Reviewerly/exHarmony huggingface dataset:
Addressing Different Goal Selection Strategies In Hindsight Experience Replay With Actor-Critic Methods For Robotic Hand Manipulation

One of the most challenging problems in reinforcement learning is dealing with minimal rewards obtained from an environment. We present a combined technique of Twin Delayed Deep Deterministic Policy Gradient known as TD3, an off-policy Reinforcement Learning algorithm with Hindsight Experience Replay (HER). This combined technique allows for sampleefficient learning from sparse and binary rewards and avoids the need for complicated reward engineering. We use the challenge of moving things with a robotic arm to illustrate our methodology. We specifically tested six different tasks: pushing, sliding, picking up and placing in the Fetch environment, as well as manipulating a block, an egg, or a pen with our hands. We solely use binary rewards every time to indicate whether or not a task has been performed. In a comparative study, we primarily concentrate on the impact of various goal selection strategies of HER replay butter on both DDPG and TD3. We discovered that HER was crucial in enabling training in these demanding situations.
A RNN Based Self-Learning Audio Generating Chatbot For French Language Learners

A chatbot is an artificial intelligence-based virtual conversational agent that is one of the most potential technologies in the modern era. There are so many sectors where chatbots are providing outstanding performance. This technology has the ability to be an assistant for those who are trying to learn a new language. That is why we have done research to make a chatbot based on an RNN algorithm in order to help people learn French. Although there are many online platforms available to learn French, many of these are not sufficient enough to learn a language as it is mandatory to have a conversational partner to practice a language. Without practicing a language with anyone, it is difficult for a person to keep memorizing a lot of vocabulary. French pronunciation rules are not the same as English. Therefore, we have added a feature for sending text along with audio messages and getting replies in French audio alongside text. Because of having these features, users will not only know the French vocabulary and grammatical rules but can also be introduced to French pronunciation systems and learn to speak French with a native French accent. The self-learning feature assists this chatbot to increase its capability of giving correct responses to the users day by day by having continuous conversations and removes the problem of collecting so much data to make this kind of chatbot. Also the paraphrasing feature will help users to get different replies to a particular question at different times, enriching their vocabulary and familiarizing them with the variety of replies to a particular question.
Optimising Faster R-CNN Training to Enable Video Camera Compression for Assisted and Automated Driving Systems

Advanced driving assistance systems based on only one camera or one RADAR are evolving into the current assisted and automated driving functions delivering SAE Level 2 and above capabilities. A suite of environmental perception sensors is required to achieve safe and reliable planning and navigation in future vehicles equipped with these capabilities. The sensor suite, based on several cameras, LiDARs, RADARs and ultrasonic sensors, needs to be adequate to provide sufficient (and redundant, depending on the level of driving automation) spatial and temporal coverage of the environment around the vehicle. However, the data amount produced by the sensor suite can easily exceed a few tens of Gb/s, with a single ‘average’ automotive camera producing more than 3 Gb/s. It is therefore important to consider leveraging traditional video compression techniques as well as to investigate novel ones to reduce the amount of video camera data to be transmitted to the vehicle processing unit(s). In this paper, we demonstrate that lossy compression schemes, with high compression ratios (up to 1:1,000) can be applied safely to the camera video data stream when machine learning based object detection is used to consume the sensor data. We show that transfer learning can be used to re-train a deep neural network with H.264 and H.265 compliant compressed data, and it allows the network performance to be optimised based on the compression level of the generated sensor data. Moreover, this form of transfer learning improves the neural network performance when evaluating uncompressed data, increasing its robustness to real world variations of the data.
Proposal of a Swimming Pool Drowning Detection System using Cameras and Raspberry Pi based on Machine Learning

Drowning deaths represent the third leading cause of accidental deaths worldwide. This is because traditional techniques for the supervision and care of people, especially children, in large pools are inefficient or, in some cases, nonexistent. Nowadays, this problem has become a topic of interest for several researchers who seek to propose different methods of drowning detection. This research work seeks to propose the process to be followed to develop a drowning detection system in swimming pools using cameras and Raspberry Pi based on Machine Learning. To achieve the objective, the use of the Triple Diamond design methodology was proposed. In the development of the first diamond, the information was organized in a Lotus Blossom Diagram, then the problematic situation and the main objective were described. In the development of the second diamond, a bibliometric analysis was performed, searching for information with search equations and then sorting and filtering it, and finally including it in morphological matrices. As a result, an electrical diagram of the system and a flow diagram of the algorithm based on a Support Vector Machine were proposed.
Non-Uniform Quantization and Pruning Using Mu-law Companding

Contemporary deep learning models require high computation costs and large model sizes to realize high accuracy, which is not suitable for limited hardware resources such as mobile or edge devices. Model compression methods such as a quantization that reduces the precision of weights or activation and pruning that removes unimportant nodes have been proposed. However, these methods degrade the accuracy significantly. To overcome this limitation, we propose a non-uniform quantization and pruning method using mu-law companding, which preserves accuracy while simultaneously performing pruning and quantization. Experimental results using the ResNet-18 model on the ILSVRC2012 dataset showed that the accuracy increased by 0.4% in 5-bit and 0.3% in 4-bit, compared to FP32. In addition, we identified areas containing important information by changing the quantization interval, and visually demonstrate why the quantization model outperformed FP32 with Grad-CAM++.
Performance Comparison of PSO-based Informative Path Planners for Water Monitoring under Dynamic Scenarios

The monitoring of water resources as an action of prevention and control of water quality is an ongoing case study. Monitoring can be performed using autonomous surface vehicles capable of measuring water quality parameters through in-vehicle sensors. This work focuses on comparing PSO-based informative path planners for water resource pollution peak detection with autonomous surface vehicles. The scenarios that are used for the comparison of the behavior of the algorithms are dynamic scenarios. In other words, the water resource contamination peaks are able to change position and size over time. The results show that the Enhanced GP-based PSO with exploring approach obtains the lowest error in detecting water resource contamination peaks with dynamic scenarios.
Analysis of Weather Change Using Himawari-8 Satellite Image with 24-Hours Microphysics RGB and Convective Available Potential Energy Method (Study Case: Flood Central Kalimantan 18-24 August 2021)

Rain with light heavy intensity hit Central Kalimantan Province starting on Thursday (19/8/2021), and submerged 13 sub-districts in Katingan Regency, Central Kalimantan. The study analyzed the weather during heavy rains using reanalyzed data from Copernicus ECMWF, namely Convective Available Potential Energy (CAPE) which was then processed into a map and supported by data from the Himawari-8 satellite to analyze Streamline, Time series, 24-Hours Microphysics RGB starting on the 18th. −24 August 2021. On 18 August 2021 Cumulonimbus clouds were detected in the Katingan Regency area, with the highest cloud top temperature reaching −67°C whereas when the temperature was above −60 °C, this indicates that these clouds are Cumulonimbus convective clouds with a high peak, and the highest CAPE value is 1001–1500 J/kg which means it is categorized as moderate energy. On August 19, 2021, Cumulonimbus clouds were detected, the highest cloud top temperature reached −70°C, and the CAPE value was 1501-2100 J/kg which means it is categorized as moderately strong energy. On August 20, 2021, Cumulonimbus clouds were detected, the highest cloud top temperature reached −78°C, and the CAPE value was 2101-3545.6 J/kg which means it is categorized as strong energy. On August 21, there were thin Cumulonimbus clouds that did not spread evenly, the highest cloud top temperature reached −38°C where the temperature was already below the freezing level and already contained cloud crystals, and the CAPE value in most areas of Katingan Regency was 200–400 J/kg which means it is categorized as weak energy. On August 22, 2021, Cumulonimbus clouds were detected, the highest cloud top temperature reached −77°C, and the CAPE value was 701–1400 J/kg which means it is categorized as moderately weak energy. On August 23, there were no Cumulonimbus clouds in the Katingan Regency area, the highest cloud top temperature reached −37°C, and the CAPE value in most areas of the Katingan Regency was 0-600 J/kg which means it is categorized as weak energy. On August 24, 2021, Cumulonimbus clouds were detected in the Katingan Regency area, the highest cloud top temperature reached −78°C, and the CAPE value in the Katingan Regency area was 2102-3474.5 J/kg which means it is categorized as strong energy. Streamline analysis dated 18,19,20,22,24 in the 850 mb layer above the Katingan Regency area, there is a Shearline. Meanwhile, on 21 and 23 August 2021, the wind over Katingan Regency looked straight and there were no very significant turns so on 21 and 23 August 2021 there was no formation or formation of very thin convective clouds.
End-to-End Video Quality Assessment with Deep Neural Networks

The explosiveness of media consumption patterns over the last years has brought a new landscape of high competition that forces the involved companies to worry about the quality of service of the end-user. However, current methods fail to measure the quality of experience in a manner close to that of the end-user. This work presents an artificial intelligence-based system able to assess the video quality in an end-to-end manner and in a similar way to what a user would do. For such purpose, a novel method for generating samples that reflects the quality of video signals over real network conditions has been implemented. Additionally, a hybrid neural network was developed. This hybrid neural network is comprised of convolutional and recurrent layers in charge of extracting spatial and temporal features respectively. Results show a high ability of the proposed system to generate reliable estimations. More precisely, the system can get precision values up to 80 compared to that of humans during the same task, which can go up 89. Moreover, it has been proved that such results can be achieved without the need for long training processes or large datasets.
Opinion Mining and Sentiment Analysis on Twitter Data

Sentiment analysis on Twitter data provides organizations with a quick and effective method to monitor the general public's sentiments about a brand, business, leaders, and so on. In recent years, a wide range of parameters and techniques for training sentiment analyzers for Twitter datasets has been researched, with various outcomes. To perform sentiment analysis on tweets, our proposed system extracts the posts from Twitter and analyzes tweet's sentiments towards products and services. In this paper, we used Long-Short Term Memory (a type of recurrent neural network) for Text Classification as it effectively enhances performance by memorizing vital information and detecting patterns. Using LSTM, we implemented an algorithm for automatic classification of text/tweets into six different sentiment classes, which can be extended further depending on the requirements. Our results showed a precision of 92.17% on testing the model based on test sequences and test labels and we visually expressed the sentiments in the form of a Pie-Chart.
Implementation of Noise-Resistant Crowd Equalisation in Optical Communication Systems with Machine Learning DSP

We propose a solution to noisy neural networks employed in future optical communication systems. The proposed approach includes breaking down large networks into smaller ones and forming "crowds" using these elementary networks.
Random Forest Regression Model AugmenteD With KL Algorithm (RFRKL) To Minimize Power In FSM Synthesis

The logic synthesis domain has recently benefited from improvements in intelligent learning, particularly for low power minimization. This research employed AI with the classic Traveling Salesman Problem (TSP) to provide a low-power synthesis. Initially, from an input State Transition Graph (STG), a Markov process probabilistic model is formed and the next KL algorithm is applied using the partitioning method where it calculates the Weighted Hamming Distance (WHD). To find the minimum power with respect to proper state encoding using SIS tool is a NP-Hard problem and rather time consuming. When the database has been generated, a Random-Forest regression model is trained to replace the SIS tool in order to predict the power for multi-level realizations based on the database. The model is even more effective at making predictions, with a maximum test error of 4.584% and an average test error of 0.799 %. According to the results, the augmented-based framework accelerates the process on average 1.5 times faster than the traditional simulation-based framework.
Rapid Configuration of Optical Processor Based on Reconfigurable Multiport Interferometer

We demonstrate an optimization method for universal multiport interferometers to perform rapid configuration reconstruction. This method uses deep learning to model the relationship between the transmission characteristics and the configuration parameters of the component of the reconfigurable multiport interferometers, making it resistant to manufacturing flaws of the interferometer. Our demonstration provides a potential way to implement fast and accurate reconfiguration functions for optical processors.
Bell State Measurement between Two Spectrally Multiplexed Elementary Quantum Links

We demonstrate a Bell state measurement (BSM) between two spectrally multiplexed elementary quantum links by using two time-bin photonics qubit generators with different spectral channels. A projection fidelity of >95% is achieved by erasing the spectral distinguishability between adjacent nodes of the two links with single-photon frequency shifting while the separation between spectral channels is carefully designed according to parameters of time-bin qubits.
All-Optical Nonlinear Activators Using Silicon-Based Waveguides and Microring Resonators for Photonic Neural Networks

Silicon-based waveguides and all-pass microring resonators used as the all-optical nonlinear activators for photonic neural networks are demonstrated. For the three-layer, full-connected neural network and the MNIST dataset, the recognition accuracies based on the proposed silicon-based devices all can reach more than 90%.
Deep Reinforcement Learning-based RMSA of EONs with Elastic Weight Consolidation

We study the deep reinforcement learning-based routing, modulation and spectrum assignment of the elastic optical networks. When the optical networks change, transfer learning is helpful but accompanied with catastrophic forgetting. We propose an elastic weight consolidation method, where the agent can remember the learnt policies for the previous task during the transfer learning, which will effectively reduce catastrophic forgetting. Simulation results indicate the effectiveness of our proposal.
Physical Aware Clustering Training Method for Integrated Photonic Convolution Neural Network with Nonlinear Distributed Weights

The computing power of the Photonic Convolution Neural Network (CNN) have achieved Tera-level operations per second (OPS) for supporting machine vision algorithms. However, restrained by the nonlinear characteristics of weighting devices, it is hard to realize convergent training of Photonic CNN under poor controlling precision. In this paper we proposed a Physical Aware Clustering training method where the Physical Aware Cluster Quantizer is embed with the straight through estimator (STE) algorithm for integrated Photonic CNN with quantized and nonlinear distributed weights. Experiment shows that, by employing the upgraded STE methods (namely, STE plus), the Photonic CNN using micro ring weighting bank and PAM4 controlling modules achieves the nearly 99.3% accuracy ratio for Fashion MNIST recognition task, whereas only 41.5% of that by using baseline STE training algorithms with the same physical devices.
SEACOP: SatEllite Application Capability Open Platform

Satellite applications become more diversified and complex with the development of space science and space-earth integration network. Due to the wide satellite applications, the deployment evolves from dedicated devices to unified platforms. Docker-based container has served as the application execution environment with the advanced cloud computing technology. However, how to provide the running environment, convenient operations and flexible schedule scenario for the satellite applications remain to be solved. In this paper, we demonstrate an architecture of SatEllite Application Capability Open Platform (SEACOP). SEACOP is deployed on both ground-side and space-side nodes. It provides the capabilities for various satellite applications deployment and management based on Docker and Kubernetes. To address the challenges, we leverage satellite ground link adapter for satellite-ground-link communication protocol. Users can manipulate the satellite applications and monitor the running status in real time through web portal on the ground side. Furthermore, three levels of applications, data-based, template-based and image-based applications can be deployed on SEACOP.
Improved A* Algorithm Based Path Planning for Mobile Robots

An improved obstacle expansion algorithm is proposed to address the problem of too many traversing nodes and the lack of safety and smoothness of the solved paths in the traditional algorithm path planning process. A new map is generated by expanding the obstacles in the initial map, and the path planning is carried out in the new map to reduce the number of traversing nodes and improve the solution speed; the solution path is placed in the initial map to eliminate the redundant nodes; and the smoothness is optimized by the b-sample function. Simulation results show that the method is able to generate a safe and smooth planning path compared with traditional algorithms under the same environment.
Research on The Method of Constructing Knowledge Graph of Affective Disorder Based on Multi-Source Data Fusion

Affective disorder (Depression) has the characteristics of high incidence rate, low recognition rate, high disability and death rate, and has become a serious public health problem that needs to be solved urgently. It is particularly important to use machine learning and big data mining technology to build a complete knowledge graph of affective disorders. In this paper, multi-source data fusion is used to construct the original corpus of the knowledge graph of affective disorders. In the information extraction stage, we designed a machine learning algorithm based on Hidden Markov Model (HMM). The algorithm can extract entities and entity mapping relationships from corpus to form entity database and triplet relationship database. In the knowledge fusion stage, multi-source heterogeneous knowledge fusion algorithm is used to complete entity alignment and relationship deduction. Finally, the open-source graphic database Neo4j is used as the underlying storage structure, so as to realize the visualization of the knowledge graph of affective disorders.
A Method of Topic Extraction Based on WordTag and LDA

Extracting topics from documents is a common task in the field of Natural Language Processing (NLP). Both traditional feature extraction methods and various topic models can be used for such tasks of finding key information. Latent Dirichlet Allocation (LDA) is one of the classic topic models. The recently popular deep learning pre-training model has greatly improved the effect of various NLP tasks, and the method of applying the pre-training model to downstream tasks has research value. The application of Chinese pre-trained models also requires more attempts. This paper believes that combining deep learning technology can help to improve traditional methods and find key information. Therefore, based on the deep learning knowledge tagging model WordTag, we combine it with the results of knowledge tagging and LDA topic model, and propose a topic extraction method based on word classification tagging (WordTag and Latent Dirichlet Allocation, WT-LDA). Experiments show that the method proposed in this paper is more effective than other methods of topic extraction.
Vps Real-Time Monitoring System Based on Cluster Distribution

The routine maintenance of traditional single point server software system can not meet the higher demand of contemporary intelligent information system operation and maintenance management. In order to realize real-time monitoring of cloud server running status and data, this system develops cluster management for kvm and other virtualization using the general framework, based on the Nginx service for Node.js to handle various transactions and functions of the server construction. Experimental results show that it can reduce the waste of resources caused by traditional single point server and has a good application prospect in integrating the resources of computer units.
Exploring the Effect of Adversarial Attacks on Deep Learning Architectures for X-Ray Data

As artificial intelligent models continue to grow in their capacity and sophistication, they are often trusted with very sensitive information. In the sub-field of adversarial machine learning, developments are geared solely towards finding reliable methods to systematically erode the ability of artificial intelligent systems to perform as intended. These techniques can cause serious breaches of security, interruptions to major systems, and irreversible damage to consumers. Our research evaluates the effects of various white box adversarial machine learning attacks on popular computer vision deep learning models leveraging a public X-ray dataset from the National Institutes of Health (NIH). We make use of several experiments to gauge the feasibility of developing deep learning models that are robust to adversarial machine learning attacks by taking into account different defense strategies, such as adversarial training, to observe how adversarial attacks evolve over time. Our research details how a variety white box attacks effect different components of InceptionNet, DenseNet, and ResNeXt and suggest how the models can effectively defend against these attacks.