable Gate Array (FPGA) has a wide range of applications. This paper proposes a tri-classification convolutional neural networks with a simple structure and easy FPGA implementation, which is used to realize traffic lights recognition. Based on the understanding of the convolution process, this design adopts methods such as optimized array structure, multi-level data multiplexing, and positive index classification to improve parallelism in the hardware implementation process, which can take into account speed and performance. Transplant the improved CNN to FPGA through High-level Synthesis (HLS) software. The experimental results present that the accuracy of traffic lights classification can reach about 99%, and the processing speed on the FPGA platform is 22 times higher than that on Inter Core i5-8300H CPU.
The impact on the employees through the use of AI tools in accountancy

In the agriculture and healthcare industries, AI has been deployed to achieve better crop production, disease prediction, continuous monitoring, efficient supply chain management, improved operational efficiency, and reduced water waste, with the main goal of designing standard, reliable product quality control methods and the search for new ways of reaching and serving society while maintaining low cost. Machine learning and deep learning are two of the most often used AI approaches. Individuals, businesses, and government agencies utilize these models to anticipate and learn from data. Machine learning models for the complexity and diversity of data in the real-world applications are being developed at the moment. This article discusses machine learning and artificial intelligence applications in agriculture, healthcare, management, and social studies. Disease Prediction, water irrigation optimization, Sales growth, profit maximization, sales forecast, inventory management, security, fraud detection, and portfolio management are some of the major uses.