Reviewerly/exHarmony huggingface dataset:
Towards Better Evaluation of Topic Model Quality

Topic modelling is a popular unsupervised method for text corpora processing to obtain interpreted knowledge of the data. However, there is an automatic quality measurement gap between existing metrics, human evaluation and performance on the target tasks. That is a big challenge for automatic hyperparameter tuning methods as they heavily rely on the output signal to define the optimization direction. Currently, this process of evaluating the effectiveness of the topic model faces a number of difficulties and keeps being a labour-intensive routine performed manually due to the absence of a universal metric that may show strong correspondence with human assessment. The development of a quality metric that may satisfy this condition is essential to provide valuable feedback for the optimization algorithm when working with flexible and complex models, such as models based on additive regularisation or neural networks. To address the quality measurement gap, we performed an experimental study of existing scores on a specially created dataset containing topic models for several different text corpora in two languages accompanied by evaluated existing metrics and scores obtained from human assessment. The study results show how the situation with automatic quality estimation may be improved and pave the way to metrics learning with ensembles of machine learning algorithms.
Blockchain Application Analysis Based on IoT Data Flow

In the Internet of Things (IoT) system, data leakage can easily occur due to the differing security of edge devices and the different processing methods of data in the transmission process. Blockchain technology has the advantages of good non-tamperability, decentralization, de-trust, openness, and transparency, and it can protect data security on the Internet of Things. This research integrates the means by which data flow can be combined with blockchain technology to prevent privacy leakage throughout the entire transportation process from sender to receiver. Through a keyword search of the last five years, 94 related papers in Web of Science and IEEE Xplore were extracted and the complex papers and frameworks explained using a reconstruction graph. The data processing process is divided into five modules: data encryption, data access control, data expansion, data storage, and data visualization. A total of 11 methods combining blockchain technology to process IoT data were summarized. The blockchain application technology in the IoT field was summarized objectively and comprehensively, and a new perspective for studying IoT data flow was given.
Effective enhancement method of low-light-level images based on the guided filter and multi-scale fusion

Aiming to solve the problem of low-light-level (LLL) images with dim overall brightness, uneven gray distribution, and low contrast, in this paper, we propose an effective LLL image enhancement method based on the guided filter and multi-scale fusion for contrast enhancement and detail preservation. First, a base image and detail image(s) are obtained by using the guided filter. After this procedure, the base image is processed by a maximum entropy-based Gamma correction to stretch the gray level distribution. Unlike the existing methods, we enhance the detail image(s) based on the guided filter kernel, which reflects the image area information. Finally, a new method is proposed to generate a sequence of artificial images to adjust the brightness of the output, which has a better performance in image detail preservation compared with other single-input algorithms. Experiments show that the proposed method can provide a more significant performance in enhancing contrast, preserving details, and maintaining the natural feeling of the image than the state of the art.
A Method of Transparent Graceful Failover in Low Latency Stateful Microservices

Microservice architecture is a preferred way to build applications. Being flexible and loosely coupled, it allows to deploy code at a high pace. State, or, in other words, data is not only a commodity but crucial to any business. The high availability and accessibility of data enables companies to remain competitive. However, maintaining low latency stateful microservices, for example, performing updates, is difficult compared to stateless microservices. Making changes to a stateful microservice requires a graceful failover, which has an impact on the availability budget. The method of graceful failover is proposed to improve availability of a low latency stateful microservice when performing maintenance. By observing database connection activity and forcefully terminating idle client connections, the method allows to redirect database requests from one node to another with negligible impact on the client. Thus, the proposed method allows to keep the precious availability budget untouched while performing maintenance operations on low latency stateful microservices. A set of experiments was performed to evaluate stateful microservice availability during failover and to validate the method. The results have shown that near-zero downtime was achieved during a graceful failover.
Deformation Measurement of a SS304 Stainless Steel Sheet Using Digital Image Correlation Method

The digital image correlation (DIC) method is widely used in deformation measurements as it has the advantages of being a non-contact, high precision method that provides full field measurements, and requires simple experimental equipment. Traditionally, the grayscale speckle patterns captured by a monochromatic camera are used in the DIC method. With the growing development of consumer color cameras, there is great potential for developing color information in the DIC method. This paper proposes a displacement- and stress–strain-invariant DIC deformation measurement method based on the integer-pixel matching approach for speckle patterns during a tension test. For the integer-pixel matching stage, the load and displacement and stress–strain-invariant histories feature is used to estimate the initial value of the deformation parameters. In addition, this paper proposes a reverse retrieve strategy, instead of a forward search, to reduce the search time. Experiments show that the proposed DIC deformation measurement approach is not only capable of displacement invariance measurement, with robustness and high efficiency, but also that the average accuracy of the stress–strain result can reach 0.1%.
Achieving Heisenberg scaling on measurement of a three-qubit system via quantum error correction

In many-body quantum systems, the quantum Fisher information an observer can obtain is susceptible to decoherence. Consequently, quantum enhanced metrology, such as Heisenberg scaling, cannot usually be achieved. We show, via two distinct methods, that by applying periodic quantum error corrections, we can achieve the Heisenberg scaling for an extended period of time on a three-qubit Tavis-Cumming Model, where three two-level atoms interact with a single cavity mode, under certain approximations. The generalization to arbitrary number of atoms case is also discussed.
Virtual Lab Environment for Programmable Logic Controllers Training

Virtual environments for educational and training purposes have become a trend in the last decade as an alternative to traditional pedagogic methods. In this work, a framework for the design of a virtual training system from a Programmable Logic Controller (PLC) and its integration in a virtual environment based on Unity and Computer Aided Design (CAD) modeling is presented. The system presents a 3D environment of an Automation and Control Lab and virtualization of a PLC interface with interactive digital and analog inputs and outputs. A Ladder Diagram (LD) interface for the user to address the mentioned PLC elements is also presented. A System Usability Scale (SUS) test was carried out to determine system functionality and usability, the system and the test were applied to a group of 65 participants. 15 participants evaluated the 3D environment while the other 50 evaluated the PLC and the LD interface. The obtained results show an average of 73.19 and 75.8 points respectively in the SUS tests, making it possible to determine that the usability and functionality of the systems are in a good position.
Real-time multi-quadrotor trajectory generation via distributed receding architecture and hierarchical planning in complex environments

Generation of multi-quadrotor trajectories in real-time in complex three-dimensional environments remains a grand challenge. Trajectory planning becomes computationally prohibitive as the number of quadrotors and obstacles increases. This paper proposes the distributed receding architecture-based hierarchical trajectory planning method (drHTP) to tackle this issue. The distributed receding architecture is established to formulate and solve a series of single-quadrotor short-horizon planning problems for reducing the computation complexity. In distributed planning, the time-heuristic priority mechanism is devised to assign a reasonable planning sequence to enhance the convergence performance. The hierarchical planning, including front-end initial trajectory generation and back-end trajectory optimization, is introduced for the single-quadrotor in each short horizon to further reduce the computation time. The sparse A* search algorithm is modified to only consider adjacent obstacles for obtaining the initial trajectory rapidly. The convergence of drHTP is analyzed theoretically. Numerical simulations with moving and dense obstacle scenarios are carried out to verify the effectiveness of drHTP. The comparative simulation results demonstrate that drHTP outperforms the state-of-the-art distributed sequential convex programming and distributed model predictive control methods in terms of computational efficiency. drHTP is also validated by the physical experiment in an indoor testbed.
Domain Adaptation Training of a Transformer

Domain adaptation is a powerful technology that solves the problem of data distribution mismatch among different domains, and improves generalization performance in various deep learning application fields. However, this technique is not easily applicable to home appliances such as air conditioners that are important in daily life. In this study, we propose a Domain Adaptation Transformer(DAT) to predict the amount of refrigerant in air conditioners using deep learning based on a transformer encoder and the domain-adversarial training of neural networks (DANN). The proposed DAT is a novel deep-learning-based refrigerant prediction model that is not constrained by the specific types of air conditioners, and can be used to other types of air conditioners. We construct a novel dataset to develop and test our model on different types of air conditioners and experiments on those datasets. Experimental results demonstrate that our approach outperforms heuristic methods based on conventional physics phenomena and achieves excellent performance in domain adaptation tests.
Point-Cloud Perspective Transform via Super-Resolution Model

The LiDAR sensor, which detects the relative distance to an object, is widely used in the field of 3D object detection based on point cloud, and is a key sensor in autonomous vehicles because it can recognize the distance and shape of the surrounding object. The LiDAR sensor extracts different features even when looking at the same object depending on the installation location and angle. Usually, the open dataset and the user's LiDAR installation location are different. In this case, the performance of the model trained with the open dataset is not fully exhibited. We propose a method and model to convert the point cloud data acquired from the source location to the point cloud acquired from the target location.
High-speed BNN Design in HLS with Optimized Classification and Computation Method

With the development of computer vision and artificial intelligence, many neural networks are being studied. Also, FPGAs are being used in many fields in modern society by its versatility. While FPGA resources are limited, neural networks are getting bigger, and the amount of computation is also increasing. Therefore, even if the accuracy is slightly lowered, more focus is placed on the amount of H/W resources and speed. In this paper, the latency is reduced by 29% compared to the conventional design by using the pipeline and loop flatten to optimize the operation. Also, while using the H/W resources available in the FPGA, the power and accuracy are kept similar to those of the conventional BNN.
Optimization of Hyperparameters in Object Detection Models Based on Fractal Loss Function

Hyperparameters involved in neural networks (NNs) have a significant impact on the accuracy of model predictions. However, the values of the hyperparameters need to be manually preset, and finding the best hyperparameters has always puzzled researchers. In order to improve the accuracy and speed of target recognition by a neural network, an improved genetic algorithm is proposed to optimize the hyperparameters of the network by taking the loss function as the research object. Firstly, the role of all loss functions in object detection is analyzed, and a mathematical model is established according to the relationship between loss functions and hyperparameters. Secondly, an improved genetic algorithm is proposed, and the feasibility of the improved algorithm is verified by using complex fractal function and fractional calculus. Finally, the improved genetic algorithm is used to optimize the hyperparameters of the neural network, and the prediction accuracy of the model before and after the improvement is comprehensively analyzed. By comparing with state-of-the-art object detectors, our proposed method achieves the highest prediction accuracy in object detection. Based on an average accuracy rate of 95%, the detection speed is 20 frames per second, which shows the rationality and feasibility of the optimized model.
A Secure Cloud-based Architecture for monitoring Cyber-Physical Critical Infrastructures

Monitoring systems that capture information from the physical world are becoming increasingly essential nowadays. These systems can integrate diverse technologies such as sensor networks, IoT devices, Big Data analysis, machine learning classifiers and predictors, and other tools that can help protect critical infrastructures, mainly their physical assets. However, limitations regarding hardware and edge computing resources can challenge the suitable implementation of these systems. Furthermore, cybersecurity has become a serious concern since one needs to ensure system availability and the integrity and authenticity of large data amounts. This paper proposes the thoughtful employment of cloud computing resources to address the abovementioned problems, applying microservices and cloud computing for validating blockchain security and monitoring the protection of physical environments. Our contributions focus on two main aspects: a microservices-based architecture to deal with data receiving, analysis, and storage while enhancing availability; and the integration of blockchains as a security mechanism to verify data integrity and authenticity using smart contracts. Moreover, our proposal also discusses the challenges involving blockchains in a Big Data scenario and appropriate mechanisms to deal with scalability and performance constraints. Finally, we present a complete implementation of our architecture using the AWS cloud environment and Hyperledger Fabric platform. Our tests demonstrate how microservices can help increase blockchain throughput. We argue that our architecture proposal and its implementation constitute a suitable solution for modern monitoring systems and serve as a reference model for future works.
A hardware-friendly pruning approach by exploiting local statistical pruning and fine grain pruning techniques

Deep neural networks (DNN) have recently become a popular research topic and achieved well success in many signal processing tasks. However, when we deploy these neural networks on resource-limited hardware; e.g., edge devices, computation complexity and memory capability become great challenges. Many researches devote in compressing model parameters without significant accuracy loss, which is so-call pruning approach, so as to reduce the requirements of computation resource and memory space. However, pruned neural network (NN) model often shows heavy irregular sparsity between layers, convolution kernels, etc. Thus the utilizations of multiply-and-accumulate (MAC) array or processing element (PE) array could be low so that the inference time could not be reduced accordingly. In this paper, we propose a hardware-friendly pruning approach by exploiting local statistical pruning and fine-grain pruning techniques to possibly improve the utilizations of MAC array or PE array. Performance evaluations demonstrate that the performance of a NN-based super-resolution was kept good (i.e., > 37dB) with a high pruning ratio (i.e., > 55%)
Training human-like bots with Imitation Learning based on provenance data

Believable NonPlayer Characters in video games are one of the most challenging problems in the game industry over the last years. Players demand to expect to perceive the NPCs as other human-based player. Modeling NPC behavior manually is not always a good choice, mainly due to the number of NPCs a game can have and the difficulty of modeling a large number of actions that they can take. Our main goal is create a believable NPC acting like a real player. This work proposes an approach to training an NPC using Imitation Learning so that it is as similar as possible to a human player. Through this strategy, NPCs are trained from various types of players, avoiding predefined behaviors. Our proposal trains agents with the use of provenance data sets, tackling cause-effects data mining possibilities, and use Generative Adversarial Imitation Learning framework to take actions similar to what a player would take. The model proposed was create to be generic and applicable to various games. We validate our presented model with the DodgeBall environment inside Unity ML-Agents Toolkit for Unity Engine. Some players was asked to play against our agent and they validated the believability of our trained NPCs.
Real-Time Semantic Segmentation on Edge Devices with Nvidia Jetson AGX Xavier

Semantic segmentation is the problem of classifying every pixel in the image from a predefined set of classes or categories. It is a vital part of an autonomous vehicle vision system to perform traffic scene parsing. In such application, having accurate and low-latency performance is necessary but also challenging due to the task's high computational complexity. This study presents our implementation and performance evaluation of recent state-of-the-art semantic segmentation methods aiming to operate in high processing speed while maintaining high accuracy. Our experimental results on Cityscapes dataset and on Nvidia Jetson AGX Xavier mobile embedded platform demonstrate that it is possible to achieve that goal on resource-limited devices.
Blockchain Meets Formal Logic: Semantics Level Cybersecurity Challenges

Blockchain is a disruptive combination of dis-tributed data structures and cryptography functions in an attempt to implement the concept of trust among multiple distributed agents. It fostered the arising of diverse applications from cryptocurrencies to Distributed Autonomous Organisations (DAO), passing by smart contacts. Despite the strong crypto-graphic means employed, still, Blockchain-based technologies are not free from vulnerabilities from a cybersecurity point of view. Many countermeasures have been introduced to cope with known vulnerabilities at the network, platform, and application levels, but it is still several issues to overcome at the semantics/logical level, where the attacks target particularly smart contracts, made of logical conditions that can be seen as predicates and inference rules. Such attacks are all the more difficult to detect since they are played at the logical level. Therefore, we explore in this paper the relevance, constraints, and properties of logical frameworks to formally verify the correctness of smart contracts before their implementation and be able to solve some vulnerabilities and then prevent their related cyber-attacks.
CST-Godot: Bridging the Gap Between Game Engines and Cognitive Agents

Cognitive architectures (CA) exist in the neighborhood of research in general AI. However, despite the high importance placed on classical AI techniques in developing complex videogame systems and the success the CA field has had in producing intelligent behavior of different kinds, it has gone largely unexplored in the context of videogame development. This paper presents a framework for implementing cognitive agents based on CAs in the Godot game engine. We employ the Cognitive Systems Toolkit (CST) for this purpose, providing opportunities for development in the academic sphere, primarily for creating experiments and visualizers that take advantage of Godot's physics and rendering libraries. In the context of videogame development, it can help expand upon classical AI techniques and enable more complex and interesting systems' implementation. As a concept proof, we show how a reinforcement learning-based agent can successfully learn how to behave in a game designed under this cognitive environment.
ICpSAF - Integrated Cross-platform Security Assessment Framework

This article presents a proposal for an “Integrated cross-platform security assessment framework” (ICpSAF) for real-time security management, which is one of the results of the TecSEG Project (Security Technologies Project), a long-term Brazilian program focused on Research, Development and Innovation in Security Assessment. It is known that there are several frameworks related to security assessment, however, the vast majority have a specific focus on some technology, which is not a reality in the current scenario, where there is and is required a great heterogeneity of technologies in the solutions. To meet this challenge, the TecSEG project was proposed, where one of the main objectives is the development and application of security assessment and investigation methodologies with strong integration with national and international cybersecurity reference centers, which the framework proposed in this article is the base.
Dual-Attention Sparse R-CNN via Single ROI Transformer and Dynamic CBAM

A critical drawback of CNN-based object detection is an additional post processing such as non-maximum suppression(NMS). Recent works have solved that problem by applying a vision transformer, which is able to make sparse correlation between feature maps and proposal embedding. Sparse R-CNN, which is end-to-end object detection network, applies dynamic convolution for the correlation but requires many parameters. Based on the sparse R-CNN, this paper presents a dual-attention module for accurate and efficient end-to-end object detection. The proposed dual-attention method consists of: i) dynamic CBAM, which is more efficient than dynamic convolution and ii) single ROI transformer. Experimental results show that the proposed method not only is efficient in the sense of reduced number of parameters but also improves the performance.
Efficient Human Action Recognition with Dual-Action Neural Networks for Virtual Sports Training

In this paper, we propose an efficient human action recognition method for virtual sports training. In the proposed method, 3D skeleton data of human actions for four kinds of sports, which are composed of ready actions and main ones, are accurately captured by using RGB-D cameras. Then, skeleton features, which are sophisticatedly designed for each sport training, are extracted from captured 3D skeleton data. Finally, a dual-action neural network, which is developed to simultaneously recognize ready actions and main ones for sports training, is trained with extracted skeleton features. The effectiveness of the proposed method based on the dual-action neural network is verified with several real experiments for various sports.