Reviewerly/exHarmony huggingface dataset:
HSP: Hybrid Synchronous Parallelism for Fast Distributed Deep Learning

In the parameter-server-based distributed deep learning system, the workers simultaneously communicate with the parameter server to refine model parameters, easily resulting in severe network contention. To solve this problem, Asynchronous Parallel (ASP) strategy enables each worker to update the parameter independently without synchronization. However, due to the inconsistency of parameters among workers, ASP experiences accuracy loss and slow convergence. In this paper, we propose Hybrid Synchronous Parallelism (HSP), which mitigates the communication contention without excessive degradation of convergence speed. Specifically, the parameter server sequentially pulls gradients from workers to eliminate network congestion and synchronizes all up-to-date parameters after each iteration. Meanwhile, HSP cautiously lets idle workers to compute with out-of-date weights to maximize the utilizations of computing resources. We provide theoretical analysis of convergence efficiency and implement HSP on popular deep learning (DL) framework. The test results show that HSP improves the convergence speedup of three classical deep learning models by up to 67%.
Towards a GPU accelerated selective sparsity multilayer perceptron algorithm using K-Nearest Neighbors search

The use of artificial neural networks and deep learning is common in several areas of knowledge. In many situations, it is necessary to use neural networks with many neurons. For example, the Extreme Classification problems can use neural networks that process more than 500,000 classes and inputs with more than 100,000 dimensions, which can make the training process unfeasible due to the high computational cost required. To overcome this limitation, several techniques were proposed in past works, such as the SLIDE algorithm, whose implementation is based on the construction of hash tables and on CPU parallelism. This work proposes the SLIDE-GPU, which replaces the use of hash tables by algorithms that use GPU to search for approximate neighbors, or approximate nearest neighbors (ANN) search. In addition, SLIDE-GPU also proposes the use of GPU to accelerate the activation step of neural networks. Among the experiments carried out, it was possible to notice a training process acceleration of up to 268% in execution time considering the inference accuracy, although currently maintaining the backpropagation phase with CPU processing. This suggests that further acceleration can be obtained in future work, by using massive parallelism in the entire process. The ANN-based technique provides better inference accuracy at each epoch, which helps producing the global acceleration, besides using the GPU in the neuron activation step. The GPU neuron activation acceleration reached a 28.09 times shorter execution time compared to the CPU implementation on this step alone.
A Software/Hardware Co-design Local Irregular Sparsity Method for Accelerating CNNs on FPGA

Convolutional neural networks (CNNs) have been widely used in different areas. The success of CNNs comes with a huge amount of parameters and computations, and nowaday CNNs still keep moving toward larger structures. Although larger structures often bring about better inference accuracy, the increasing size also slows the inference speed down. Recently, various parameter sparsity methods have been proposed to accelerate CNNs by reducing the number of parameters and computations. Existing sparsity methods could be classified into two categories: unstructured and structured. Unstructured sparsity methods easily cause irregularity and thus have a suboptimal speedup. On the other hand, the structured sparsity methods could keep regularity by pruning the parameters following a certain pattern but result in low sparsity. In this paper, we propose a software/hardware co-design approach to bring local irregular sparsity into CNNs. Benefiting from the local irregularity, we design a row-wise computing engine, RConv Engine, to achieve workload balance and remarkable speedup. The experimental results show that our software/hardware co-design method can achieve a 10.9x speedup than the state-of-the-art methods with a negligible accuracy loss.
Robust detectors of rotationally symmetric shapes based on novel semi-shape signatures

Efficient detectors of rotationally symmetric shapes are proposed by introducing a novel concept of semi-shape signatures to overcome the main problem of projection-based approaches for studying the rotationally symmetric properties of an arbitrary binary shape. Indeed, the fact that the projection cues in these conventional approaches are periodical with a period of π has restricted an applicable exploitation of rotational symmetry detection. To this end, we propose a new concept of the profile of semi-shapes as a shape signature together with a simple yet efficient technique so that the rotational symmetry of the binary shape can be determined by considering the correlation between this signature and its circular shift. Moreover, a new meaningful measure, ranging from 0 to 1, is also introduced to indicate how perfect the rotational symmetry would be. Experimental results of detecting on single/compound shapes have clearly corroborated the competence of our proposal.
Improving edit-based unsupervised sentence simplification using fine-tuned BERT

Word suggestion in unsupervised sentence simplification aims to replace complex words of a given sentence with their simpler alternatives. This is mostly done without considering their context within the input sentence. In this paper, we propose a technique that brings context awareness to word suggestion by merging pre-trained BERT models with a successful edit-based unsupervised sentence simplification model. More importantly, we show that only by fine-tuning the BERT model on simple English corpora, simplification results can be improved and even outperform some of the competing supervised methods. Finally, we introduce a framework that involves filtering an arbitrary amount of unlabeled in-domain text for tuning the model in situations where labeled data, as simple and complex, is scarce. This preprocessing step also speeds up the training process by ignoring fine-tuning on unnecessary samples.
A turbo‐based encryption and coding scheme for multiple‐input multiple‐output orthogonal frequency division multiplexing wireless communication systems affected by Doppler frequency offset

Abstract Multiple‐Input Multiple‐Output Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) is an essential technology in the wireless communication system. However, its performance is significantly affected by Carrier Frequency Offset (CFO), which leads to inter‐carrier interference (ICI). The CFO estimation and compensation techniques are used to mitigate the Doppler effect. In addition, the confidentiality of data is a key factor of the system. This paper proposes a Turbo‐based encryption and coding scheme to improve reliability and security for the MIMO‐OFDM wireless communication systems affected by the Doppler frequency offset. In our proposed scheme, the secret key is produced from channel parameters between legitimate users and it is used as a seed for generating a pseudo‐random bit sequence using an Advanced Encryption Standard (AES) generator with variable key lengths. The Turbo code's puncturing mechanism is controlled by this pseudo‐random bit sequence. The proposed method is applied to the system with different modulation schemes through the Rayleigh channel. The simulation results show that the system affected by the Doppler effect would obtain the best performance when using the highest key length of the AES generator, the BPSK, and a great number of receive antennas. Furthermore, the proposed solution outperforms several previous approaches for the MIMO‐OFDM systems.
The indexing policy in the practices of Brazilian institutional repositories: A diagnostic study from the perception of managers and indexers

Institutional repositories aim to gather, treat and disseminate information resources produced in a given institution. The adoption of an indexing policy aims to improve information retrieval by the user community of the institutional repository, by proposing elements that standardize the practices of librarians who work in subject cataloging. The objective of the research is to analyze the perception of managers and indexers of Brazilian institutional repositories about the indexing policy for information organization and representation, with the specific objectives of performing a compared evaluation of elements and variables (characteristics, processes, and instruments) of the indexing policy regarding indexing and retrieval, discussion of vocabulary control procedures, and obtaining an overview focusing on the perception of the need for an indexing policy in Brazilian institutional repositories. Semi-structured interviews were applied to nine participants from eight institutional repositories to investigate the requirements, elements and variables of the indexing policy in the practice of managers and librarians of institutional repositories. The results reveal the need for improvements in the practices of the analyzed institutional repositories regarding the activities of subject cataloging, standardization of subject metadata, and vocabulary control, queries, and in carrying out tests or trials for continuous evaluation of indexing and information retrieval with users. The study also revealed that this scenario can be more positive if institutional repositories adopt an indexing policy for the practices they perform in repositories to be understood by managers and librarians as a tool to improve information retrieval. La política de indización en las prácticas de los repositorios institucionales brasileños: un estudio de diagnóstico desde la percepción de gestores e indexadores
An Efficient Hybrid Linear Clustering Superpixel Decomposition Framework for Traffic Scene Semantic Segmentation

Superpixel decomposition could reconstruct an image through meaningful fragments to extract regional features, thus boosting the performance of advanced computer vision tasks. To further optimize the computational efficiency as well as segmentation quality, a novel framework is proposed to generate superpixels from the perspective of hybridizing two existing linear clustering frameworks. Instead of conventional grid sampling seeds for region clustering, a fast convergence strategy is first introduced to center the final superpixel clusters, which is based on an accelerated convergence strategy. Superpixels are then generated from a center-fixed online average clustering, which adopts region growing to label all pixels in an efficient one-pass manner. The experiments verify that the integration of this two-step implementation could generate a synergistic effect and that it becomes more well-rounded than each single method. Compared with other state-of-the-art superpixel algorithms, the proposed framework achieves a comparable overall performance in terms of segmentation accuracy, spatial compactness and running efficiency; moreover, an application on image segmentation verifies its facilitation for traffic scene analysis.
Multiobjective Optimization of the Performance of Safety Systems

The activation of the safety-instrumented systems in industrial processes is carried out after the occurrence of specific deviations (dangerous situations) from normal operation (normal situations), but in some cases, the safety-instrumented systems are activated in the absence of deviations or requests; these are the unwanted activations. The system chosen in this study is a system with high-pressure gas and inflammable gas, and it is protected by a firefighting system that prevents any kind of accident in order to protect humans, systems, and the environment. The activation of the emergency shutdown system causes stoppage of the whole system by closing the input and output valves. This paper presents the optimization of the voting redundancies of safety-instrumented systems by a multiobjective genetic algorithm. The objectives to optimize are the average probability of dangerous failure on demand, which represents the system safety integrity, and the spurious trip rate, which presents the activation of a safety function without the presence of a demand.
A Granular Computing Classifier for Human Activity with Smartphones

Recently, smart home devices have been widely used to assist and facilitate the lives of human beings. Human activity recognition (HAR) aims to identify human activities using sensors in smartphones. Therefore, it can be employed in many applications, such as remote health monitoring for disabled and elderly people. This paper proposes a granular computing-based approach to classifying human activities using wearable sensing devices. The approach has two main phases: feature selection and classification. In the feature selection phase, the approach attempts to remove redundant and irrelevant attributes. At the same time, the classification phase makes use of granular computing concepts to build the granules and find the relationships between granules at different levels. To evaluate the approach, we applied the dataset to five famous machine learning models. For the comparative evaluation, we also tested other well-known machine learning methods. The experimental results presented in this paper show that the approach outperformed common traditional classifiers in terms of classification precision recall, f-measure, and MCC for most recognized human activities by approximately 97.3%, 94%, 95.5%, and 94.8%, respectively. However, in terms of processing time, it performs comparably.
Biomac3D: 2D-to-3D Human Pose Analysis Model for Tele-Rehabilitation Based on Pareto Optimized Deep-Learning Architecture

The research introduces a unique deep-learning-based technique for remote rehabilitative analysis of image-captured human movements and postures. We present a ploninomial Pareto-optimized deep-learning architecture for processing inverse kinematics for sorting out and rearranging human skeleton joints generated by RGB-based two-dimensional (2D) skeleton recognition algorithms, with the goal of producing a full 3D model as a final result. The suggested method extracts the entire humanoid character motion curve, which is then connected to a three-dimensional (3D) mesh for real-time preview. Our method maintains high joint mapping accuracy with smooth motion frames while ensuring anthropometric regularity, producing a mean average precision (mAP) of 0.950 for the task of predicting the joint position of a single subject. Furthermore, the suggested system, trained on the MoVi dataset, enables a seamless evaluation of posture in a 3D environment, allowing participants to be examined from numerous perspectives using a single recorded camera feed. The results of evaluation on our own self-collected dataset of human posture videos and cross-validation on the benchmark MPII and KIMORE datasets are presented.
Smart Yoga instructor for guiding and correcting Yoga postures in real time

In recent days, Yoga is gaining more prominence and people all over the world have started to practice it. Performing Yoga with proper postures is beneficial. Hence, an instructor is required to monitor the correctness of Yoga postures. However, at times, it is difficult to have an instructor. This study aims to provide a system that will act as a personal Yoga instructor and practitioners can practice Yoga in their comfort zone. The device is interactive and provides audio guidance to perform different Yoga asanas. It makes the use of a camera to capture the picture of the person performing Yoga in a particular position. This captured pose is compared with the benchmark postures. A pretrained deep learning model is used for the classification of different Yoga postures using a standard dataset. Based on the comparison, the practitioner's posture will be corrected using a voice message to move the body parts in a certain direction. As the device performs all the operations in real-time, it has a quick response time of a few seconds. Currently, this work aids the practitioners in performing five Asanas, namely, Ardha Chandrasana/Half-moon pose, Tadasana/Mountain pose, Trikonasana/Triangular pose, Veerabhadrasana/Warrior pose, and Vrikshasana/Tree pose.
Innovative Usage of Grid Solutions with a Technology Behavior Model in a Medium-Size Enterprise

Integrating technology innovation within human behaviour challenged a new paradigm shift during business uncertainty. Virtualized grid server implementation is still in its infancy for the “Medium Size Enterprise” that is sandwiched between financial constraints and growing economies of scale opportunities. Grounded by the “Technology Acceptance Model” with the “Theory of Planned Behaviour”, the proposed “Technology Behaviour Model” is developed. A “convenient sampling” method was applied to gather feedback through mixed methods from various enterprises in Malaysia on several considerations that influence technology and behaviour. The research applies “PLS-SEM” with statistic triangulation, exploratory, and cross-sectional analysis. The results show that the adoption of the BOINC share-product solution to structure the returns, by encouraging monetary savings, utilized composed interworks which were consequently further developed. In ascertaining union legitimacy, the extensive unwavering quality is more noteworthy than 0.9, and the mean Average Variance Extracted is above 0.85. The Hetero-Trait-Mono-Trait relationship proportion above 0.85 is normally used to assess the Dependent Variable, with a time span of 95% arriving at 1. This new model uses “Exostructure as a Service” organic server virtualization to drive digital transformation that relooks into the infrastructure overheads. From the tested hypothesis at the Slightly Critical and Most Critical correlation, further theoretical and management contributions were elaborated.
Output Layer Structure Optimization for Weighted Regularized Extreme Learning Machine Based on Binary Method

In this paper, we focus on the redesign of the output layer for the weighted regularized extreme learning machine (WRELM). For multi-classification problems, the conventional method of the output layer setting, named “one-hot method”, is as follows: Let the class of samples be r; then, the output layer node number is r and the ideal output of s-th class is denoted by the s-th unit vector in Rr (1≤s≤r). Here, in this article, we propose a “binarymethod” to optimize the output layer structure: Let 2p−1&lt;r≤2p, where p≥2, and p output nodes are utilized and, simultaneously, the ideal outputs are encoded in binary numbers. In this paper, the binary method is employed in WRELM. The weights are updated through iterative calculation, which is the most important process in general neural networks. While in the extreme learning machine, the weight matrix is calculated in least square method. That is, the coefficient matrix of the linear equations we solved is symmetric. For WRELM, we continue this idea. And the main part of the weight-solving process is a symmetry matrix. Compared with the one-hot method, the binary method requires fewer output layer nodes, especially when the number of sample categories is high. Thus, some memory space can be saved when storing data. In addition, the number of weights connecting the hidden and the output layer will also be greatly reduced, which will directly reduce the calculation time in the process of training the network. Numerical experiments are conducted to prove that compared with the one-hot method, the binary method can reduce the output nodes and hidden-output weights without damaging the learning precision.
A hierarchical long short term safety framework for efficient robot manipulation under uncertainty

Safe and efficient robot manipulation in uncertain clustered environments has been recognized to be a key element of future intelligent industrial robots. Unlike traditional robots that work in structured and deterministic environments, intelligent industrial robots need to operate in dynamically changing and stochastic environments with limited computation resources. This paper proposed a hierarchical long short term safety system (HLSTS), where the upper layer contains a long term planner for global reference trajectory generation and the lower layer contains a short term planner for real-time emergent safety maneuvers. Additionally, a hierarchical coordinator is proposed to enable smooth coordination of the two layers by compensating the communication delay through trajectory modification. The theoretical results verify that the long term planner can always find a feasible trajectory (feasibility guarantee); and the short term planner can guarantee safety in the probabilistic sense. The proposed architecture is validated in industrial settings in both simulations and real robot experiments, where the robot is interacting with randomly moving obstacles while performing a goal reaching task. Experimental results demonstrate that the proposed HLSTS framework not only guarantees safety but also improves task efficiency.
Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree

Spatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial scales, we propose a method called Multi-Scale Massive Points Fast Clustering based on Hierarchical Density Spanning Tree. This study refers to the basic principle of Clustering by Fast Search and Find of Density Peaks aggregation algorithm and introduces the concept of a hierarchical density-based spanning tree, combining the spatial scale with the tree links of elements to propose the corresponding pruning strategy, and finally realizes the fast multi-scale clustering of elements. The first experiment proved the time efficiency of the method in obtaining clustering results by the distance-scale adjustment of parameters. Accurate clustering results were also achieved. The second experiment demonstrated the feasibility of the method at the aggregation point element and showed its visual effect. This provides a further explanation for the application of tree-link structures.
Embedding knowledge graph of patent metadata to measure knowledge proximity

Abstract Knowledge proximity refers to the strength of association between any two entities in a structural form that embodies certain aspects of a knowledge base. In this work, we operationalize knowledge proximity within the context of the US Patent Database (knowledge base) using a knowledge graph (structural form) named “PatNet” built using patent metadata, including citations, inventors, assignees, and domain classifications. We train various graph embedding models using PatNet to obtain the embeddings of entities and relations. The cosine similarity between the corresponding (or transformed) embeddings of entities denotes the knowledge proximity between these. We compare the embedding models in terms of their performances in predicting target entities and explaining domain expansion profiles of inventors and assignees. We then apply the embeddings of the best‐preferred model to associate homogeneous (e.g., patent–patent) and heterogeneous (e.g., inventor–assignee) pairs of entities.