Reviewerly/exHarmony huggingface dataset:
Long Time No See! Open-Domain Conversation with Long-Term Persona Memory

Most of the open-domain dialogue models tend to perform poorly in the setting of long-term human-bot conversations.The possible reason is that they lack the capability of understanding and memorizing long-term dialogue history information.To address this issue, we present a novel task of Long-term Memory Conversation (LeMon) and then build a new dialogue dataset DuLeMon and a dialogue generation framework PLATO-LTM with a Long-Term Memory (LTM) mechanism.This LTM mechanism enables our system to accurately extract and continuously update long-term persona memory without requiring multiple-session dialogue datasets for model training.To our knowledge, this is the first attempt to conduct real-time dynamic management of persona information of both parties, including the user and the bot.Results on DuLeMon indicate that PLATO-LTM can significantly outperform baselines in terms of long-term dialogue consistency, leading to better dialogue engagingness 1 .
Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective. By overlapping the images, the DPN uses the addition of small convolution, deconvolution, and attention mechanisms to enhance the model’s ability to classify the texture features of pathological images on the BreaKHis dataset. The parameters that are taken into consideration for measuring the accuracy of the proposed model are false-positive rate, false-negative rate, recall, precision, and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> <mn>1</mn> </math> score. Several experiments are carried out over the selected parameters, such as making comparisons between benign and malignant classification accuracy under different normalization methods, comparison of accuracy of image level and patient level using different CNN models, correlating the correctness of DPN68-A network with different deep learning models and other classification algorithms at all magnifications. The results thus obtained have proved that the proposed model DPN68-A network can effectively classify the benign and malignant breast cancer pathological images at various magnifications. The proposed model also is able to better assist the pathologists in diagnosing the patients by synthesizing the images of different magnifications in the clinical stage.
Per-run Algorithm Selection with Warm-Starting Using Trajectory-Based Features

Per-instance algorithm selection seeks to recommend, for a given problem instance and a given performance criterion, one or several suitable algorithms that are expected to perform well for the particular setting. The selection is classically done offline, using openly available information about the problem instance or features that are extracted from the instance during a dedicated feature extraction step. This ignores valuable information that the algorithms accumulate during the optimization process. In this work, we propose an alternative, online algorithm selection scheme which we coin as “per-run” algorithm selection. In our approach, we start the optimization with a default algorithm, and, after a certain number of iterations, extract instance features from the observed trajectory of this initial optimizer to determine whether to switch to another optimizer. We test this approach using the CMA-ES as the default solver, and a portfolio of six different optimizers as potential algorithms to switch to. In contrast to other recent work on online per-run algorithm selection, we warm-start the second optimizer using information accumulated during the first optimization phase. We show that our approach outperforms static per-instance algorithm selection. We also compare two different feature extraction principles, based on exploratory landscape analysis and time series analysis of the internal state variables of the CMA-ES, respectively. We show that a combination of both feature sets provides the most accurate recommendations for our test cases, taken from the BBOB function suite from the COCO platform and the YABBOB suite from the Nevergrad platform.
Neural graph embeddings as explicit low-rank matrix factorization for link prediction

Learning good quality neural graph embeddings has long been achieved by minimzing the pointwise mutual information (PMI) for co-occuring nodes in simulated random walks. This design choice has been mostly popularized by the direct application of the highly-successful word embedding algorithm word2vec to predicting the formation of new links in social, co-citation, and biological networks. However, such a skeuomorphic design of graph embedding methods entails a truncation of information coming from pairs of nodes with low PMI. To circumvent this issue, we propose an improved approach to learning low-rank factorization embeddings that incorporate information from such unlikely pairs of nodes and show that it can improve the link prediction performance of baseline methods from 1.2% to 24.2%. Based on our results and observations, we outline further steps that could improve the design of next graph embedding algorithms that are based on matrix factorizaion.
Deep learning based sentiment analysis of public perception of working from home through tweets

Nowadays, we are witnessing a paradigm shift from the conventional approach of working from office spaces to the emerging culture of working virtually from home. Even during the COVID-19 pandemic, many organisations were forced to allow employees to work from their homes, which led to worldwide discussions of this trend on Twitter. The analysis of this data has immense potential to change the way we work but extracting useful information from this valuable data is a challenge. Hence in this study, the microblogging website Twitter is used to gather more than 450,000 English language tweets from 22nd January 2022 to 12th March 2022, consisting of keywords related to working from home. A state-of-the-art pre-processing technique is used to convert all emojis into text, remove duplicate tweets, retweets, username tags, URLs, hashtags etc. and then the text is converted to lowercase. Thus, the number of tweets is reduced to 358,823. In this paper, we propose a fine-tuned Convolutional Neural Network (CNN) model to analyse Twitter data. The input to our deep learning model is an annotated set of tweets that are effectively labelled into three sentiment classes, viz. positive negative and neutral using VADER (Valence Aware Dictionary for sEntiment Reasoning). We also use a variation in the input vector to the embedding layer, by using FastText embeddings with our model to train supervised word representations for our text corpus of more than 450,000 tweets. The proposed model uses multiple convolution and max pooling layers, dropout operation, and dense layers with ReLU and sigmoid activations to achieve remarkable results on our dataset. Further, the performance of our model is compared with some standard classifiers like Support Vector Machine (SVM), Naive Bayes, Decision Tree, and Random Forest. From the results, it is observed that on the given dataset, the proposed CNN with FastText word embeddings outperforms other classifiers with an accuracy of 0.925969. As a result of this classification, 54.41% of the tweets are found to show affirmation, 24.50% show a negative disposition, and 21.09% have neutral sentiments towards working from home.
A path planning algorithm for autonomous flying vehicles in cross-country environments with a novel TF-RRT∗ method

Autonomous flying vehicles (AFVs) are promising future vehicles, which have high obstacle avoidance ability. To plan a feasible path in a wide range of cross-country environments for the AFV, a triggered forward optimal rapidly-exploring random tree (TF-RRT∗) method is proposed. Firstly, an improved sampling and tree growth mechanism is built. Sampling and tree growth are allowed only in the forward region close to the target point, which significantly improves the planning speed; Secondly, the driving modes (ground-driving mode or air-driving mode) of the AFV are added to the sampling process as a planned state for uniform planning the driving path and driving mode; Thirdly, according to the dynamics and energy consumption models of the AFV, comprehensive indicators with energy consumption and efficiency are established for path optimal procedures, so as to select driving mode and plan driving path reasonably according to the demand. The proposed method is verified by simulations with an actual cross-country environment. Results show that the computation time is decreased by 71.08% compared with Informed-RRT∗ algorithm, and the path length of the proposed method decreased by 13.01% compared with RRT∗-Connect algorithm.
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The applications of random quantum circuits range from quantum computing and quantum many-body systems to the physics of black holes. Many of these applications are related to the generation of quantum pseudorandomness: Random quantum circuits are known to approximate unitary $t$-designs. Unitary $t$-designs are probability distributions that mimic Haar randomness up to $t$th moments. In a seminal paper, Brand\~{a}o, Harrow and Horodecki prove that random quantum circuits on qubits in a brickwork architecture of depth $O(n t^{10.5})$ are approximate unitary $t$-designs. In this work, we revisit this argument, which lower bounds the spectral gap of moment operators for local random quantum circuits by $\Omega(n^{-1}t^{-9.5})$. We improve this lower bound to $\Omega(n^{-1}t^{-4-o(1)})$, where the $o(1)$ term goes to $0$ as $t\to\infty$. A direct consequence of this scaling is that random quantum circuits generate approximate unitary $t$-designs in depth $O(nt^{5+o(1)})$. Our techniques involve Gao's quantum union bound and the unreasonable effectiveness of the Clifford group. As an auxiliary result, we prove fast convergence to the Haar measure for random Clifford unitaries interleaved with Haar random single qubit unitaries.
Performance Analysis of Energy Production of Large-Scale Solar Plants Based on Artificial Intelligence (Machine Learning) Technique

Due to the continual fusion reaction, the sun generates tremendous energy. This solar energy is freely available and can be extracted by installing a large-scale solar power plant. Therefore, such PV solar plants are key contributors to cutting the energy deficit in remote areas. This study focused on predicting a 10-year performance analysis of a large-scale solar power plant by using 1 year of real-time data from the Quaid-e-Azam Solar Park (QASP) situated in Bahawalpur, Pakistan. For the purpose of prediction, the ARIMA model was developed using Python, which is one of the best tools in machine learning. Since ARIMA is a statistical technique for prediction, by using the developed model through Python, we predicted the values of the performance ratio (PR), production amount (MWh), and plan of array (POA) of the solar plant for the next 10 years using 1 year of real-time data. This machine learning prediction technique is very effective and efficient, compared with other traditional prediction and forecasting techniques, for estimating the performance of the solar power plant and the status of the solar power plant in the long-term future. The forecasting/prediction results acquired from the process show that power production during the next ten years increases to approximately 400 MW and that POA will grow from 6.8 to 7.8 W/m2. However, a decline occurred in the performance ratio, which decreased from 76.7% to 73%. Based on these results, the ARIMA model for predicting solar power generation is effective and can be utilized for any solar power plant.
Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble

The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this area. However, lack of dictionaries, unannotated data, large-scale unstructured data, and low accuracies have plagued these approaches. Also, sentiment classification through classifier ensemble has been underexplored in literature. In this article, we propose a Semantic Relational Machine Learning (SRML) model that automatically classifies the sentiment of tweets by using classifier ensemble and optimal features. The model employs the Cascaded Feature Selection (CFS) strategy, a novel statistical assessment approach based on Wilcoxon rank sum test, univariate logistic regression assisted significant predictor test and cross-correlation test. It further uses the efficacy of word2vec-based continuous bag-of-words and n-gram feature extraction in conjunction with SentiWordNet for finding optimal features for classification. We experiment on six public Twitter sentiment datasets, the STS-Gold dataset, the Obama-McCain Debate (OMD) dataset, the healthcare reform (HCR) dataset and the SemEval2017 Task 4A, 4B and 4C on a heterogeneous classifier ensemble comprising fourteen individual classifiers from different paradigms. Results from the experimental study indicate that CFS supports in attaining a higher classification accuracy with up to 50% lesser features compared to count vectorizer approach. In Intra-model performance assessment, the Artificial Neural Network-Gradient Descent (ANN-GD) classifier performs comparatively better than other individual classifiers, but the Best Trained Ensemble (BTE) strategy outperforms on all metrics. In inter-model performance assessment with existing state-of-the-art systems, the proposed model achieved higher accuracy and outperforms more accomplished models employing quantum-inspired sentiment representation (QSR), transformer-based methods like BERT, BERTweet, RoBERTa and ensemble techniques. The research thus provides critical insights into implementing similar strategy into building more generic and robust expert system for sentiment analysis that can be leveraged across industries.
Teachers’ Perceptions of Remote Learning during the Pandemic: A Case Study

The closure of higher education institutions (HEIs) due to the outbreak of the COVID-19 pandemic led to visible changes in pedagogical practices. With the lockdown, there was ambiguity and disagreement about the workload of teachers and students, and about what to teach and what strategies to select. For most instructors, the first challenge was to recreate the face-to-face experience. Worldwide, most universities have speedily adopted synchronous and asynchronous communication modes. Google Classroom, Microsoft Teams, Cisco, Webex, Zoom, and Moodle were among the most used tools. The present study is based upon a quantitative approach, and it intends to analyse teachers’ perceptions of remote teaching during the first pandemic period. Data were collected through an online questionnaire during June and July 2020. The questionnaire had 27 questions divided into three main sections: sociodemographic characterization, e-Learning strategies, and remote assessment. The study population was teachers of a Portuguese HEI. A random sample was used with 547 participants. The main conclusions show that the less experienced teachers are, the more satisfied they feel with remote classes and remote assessment. On the other hand, the most experienced teachers used more tools during the remote teaching period and developed more strategies to perform remote assessment. Regarding the overall assessment of the emergency remote teaching, the participants consider that it was a positive period, and they were moderately satisfied with remote classes and the strategies and tools used during this period.
Adaptive multi-UAV path planning method based on improved gray wolf algorithm

• Aiming at the problem of slow convergence in the gray wolf algorithm, a spiral position update method is introduced. And set the probability p of choosing the update method as the golden ratio. • In the original gray wolf algorithm, the number of leadership layers is fixed at 3, which makes the algorithm weaker in search ability. In the iterative process, an adaptive leadership hierarchy was added, including two parts: increasing the leadership hierarchy and decreasing the leadership hierarchy. • Improved the convergence speed of the algorithm and the efficiency of completing the task of the UAV . Due to the slow convergence and insufficient flight path in path planning, we proposes an adaptive multi-UAV path planning method (AP-GWO) that improves the gray wolf algorithm. The spiral update position method is introduced using the whale algorithm as reference, while the probability of selecting the update method is set to the golden ratio of 0.618. Afterwards, in the iterative process, a different number of leadership levels is used to update the position of the individual, and the leadership is adjusted using an adaptive mechanism. The number of strata balances the process of encirclement and attack. The experimental results show that the proposed AP-GWO method can shorten the flight time of the UAV by an average of 22.8%, shorten the convergence time of the algorithm, and make the flight path of the UAV smoother.
Adaptive multi-channel Bayesian graph attention network for IoT transaction security

With the rapid advancement of 5G technology, the Internet of Things (IoT) has entered a new phase of application and is rapidly becoming a significant force in promoting economic development. Due to the vast amounts of data created by numerous 5G IoT devices, the Ethereum platform has become a tool for the storage and sharing of IoT device data, thanks to its open and tamper-resistant characteristics. So, Ethereum account security is necessary for the Internet of Things to grow quickly and improve people's lives. By modeling Ethereum transaction records as a transaction network, the account types are well identified by the Ethereum account classification system established based on Graph Neural Networks (GNNs). This work first investigates the Ethereum transaction network, Surprisingly, experimental metrics reveal that the Ethereum transaction network is neither optimal nor even satisfactory in terms of accurately representing transactions per account. This flaw may significantly impede the classification capability of GNNs, which is mostly governed by their attributes. This work proposes an Adaptive Multi-channel Bayesian Graph Attention Network (AMBGAT) for Ethereum account classification to address this difficulty. AMBGAT uses attention to enhance node features, estimate graph topology structure that conforms to the ground truth, and efficiently extract node features pertinent to downstream tasks. An extensive experiment with actual Ethereum transaction data demonstrates that AMBGAT obtains competitive performance in the classification of Ethereum accounts while accurately anticipating the graph's topology.
Research on short-term photovoltaic power prediction based on multi-scale similar days and ESN-KELM dual core prediction model

Photovoltaic (PV) power generation has emerged as an essential means of developing and utilizing new energy. Accurate PV power prediction is critical for building a new power system generation and guaranteeing system stability when a high proportion of renewable energy is connected. Therefore, this research proposes a hybrid prediction method based on multi-scale similar days and ESN-KELM dual-kernel prediction to increase the prediction accuracy of PV power generation. First, the multi-scale similar days algorithm is used to determine similar days of the forecast day as the model training data. This operation can reduce the impact of the randomness of PV power output on the model performance. Second, the hidden features of PV power are mined using a fast iterative filter decomposition method. Based on the complexity of the components, the corresponding ESN-KELM dual-kernel prediction models are established. An improved Archimedes optimization approach is used to optimize the ESN-KELM model's parameters. Next, the predicted power is obtained by aggregating the predicted results of each component. Ultimately, the method is validated using historical operational data from PV power plants. The results indicate that the proposed model can achieve well prediction results for various seasons and weather conditions.
Enabling Conversational Interaction with Mobile UI using Large Language Models

Conversational agents show the promise to allow users to interact with mobile devices using language. However, to perform diverse UI tasks with natural language, developers typically need to create separate datasets and models for each specific task, which is expensive and effort-consuming. Recently, pre-trained large language models (LLMs) have been shown capable of generalizing to various downstream tasks when prompted with a handful of examples from the target task. This paper investigates the feasibility of enabling versatile conversational interactions with mobile UIs using a single LLM. We designed prompting techniques to adapt an LLM to mobile UIs. We experimented with four important modeling tasks that address various scenarios in conversational interaction. Our method achieved competitive performance on these challenging tasks without requiring dedicated datasets and training, offering a lightweight and generalizable approach to enable language-based mobile interaction.
Sketch-Guided Text-to-Image Diffusion Models

Text-to-Image models have introduced a remarkable leap in the evolution of machine learning, demonstrating high-quality synthesis of images from a given text-prompt. However, these powerful pretrained models still lack control handles that can guide spatial properties of the synthesized images. In this work, we introduce a universal approach to guide a pretrained text-to-image diffusion model, with a spatial map from another domain (e.g., sketch) during inference time. Unlike previous works, our method does not require to train a dedicated model or a specialized encoder for the task. Our key idea is to train a Latent Guidance Predictor (LGP) - a small, per-pixel, Multi-Layer Perceptron (MLP) that maps latent features of noisy images to spatial maps, where the deep features are extracted from the core Denoising Diffusion Probabilistic Model (DDPM) network. The LGP is trained only on a few thousand images and constitutes a differential guiding map predictor, over which the loss is computed and propagated back to push the intermediate images to agree with the spatial map. The per-pixel training offers flexibility and locality which allows the technique to perform well on out-of-domain sketches, including free-hand style drawings. We take a particular focus on the sketch-to-image translation task, revealing a robust and expressive way to generate images that follow the guidance of a sketch of arbitrary style or domain.