Reviewerly/exHarmony huggingface dataset:
Localization and Control Architecture for Collaborating Smart Projectiles in a Contested Environment

We propose a localization and control architecture for communicating and collaborating projectiles in a contested environment. The contested environment degrades communication performance, hindering the ability to control the multi-agent system externally in the rapidly shifting environment, where there is limited access to localization aides such as GPS that bound the navigation errors. In this paper we propose inter-agent observations with realistic onboard transceiver model to aid in the estimation process, and closed-loop control of the projectiles to their intended destinations.
Nature and Quantum-Inspired Procedures – A Short Literature Review

Even before quantum computers were known to the public, quantum-inspired algorithms were already introduced and acquired a well-deserved reputation. Their strength lies on their ability to combine innovative characteristics and superior results. In the almost 20 years that the field has existed, a huge number of results have been obtained. This chapter is an attempt to give a succinct but comprehensive review of the current state of affairs in the field of quantum-inspired-based metaheuristics, particularly geared towards those that can be used in existing quantum computers.
Sentiment Analysis of Barrage Text Based on ALBERT and Multi-channel Capsule Network

With the rapid development of digital media technology, barrage has become an important way for netizens to express their opinions. It is very popular among young users and is becoming popular on many video platforms such as Bilibili and iQiyi. Because there are a large number of “same words with different meanings” in the barrage text, traditional sentiment analysis methods cannot distinguish the different meanings of the same words in different contexts when performing feature extraction. This article proposes a way to combine ALBERT with multi-channel Barrage text sentiment analysis model combined with capsule network (ALBERT-MCaps). First, the ALBERT pre-training language model is used to obtain the dynamic feature representation of the barrage text, and then input to the BiGRU network to encode the sentence word vector to obtain the high-dimensional semantics of the sentence. Then the attention mechanism is used to assist in the generation of multi-channel representations of sentences, and finally, combined with the capsule network, dynamic routing is used to obtain the characteristics of the relationship between the local and global texts. Through experiments on the barrage text data sets of the two platforms Bilibili and iQiyi, the results show that the text model is better than other existing network models.
Integrating Blockchain and Internet of Things for Smart Homes

One of the most popular applications of the Internet of Things (IoT) is smart home which consists of several IoT devices for collecting data and controlling home appliances. Since IoT devices communicate with external entities for service provision smart homes are vulnerable to security threats. Blockchain is a public distributed ledger which is immutable and can securely store data. One solution for security issues with smart homes can be integrating blockchain and smart homes. This paper proposes a new framework for integrating blockchain and smart home in which a smart contract controls the interaction among IoT devices with external entities. Smart contract of a home is associated with the public key of the householder and can have several subcontracts. Subcontracts are used to govern the interaction between one or more IoT devices and one or more external entities. The successful results of the first prototype of the proposed framework show its potential to appropriately address the security and data privacy issues with smart homes.
One System to Rule Them All: A Universal Intent Recognition System for Customer Service Chatbots

Customer service chatbots are conversational systems designed to provide information to customers about products/services offered by different companies. Particularly, intent recognition is one of the core components in the natural language understating capabilities of a chatbot system. Among the different intents that a chatbot is trained to recognize, there is a set of them that is universal to any customer service chatbot. Universal intents may include salutation, switch the conversation to a human agent, farewells, among others. A system to recognize those universal intents will be very helpful to optimize the training process of specific customer service chatbots. We propose the development of a universal intent recognition system, which is trained to recognize a selected group of 11 intents that are common in 28 different chatbots. The proposed system is trained considering state-of-the-art word-embedding models such as word2vec and BERT, and deep classifiers based on convolutional and recurrent neural networks. The proposed model is able to discriminate between those universal intents with a balanced accuracy up to 80.4%. In addition, the proposed system is equally accurate to recognize intents expressed both in short and long text requests. At the same time, misclassification errors often occurs between intents with very similar semantic fields such as farewells and positive
Blockchain and Ecological Impact: Between Reality and Accusation?

The blockchain is a decentralized ledger that enables information transfers to be carried out in a secure manner. It is also a solution mentioned by several sectors, particularly industrial and financing with the creation of crypto currency. The blcokchain minimizes waste thanks to the real-time traceability it provides. But following the growth of mining operations linked to this technology, bitcoin slayers are denouncing an ecological disaster because the activity of mining crypto currencies requires enormous resources of electricity. Other research has disputed these accusations, and presented digit minus in terms of ecological impact. But well aware of the impact of certain democratized crypto currencies, other modes of operation based on renewable energies have emerged in order to reduce their impact on the environment. And some researchers have even considered it as an anti energy-waste on the one hand and on the other hand, this technology is a vector for the growth of renewable energies. The blockchain is therefore seen as an ‘ideal’ ecological alternative. Other schools of thought have considered this technology to be energy intensive, but when compared to other traditional computerized sectors, the ecological impact is quite relative.
GCFI++: Embedding and Frequent Itemset based Incremental Hierarchical Clustering with Labels and Outliers

Customer support is an essential part of any company to ensure customer satisfaction and business growth. The responsibility and scale get even higher for enterprise companies like Freshworks that build customer support platforms for its enterprise customers from multiple and previously unseen domains.
Ultra-Fast FPGA Implementation of Graph Cut Algorithm With Ripple Push and Early Termination

Graph cut has been a popular approach widely used to solve the minimum cut problem, which is prevalent in computer vision tasks, although not limited to this field. Push-relabel is considered as one of the promising algorithms of graph cut due to its good potential to be parallelized. However, existing implementations often not only fail to fully exploit the available parallelism but also fail to make full use of the application context to reduce redundant computations. Therefore, they are not competent for application scenarios with high resolution and real-time requirements. To address the issue, we propose three novel techniques to achieve an ultra-fast and efficient FPGA implementation of a push-relabel algorithm. First, we propose a ripple push technique that significantly parallelizes push operations so as to accelerate the push-relabel convergence process. Second, we propose an early-termination technique that effectively removes redundant computations of the push-relabel algorithm. We also theoretically prove the correctness of our early-termination technique. Third, we propose a highly parallelized search technique called flood irrigation search (FIS). It quickly judges early termination conditions based on a pixel parallel architecture. Our implementation focuses on performance-sensitive applications that divide large images into small graph tiles with a specific size. Compared to the state-of-the-art FPGA implementations of push-relabel algorithms, experimental results show that our method can at least achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.93\times $ </tex-math></inline-formula> improvement of execution time.
Tracking Clinical Trials and Enhancement of Security &amp; Control with Blockchain for Medical Record

The healthcare system uses a centralized system, where a patient's record is stored in the healthcare provider's database. Internet of Things has made a big impact on the healthcare industry. Its centralized databases are prone to privacy and security risks. This paper proposed a research concept based on the blockchain to the healthcare sector for health record access control and providing a way for clinical trials to be tracked with authentic unhampered data on time. Clinical trials outcome success relies heavily on accurate data analyses, the risk of having inaccurate results due to tampered data can be vital. The objectives of the research are to develop a system for patient-doctor interactions using blockchain, enhanced security and control mechanisms for medical record management, to track clinical trials and pharmaceuticals with blockchain, to assess the impact of blockchain on the healthcare industries, to assess the success of blockchain on healthcare industries. Here, we need to develop a blockchain-based meta-model to handle the security and privacy issues in healthcare record-keeping and IoT & Machine Learning to trace the impact of medicines and vaccines on patients in clinical trial scenarios. At the end of this research, we expect a better system where the research and development personnel will have access to real-time data confirmed across the other network members involved.
Invariant Feature Extraction for CNN Classifier by using Gradient Reversal Layer

Deep learning has been successfully applied to a variety of tasks. However, a lot of unnecessary information for the task is included in the training data and it is difficult to automatically remove such unnecessary information in the trained model. For example, it is necessary to ignore the variations of the patients in the measured data for medical diagnosis. To address this problem, we propose a method that applies a module called Gradient Reversal Layer to train the model by removing unnecessary information and extracting only the relevant information for the target task. In this study, we conducted three experiments to demonstrate the effectiveness of the proposed method. The first one is to classify clothing images with unnecessary shift information and the second one is to identify the person from the face images with unnecessary facial expressions. Finally, the third one is to estimate the presence of diseases in medical data with unnecessary variations due to the differences of the patients. In all experiments, we obtained results where the unwanted information in the features was reduced, thereby improving the desired classification performance.
Voice Commanded System for Navigation of Mobile Robots

We propose a new voice commanded system using speech recognition for robots. This RoboticsysTool System performs navigation instructed by isolated-word guiding linear and angular velocity of the TIAGo robot. We also tested the navigation of the TIAGo robot between points of interest commanded by short sentences. We have chosen the Speech Commands database and the Librispeech database to validate the isolated word recognizer and the continuous speech recognizer. We also added babble noise, using the ADDNoiseTool noise addition tool, to the noise generated by the engine TIAGo robot. We reached promising results with this real-time system. Thus, the execution of the MLP and RNN neural networks in this context of the babble noise obtained a satisfactory response for TIAGo robot navigation tasks.
FSASAC: Random Sample Consensus Based on Data Filter and Simulated Annealing

Verification with RANSAC has become a crucial step for local feature-based matching applications in machine vision. Details of its implementation are directly relevant to time consumption and the quality of the estimated results. A novel method of robust model estimation for matched images with a large viewpoint change, namely Random Sample Consensus Based on Data Filter and Simulated Annealing, FSASAC in short, is introduced. FSASAC begins by filtering out most outliers according to their distributions, and then adjusts the sample probability of matched pairs by Simulated Annealing in the process of iteration. The process of sampling ceased until the current best hypothesis met with the termination conditions. Finally, FSASAC is presented experimentally, both on synthesized data and image pairs. Choose some modified methods as the comparative objectives. The results indicate that FSASAC can estimate the optimal model from image pairs with lower number of iterations and run-time under the condition of large viewpoint change, even when the outlier ratio is higher than 90%.
On model selection for dense stochastic block models

Abstract This paper studies estimation of stochastic block models with Rissanen’s minimum description length (MDL) principle in the dense graph asymptotics. We focus on the problem of model specification, i.e., identification of the number of blocks. Refinements of the true partition always decrease the code part corresponding to the edge placement, and thus a respective increase of the code part specifying the model should overweight that gain in order to yield a minimum at the true partition. The balance between these effects turns out to be delicate. We show that the MDL principle identifies the true partition among models whose relative block sizes are bounded away from zero. The results are extended to models with Poisson-distributed edge weights.
An Empirical Optimization to Logistic Classification Model

Recently, the scientific community has been proposing several automatic algorithms to biomedical image segmentation procedure, being an interesting and helpful approach to assist both technicians and radiologists in this time-consuming and subjective task. One of these interesting and widely used image segmentation method could be the voxel intensity-based algorithms, e.g. image histogram threshold methods, which have been intensively improved in the past decades. Recently, an interesting approach that gained focus is the logistic classification (LC) for object detection in biomedical images. Even though the general concept behind the LC method is fairly known, the proper method’s optimization still commonly adjusted by hand which naturally adds a level of uncertainty and subjectivity in the general segmentation performance. Therefore, an empirical LC optimization is presented, offering a ITK class that performs the LC parameters optimization based on empirical input data analysis. It is worth mentioning that the LogisticContrastEnhancementImageFilter class showed here is also applied on others computational problems, being briefly explained in this document.
Interpolation-Based Smart Video Stabilization

Video stabilization is the process of eliminating unwanted camera movements and shaking in a recorded video. Recently, learning-based video stabilization methods have become very popular. Supervised learning-based approaches need labeled data. For the video stabilization problem, recording both stable and unstable versions of the same video is quite troublesome and requires special hardware. In order to overcome this situation, learning-based interpolation methods that do not need such data have been proposed. In this paper, we review recent learning-based interpolation methods for video stabilization and discuss the shortcomings and potential improvements of them.
Visually Communicating Microgrid Complexity

Data visualisation has proven to help support engagement and understanding of complex processes; however, few visualisations of microgrid energy systems exist. In this research we explore the area of microgrid visualisation, motivated by the establishment of a microgrid at Monash University, Australia. Using the Monash Microgrid as a case study, we follow a user-centred visualisation design process. Interviewing diverse microgrid stakeholders enabled the gathering of data and visualisation requirements and allowed us to identify key visual tasks. These were prioritised based on feasibility. A digital prototype was designed and developed for a large screen display. The prototype was formally evaluated with stakeholder participants and suggestions for future work were collected. The microgrid experts deemed the proposed prototype effective for their needs, with further potential in the energy domain.
Online product allocation in poultry batchers with lookahead

Poultry processing plants utilize batchers to produce products, aiming to minimize operational costs while adhering to a target throughput imposed by their customers. This paper studies the production control of such batchers, leading to an online, time-constrained, and bi-objective decision problem. To solve this problem, we introduce a genetic algorithm equipped with local search and utilize a novel cost prediction function. This hybrid genetic algorithm (HGA) is shown to be competitive with complete enumeration approaches, and outperforms other heuristics in small-scale experiments. We also conduct a numerical analysis that evaluates the sensitivity of costs and throughput to the problem setting. Finally, an industry case study shows that HGA significantly reduces costs, and nearly eliminates deviations from the imposed throughput compared to current practice.
Automatic Navigation Research for Multi-directional Mobile Robots on the Basis of Artificial Intelligence Application, Q-Learning Algorithm

In this article, research on the applications of artificial intelligence in implementing reinforcement learning is a subset of machine learning that deals with learning decisions from rewards given by the environment. Classic reinforcement learning algorithms are usually applied to small sets of states and actions. However, in real applications, the state spaces are of a large scale and this will bring the problems of the generalization and the curse of dimensionality. In this paper, we integrate neural network into reinforcement learning methods to generalize the value of all the states. Simulation results on the Gazebo framework show the feasibility of the proposed method. The robot can complete navigation tasks safely in an unpredicted dynamic environment and becomes a truly intelligent system with strong self-learning and adaptive abilities.
An improved GAN-based approach for image inpainting

Image inpainting aims to complete missing regions in images, effectively serves imagery processes like historical image restoration or photo editing. This task is challenging because the completion should maintain visual coherence throughout the image. This paper’s contribution lies in an architecture that comprises multiple generators and discriminators to achieve better inpainting results. The two generators work sequentially, in which the first model coarsely reconstructs the missing regions, and the latter completes these regions following the given prior knowledge. Meanwhile, the discriminator stage includes two parallel, global and local branches, allowing for more significant discrimination. We further suggest using dilated convolution, which effectively broadens the receptive field, and WGAN-GP to mitigate gradient vanishing. Both quantitative and qualitative experiments on standard datasets have shown that our method provides more plausible results than current baselines.
Data-Driven Learning Teaching Model of College English Based on Mega Data Analysis

With the arrival of the mega data era, CET has developed a new teaching model based on mega data. This teaching requirement is met by a data-driven model based on mega data. By applying it to CET, students will be guided to explore and discover language rules and pragmatic features through quantitative analysis of data-driven technology, which will help compensate for the disadvantages of traditional English teaching and improve students’ autonomous learning ability. The concept of data-driven language learning is introduced into teaching in this paper. Corpus is tried to stimulate students’ autonomous learning in the teaching process, and the independent learning model of students is further improved in the teaching reform, based on educational theories of corpus linguistics and second language acquisition linguistics. Students’ scores have improved, particularly in English listening, according to the findings. The data-driven CET-4 model improves students’ learning ability and interest, as well as their ability to think creatively and critically.
Big Data Analysis for Predicting Post-Earthquake Municipal Road Recovery

Post-disaster road recovery is important.
Optimal squeezing for quantum target detection

It is not clear if the performance of a quantum lidar or radar, without an idler and only using Gaussian resources, could exceed the performance of a semiclassical setup based on coherent states and homodyne detection. Here we prove this is indeed the case by showing that an idler-free squeezed-based setup can beat this semiclassical benchmark. More generally, we show that probes whose displacement and squeezing are jointly optimized can strictly outperform coherent states with the same mean number of input photons for both the problems of quantum illumination and reading.
Machine Learning Based Automatic Sport Event Detection and Counting

Sport event detection is an important task in the research area of human behavior recognition. Owing to different motion models of different sport events, existing general human pose recognition methods cannot achieve high accuracy for sport events detection and counting. In this paper, we propose and implement a sport event detection and counting algorithm framework based on human skeletal information. Experimental evaluation results demonstrate that the algorithm can accurately detect the sit-up events and count the number of sit-ups with the highest average accuracy of 96%.
Mineralogy of the Vorontsovskoe gold deposit (Northern Urals). Part 3: sulfosalts

This paper continues a series of publications devoted to the mineralogy of the Vorontsovskoe gold deposit in the Northern Urals (Kasatkin et al., 2020, 2021). The article reports on sulfosalts of the deposit, their chemical compositions, as well as unit-cell parameters, optical properties and Raman spectra of some minerals. For eight new mineral species discovered by the authors (auerbakhite, vorontsovite, gladkovskyite, gungerite, luborzakite, pokhodyashinite, ferrovorontsovite, tsygankoite), we provide their density, hardness, refectance spectra and crystal structure. Arsiccioite, benavidesite, bernardite, boscardinite, veenite, weissbergite, vrbaite, heptasartorite, heteromorphite, guettardite, gillulyite, dalnegroite, drechslerite, sicherite, imhofte, christite, cupropolybasite, lafttite, lorandite, manganoquadratite, nowackiite, oyonite, parapierrotite, rebulite, roshchinite, twinnite, philrothite, hutchinsonite, stalderite, ecrinsite and enneasartorite are found for the frst time in Russian Federation.