Reviewerly/exHarmony huggingface dataset:
Deep-Sea: A Reconfigurable Accelerator for Classic CNN

To meet the changing real-time edge engineering application requirements of CNN, aiming at the lack of universality and flexibility of CNN hardware acceleration architecture based on ARM+FPGA, a general low-power all pipelined CNN hardware acceleration architecture is proposed to cope with the continuously updated CNN algorithm and accelerate in hardware platforms with different resource constraints. In the framework of the general hardware architecture, a basic instruction set belonging to the architecture is proposed, which can be used to calculate and configure different versions of CNN algorithms. Based on the instruction set, the configurable computing subsystem, memory management subsystem, on-chip cache subsystem, and instruction execution subsystem are designed and implemented. In addition, in the processing of convolution results, the on-chip storage unit is used to preprocess the convolution results, to speed up the activation and pooling calculation process in parallel. Finally, the accelerator is modeled at the RTL level and deployed on the XC7Z100 heterogeneous device. The lightweight networks YOLOv2-tiny and YOLOv3-tiny commonly used in engineering applications are verified on the accelerator. The results show that the peak performance of the accelerator reaches 198.37 GOP/s, the clock frequency reaches 210 MHz, and the power consumption is 4.52 w under 16-bit width.
Review of Path Planning Algorithms for Unmanned Vehicles

The knowledge of unmanned vehicles path planning algorithm is introduced. Firstly, the basic concept and steps of path planning are described. Secondly, it introduces Dijkstra algorithm, A-Star algorithm, RRT algorithm, PRM algorithm and other typical path planning algorithms from the classification ideas of the path planning algorithm based on search and sampling. The advantages and disadvantages of each algorithm are analyzed, and the applicable environment and conditions of the algorithm are summarized. Finally, several new algorithms and their development directions are introduced.
Theory and Design Considerations for the User Experience of Smart Environments

With the infusion of computation into workplaces and homes, various service settings, and everyday objects, scholars in human–computer interaction (HCI) and related domains have begun to consider the research and design implications not only of smart “things,” but of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">smart environments</i> . Much of the work on smart environments to date has focused on smart homes; related work in HCI explores user values for smart homes, means of interacting with computation in smart homes (e.g., interfaces and agents), how to balance the needs of multiple stakeholders, and how to preserve user trust and autonomy. However, the smart environments of the future will not always fit the smart home mold of a coalescence of products that exist to automate and ease everyday tasks for the end users. They will be both user-focused and goal-focused, public and private, large and small, and ephemeral and long-lasting. It will benefit the field to look at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">smart environments</i> as a unit of analysis—including what these different types of environments have in common and what they do not—from a systemic, user experience design-oriented view. In this survey article, we review prior research on smart environments and various related bodies of literature. Informed by our literature review, we articulate five <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lenses</i> that distinguish different types of smart environments from one another. We then propose research directions for future work on this topic.
Secure Blockchain Interworking Using Extended Smart Contract

Blockchain is a distributed ledger technology used for trading digital assets, such as cryptocurrency, and trail records that need to be audited by third parties. The use cases of blockchain are expanding beyond cryptocurrency management. In particular, the token economy, in which tokenized assets are exchanged across different blockchain ledgers, is gaining popularity. Cross-chain technologies such as atomic swap have emerged as security technologies to realize this new use case of blockchain. However, existing approaches of cross-chain technology have unresolved issues, such as application limitations on different blockchain platforms owing to the incompatibility of the communication interface and crypto algorithm and inability to handle a complex business logic such as the escrow trade. In this study, the ConnectionChain is proposed, which enables the execution of an extended smart contract using abstracted operation on interworking ledgers. Moreover, field experimental results using the system prototype are presented and explained.
A Survey of Open-source Tools for FPGA-based Inference of Artificial Neural Networks

During the recent years artificial neural networks have become a great part of everyday life. One of the big problems in AI is acceleration of neural network inference using different hardware: from CPUs and GPUs to FPGAs and ASICs. Many open-source tools have been proposed for this purpose. This article contains a review of a range of open-source tools for neural network optimization, acceleration and hardware synthesis. Tools of three types have been chosen for evaluation: 1) translating neural network models into synthesizable C; 2) accelerating neural network models using custom hardware accelerators; 3) synthesizing Verilog from neural network models. Some of the tools have been tested using five simple neural network examples. Intel CPU, NVIDIA GPU and Cyclone V FPGA hardware platforms have been used for evaluation. Results show that the tested tools can successfully process neural network models and optimize them for CPU and GPU execution, whereas FPGA execution results are controversial.
Evolving Dendritic Neuron Model by Equilibrium Optimizer Algorithm

In recent years, the role of a single dendritic neural structures with non-linear localisation in computing has attracted a lot of attention from the industry. The dendritic neuron model (DNM) is an approximate logical neuron model based on dendrites, with branches of dendrites corresponding to three distributions in coordinates.The model is trained to assort data as needed by mimicking the mechanisms of transmitting information and biological nerves. Traditionally DNM models use error back propagation (BP) to optimise local minimum problems, but also degrade their performance. We now train it using an equilibrium optimizer based on physical phenomena inspired by control volume mass balance. Experimental results due to some real-world classification problems show that the mentioned algorithm can improve the accuracy of the DNM solution.
Recurrent Neural Networks algorithms and applications

Since recurrent neural networks (RNNs) were firstly proposed, it is widely used, and many extended RNNs algorithms have been developed, which achieve good results in many application fields. To report the latest research results of RNNs and help researchers quickly understand the latest progress of RNNs algorithm. In this research, we briefly introduce the basic principle of RNN, review more than 20 papers about RNNs and summarize the previous work. Then, we make qualitative and quantitative analyses on four representative algorithms (CNN-RNN, ResNet-110, ResNet-164, and ResNet-1001). Among them, the model of CNN-RNN obtains the best performance on the CIFAR-100 dataset with standard data augmentation. Finally, we discuss the future development trend of RNNs.
Evaluation of Activation Functions in CNN Model for Detection of Malaria Parasite using Blood Smear Images

Activation functions are an essential parameter in deep learning models, primarily when it is deployed with CNN. These deep neural networks not only deal with linear data but handles different non-linear classifications as well. Automatic malaria parasite detection using blood smear images is a popular classification application by using CNN architecture. Several models use various activation functions for this parasite detection. Choosing among these activation functions for a custom model sometimes is a time-consuming task. In this paper, we have evaluated Sigmoid, Hyperbolic Tangent Function (Tanh), Rectified Linear Units (ReLU), Leaky ReLU, and Swish activation functions for malaria Parasite detection in the CNN model. It has been observed in our empirical results that the Swish function performs better than others in terms of accuracy and loss value on a given malaria parasite images dataset.
SMC-CPHD Filter with Adaptive Survival Probability for Multiple Frequency Tracking

We propose a sequential Monte Carlo-based cardinalized probability hypothesis density (SMC-CPHD) filter with adaptive survival probability for multiple frequency tracking to enhance the tracking performance. The survival probability of the particles in the filter is adjusted using the pre-designed exponential function related to the distribution of the estimated particle points. In order to ensure whether the proposed survival probability affects the stability of the filter, the error bounds in the prediction process are analyzed. Moreover, an inverse covariance intersection-based compensation method is added to enhance cardinality tracking performance by integrating two types of cardinality information from the CPHD filter and data clustering process. To evaluate the proposed method’s performance, MATLAB-based simulations are performed. As a result, the tracking performance of the multiple frequencies has been confirmed, and the accuracy of cardinality estimates are improved compared to the existing filters.
Depth Image-Based Rendering of Non-Lambertian Content in MPEG Immersive Video

In the context of the development of MPEG-I standard for immersive video compression ISO/IEC 23090-12 (MIV), the need of handling scenes with non-Lambertian materials arose. This class of material is omnipresent in natural scenes, but violates all the assumptions on which depth image-based rendering (DIBR) is based. In this paper, we present a view-synthesizer designed to handle non-Lambertian objects with DIBR, replacing the classical depth maps by multi-coefficients non-Lambertian maps. We report the results of the exploration experiments on Future MIV designed to test this rendering method against the classical DIBR approaches, and demonstrate promising results on all the tested sequences.
Communication-Efficient Coded Distributed Multi - Task Learning

Consider a distributed multi-task learning (MTL) framework where the distributed users first train their own models based on the local data and then send the local updates to the server, and the server sends back helpful information by which each user can learn its task independently. Compared to the single-task case, the distributed MTL suffers more severely from the communication bottleneck, because the users wish to learn multiple models, causing the downlink communication load to linearly increase with the total number of tasks. In this paper, we propose a novel scheme named coded distributed multi-task learning, to reduce the communication loads both in the uplink and downlink. The key idea is to exploit the local information stored at the users during the local training, and utilize a particular repetitive placement and computation on the publicly shared dataset such that coded multicasting opportunities can be created at the server and users. Our method for the first time applies coding strategy to reduce the communication cost for distributed MTL framework. Experiments on real-world dataset show that the proposed scheme can substantially reduce the communication load compared to the traditional uncoded approach.
Text Classification Model Based on Multi-head self-attention mechanism and BiGRU

Deep learning promotes the development of natural language processing quickly. However, there are still many problems in natural language processing, such as semantic diversity and so on. To solve these problems, this paper proposes a text classification model based on multi-head self-attention mechanism and BiGRU. Firstly, the BERT model is used to generate input sequence of tokens for word embedding layer. Secondly, in order to capture the relation of word dependence in sentences, multi-head self-attention mechanism is used to calculate feature vector weights. And bidirectional GRU is used to extract feature and optimize classification model in order to improve calculate speed. The experiment shows that using this proposed model for text classification can improve the accuracy, recall and F1 score also were higher than traditional Benchmark algorithm, such as CNN, time consuming is lower than BERT model. And word embedding on BERT is better than word2vec.
Multi-level feature fusion for nucleus detection in histology images using correlation filters

Nucleus detection is an important step for the analysis of histology images in the field of computational pathology. Pathologists use quantitative nuclear morphology for better cancer grading and prognostication. The nucleus detection becomes very challenging because of the large morphological variations across different types of nuclei, nuclei clutter, and heterogeneity. To address these challenges, we aim to improve the nucleus detection using multi-level feature fusion based on discriminative correlation filters. The proposed algorithm employs multiple features pool, based on varying features combinations. Early fusion is employed to integrate multi-feature information within a pool and inter-pool fusion is proposed to fuse information across multiple pools. Inter-pool consistency is proposed to find the pools which are consistent and complement each other to improve performance. For this purpose, the relative standard deviation is used as an inter-pool consistency measure. Pool robustness to noise is also estimated using relative standard deviation as a robustness measure. High-level pool fusion is proposed using inter-pool consistency and pool-robustness scores. The proposed algorithm facilitates a robust and reliable appearance model for nucleus detection. The proposed algorithm is evaluated on three publicly available datasets and compared with several existing state-of-the-art methods. Our proposed algorithm has consistently outperformed existing methods on a wide range of experiments.
Deeper Neural Networks with Non-Vanishing Logistic Hidden Units: NoVa vs. ReLU Neurons

The new NoVa (nonvanishing) logistic neuron activation allows deeper neural networks because its derivative is positive. So it helps mitigate the problem of vanishing gradients in deep networks. Deep neural classifiers with NoVa hidden units had better classification accuracy on the CFAR-10, CFAR-100, and Caltech-256 image databases compared with threshold-linear ReLU hidden units. Still simpler identity hidden units also outperformed ReLU hidden units in deep classifiers but usually had less classification accuracy than NoVa networks. NoVa hidden neurons also outperformed ReLU hidden neurons in deep convolutional neural networks.
Real Business Applications and Investments in Blockchain Technology

This paper provides an empirical study to identify the objective of companies that are currently investing in adopting blockchain technologies to improve their processes and services. Unlike other studies based on the theoretical potential application of blockchain technology in different sectors, the main objective of this paper is to analyze real projects and investment of companies in blockchain technology. More than 100 blockchain projects from different sectors were examined with the aim of extracting the perceived applicability and business value of blockchain technology by managers, customers, and partners. We identified the most demanded business value and functional properties in each sector and company size, as well as the relationship between the properties that are demanded together. This article assesses the main functional values attributed to blockchain, highlighting those really appreciated by companies that invest in them and identifying new applications of blockchain technology in different sectors, and generating organizational change. The article reveals that, as expected, significant deviations are already occurring between theoretical applications identified in the literature and those finally adopted by the industry.
Whither semantics?

We discuss how mathematical semantics has evolved, and suggest some new directions for future work. As an example, we discuss some recent work on encapsulating model comparison games as comonads, in the context of finite model theory.
Optimization of the Intelligent Asset Management System Based on WSN and RFID Technology

With the development of Internet of Things (IoT) technology, especially the promotion of perception layer radio frequency identification (RFID) technology and wireless sensor network (WSN), a new way of thinking is provided for asset management. Applying both to the management technology of assets is an effective way to achieve intelligent asset management. This paper proposes an intelligent network applied to an asset management system based on WSN and RFID technologies. The whole intelligent asset management is divided into four layers according to the functional structure. From the top layer downward, the layers are the management and dispatch center, communication network, intelligent gateway, and data collection layer in order. Meanwhile, the functions and hardware composition of each layer are described. The data acquisition butcher is the sensing terminal of the whole intelligent asset management network. Smart nodes are its key devices to collect RFID data and sensing data to realize the intelligent sensing capability of the assets. The paper uses the MSP430 control chip, MFRC522 RF read/write chip, CC2425 wireless communication chip, temperature sensor, data memory, and voltage regulator chip to design the smart nodes in the system and details the working principle of the smart nodes and the networking process of the system. The newly designed intelligent asset management network can be deployed independently in asset management or can be integrated into the technical design of existing asset management systems. The management scheduling also collects and manages the RFID and sensing information of the area through the data collection layer, which improves the intelligent construction of the assets.
A Survey of Recent Neural Network Models on Code-Mixed Indian Hate Speech Data

In recent years, given the exponential increase in social media content also led to an increase in online hate speech. We need automatic hate speech detection methods due to the volume of data on the web. Various approaches have been proposed to address hate speech and offensive content on social media. This paper surveys how neural-based models have rapidly evolved to address hate speech on multilingual code-mixed data. We discuss the current state of the research in hate speech and offensive language detection on code-mixed Indian datasets.
Frame-Level Integrated Transmission for Extended Reality over 5G and Beyond

Extended reality (XR) is one of the most important media applications enabled by 5G and beyond. Providing XR services over 5G networks is very challenging due to high requirements in terms of data rate, reliability, and latency. Different from eMBB and URLLC services, XR has a unique characteristic that each XR video frame can be segmented into several correlated packets. Any one of the packets loss would result in frame decoding error, and thus bad quality of experience. In this paper, we investigate such characteristic of frame-level integrity and propose a new concept called frame-level integrated transmission. Unlike conventional wireless communication with the goal of transmitting one packet successfully, the proposed frame-level integrated transmission takes &#x201C;multiple packets belonging to one video frame&#x201D; as an &#x201C;entity&#x201D;. Specifically, we formulate a resource allocation problem by considering the frame-level integrity to maximize the number of satisfied UEs under the data rate, reliability, and latency requirements for each UE. This problem is highly challenging due to the discontinuous and non-convex objective function and intractable reliability constraint. We propose to solve it approximately by jointly considering the user admission control and a frame-level integrated transmission problem to maximize the total number of frames that are successfully transmitted, which is efficiently solved via smooth function approximation and convex-concave procedure. Simulation results demonstrate the superiority of the proposed frame-level integrated transmission scheme, which can achieve much better performance than the conventional proportional fairness scheduler in terms of various performance metrics.
SATune: A Study-Driven Auto-Tuning Approach for Configurable Software Verification Tools

Many program verification tools can be customized via run-time configuration options that trade off performance, precision, and soundness. However, in practice, users often run tools under their default configurations, because understanding these tradeoffs requires significant expertise. In this paper, we ask how well a single, default configuration can work in general, and we propose SATune, a novel tool for automatically configuring program verification tools for given target programs. To answer our question, we gathered a dataset that runs four well-known program verification tools against a range of C and Java benchmarks, with results labeled as correct, incorrect, or inconclusive (e.g., timeout). Examining the dataset, we find there is generally no one-size-fits-all best configuration. Moreover, a statistical analysis shows that many individual configuration options do not have simple tradeoffs: they can be better or worse depending on the program.Motivated by these results, we developed SATune, which constructs configurations using a meta-heuristic search. The search is guided by a surrogate fitness function trained on our dataset. We compare the performance of SATune to three baselines: a single configuration with the most correct results in our dataset; the most precise configuration followed by the most correct configuration (if needed); and the most precise configuration followed by random search (also if needed). We find that SATune outperforms these approaches by completing more correct tasks with high precision. In summary, our work shows that good configurations for verification tools are not simple to find, and SATune takes an important step towards automating the process of finding them.
Web Generator: An open-source software for synthetic web-based user interface dataset generation

Recently, Machine Learning algorithms have been employed to automate several processes, including software development. However, this action demands large datasets for training these algorithms. To our knowledge, there is no tool for generating synthetic datasets that contain HTML objects (interfaces, codes, wireframe.) Thus, we present the Web Generator, a software designed to mainly provide web pages, designs, and content based on the Bootstrap frontend framework. The software delivers markup code, screenshots, and labels for web elements. We aim to generate enough material for training and exploring the Machine Learning approach for automatic web design and development with this software.