Reviewerly/exHarmony huggingface dataset:
Speech Sentiment Analysis for Citizen's Engagement in Smart Cities' Events

Few years ago, sentiment and emotion analysis started to have attentions in different application areas, including political analytics customer services, healthcare, gaming and human computer interaction. Aiming at contributing to this area, we propose a deep learning model enabling the accurate emotion analysis of human speech voice. Our model consists of a sophisticated convolutional neural network (CNN) that is combined with RELU and pooling layers, and Fully connected layers with Softmax layer to achieve accurate sentiment and emotion analysis of speech. The proposed model was successfully tested using 40 speech recorded files containing 140,000 words, collected from different events. The model was able to correctly classify the test speech achieved an average accuracy of 90.7% when distinguishing between three emotions (Happiness, sadness, and Neural) in speech. Such model can be leveraged for the automatic analysis of speaker engagement level in events.
AdaDiag: Adversarial Domain Adaptation of Diagnostic Prediction with Clinical Event Sequences

Early detection of heart failure (HF) can provide patients with the opportunity for more timely intervention and better disease management, as well as efficient use of healthcare resources. Recent machine learning (ML) methods have shown promising performance on diagnostic prediction using temporal sequences from electronic health records (EHRs). In practice, however, these models may not generalize to other populations due to dataset shift. Shifts in datasets can be attributed to a range of factors such as variations in demographics, data management methods, and healthcare delivery patterns. In this paper, we use unsupervised adversarial domain adaptation methods to adaptively reduce the impact of dataset shift on cross-institutional transfer performance. The proposed framework is validated on a next-visit HF onset prediction task using a BERT-style Transformer-based language model pre-trained with a masked language modeling (MLM) task. Our model empirically demonstrates superior prediction performance relative to non-adversarial baselines in both transfer directions on two different clinical event sequence data sources.
Partial feedback online transfer learning with multi-source domains

Online machine learning is an effective way for observation-based learning when a static dataset is not available. However, it can be challenging in real-world applications, especially when there are missing labels in multi-class classification tasks. Although partial feedback can be applied to tackle the problem, it can make the learning process slow and limit the classification performance as the correct label information is missing when the instance is misclassified. To cope with the lack of the target domain knowledge in online learning, transfer learning can be applied to convey knowledge from one or multiple source domains to the target domain. To this end, we propose a partial feedback online transfer learning algorithm with multiple source domains (PFMSD) to transfer the knowledge learned from multi-source domains to the target domain and enhance the learning performance by exploring the correct label when there is an erroneous prediction. A mistake bound is derived for the proposed algorithm, and extensive experiments are conducted using several wildly-used benchmark datasets. The obtained results in all experiments show the superiority of the proposed algorithm over the state-of-the-art partial feedback algorithms.
Broad fuzzy cognitive map systems for time series classification

Time series classification (TSC) is a crucial and challenging problem in sequential analysis. However, most of the existing best-performing methods are time-consuming, even if coping with small-scale datasets. Broad learning systems (BLS) have shown low time complexity and high accuracy in handling various tasks and have been applied to many fields. However, none of the BLS-based methods is suitable to tackle TSC due to (1) unsuitable structure in capturing temporal information of time series; (2) low interpretability in representing the evolving relations among states along time. Thus, this paper develops a broad fuzzy cognitive map system (BFCMS) to address time series classification efficiently, which consists of the sparse autoencoder (SAE) based feature extraction block, the high-order fuzzy cognitive map (HFCM) based spatiotemporal information aggregation block, and one multilayer perceptron (MLP) based prediction layer. The feature extraction block is designed to capture the underlying core evolving patterns, and the spatiotemporal information aggregation block is developed to model the underlying causal relationships and contextual dependencies. These two blocks are designed to overcome the limitations of BLS. MLP is applied to map the feature representation to the label of time series based on the aggregated feature representations from these two blocks. In addition, BFCMS develops three incremental learning strategies for fast updating in broad expansion without a retraining procedure if the model deems to be expanded. We compared BFCMS with other state-of-the-art baselines on 26 datasets. The experimental results demonstrate the superiority of BFCMS. Concretely, BFCMS achieves a lower training cost with on-par classification accuracy. • BFCMS can balance the time complexity and accuracy of the time series classifier. • We design the feature extraction and spatiotemporal information aggregation blocks to help BLS handle TSC tasks. • BFCMS retains the advantages of BLS in flexibility.
Recognizing Ancient South Indian Language Using Opposition Based Grey Wolf Optimization

Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools.This stipulation make the dispensation period overriding, difficult and tiresome to calculate.This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network (ANN) associated with Opposition based Grey Wolf Optimization Algorithm (OGWA).It identifies the prehistoric language, signs and fonts.It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance.For adaptively determining these weights, this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization, Particle Swarm Optimization and Grey Wolf Optimization to the ANN system.Performance results are illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques.In test case 1, the accuracy value of OGWO is 94.89% and in test case 2, the accuracy value of OGWO is 92.34%, on average, the accuracy of OGWO achieves 5.8% greater accuracy than ANN-GWO, 10.1% greater accuracy than ANN-PSO and 22.1% greater accuracy over conventional ANN technique.
Artificial Intelligence Model for Software Reusability Prediction System

The most significant invention made in recent years to serve various applications is software.Developing a faultless software system requires the software system design to be resilient.To make the software design more efficient, it is essential to assess the reusability of the components used.This paper proposes a software reusability prediction model named Flexible Random Fit (FRF) based on aging resilience for a Service Net (SN) software system.The reusability prediction model is developed based on a multilevel optimization technique based on software characteristics such as cohesion, coupling, and complexity.Metrics are obtained from the SN software system, which is then subjected to min-max normalization to avoid any saturation during the learning process.The feature extraction process is made more feasible by enriching the data quality via outlier detection.The reusability of the classes is estimated based on a tool called Soft Audit.Software reusability can be predicted more effectively based on the proposed FRF-ANN (Flexible Random Fit -Artificial Neural Network) algorithm.Performance evaluation shows that the proposed algorithm outperforms all the other techniques, thus ensuring the optimization of software reusability based on aging resilient.The model is then tested using constraint-based testing techniques to make sure that it is perfect at optimizing and making predictions.
Blockchain search engine: Its current research status and future prospect in Internet of Things network

Blockchain has recently triggered extensive attention and concern due to the flourish of cryptocurrencies and decentralized applications. It can be considered as a database, and is utilized to store a large collection of time ordered data records in data intensive application scenarios. In such cases, search engines are put forward to access the data in a blockchain storage in order to perform analysis for different purposes. However, the research on blockchain search engine is still in its initial phase and there is no systematic study about this research area. This paper tries to analyze blockchain search engines from two aspects: its current research status and future prospect in Internet of Things (IoT) domain. In the research status aspect, the paper mainly focuses on the technique division and performance comparison of blockchain search engines from different angles. To elaborate the future prospect of blockchain search engine in IoT domain, the present search works related to IoT domains are also introduced. In the remaining parts of the paper, we outline the blockchain background. Then, the search fundamentals are introduced, including the classification principles and search requirements. After that, we present a detailed review on the state-of-the-art works for blockchain search and give some problem analysis on blockchain search in IoT domains. Finally, we outline the challenges of blockchain search and give the prospect of possible research directions in future in IoT domains.
EASNet: a multiscale attention semantic segmentation network combined with asymmetric convolution

The huge scale variation of objects in scene images limits the performance of deep neural networks based on single-scale image input. To tackle the scale variation issue, we propose an improved multiscale attention network based on enhancing asymmetric convolution and strip pooling (EASNet), which uses three different inference scales. First, complementary weight masks are employed from branches of coarse scale to fine scale, to enhance predictions across scales. Second, the strip pooling module is adopted as strip pooling attention module (SPAM) in the coarser scales to produce the weight masks. It will make maximum use of the coarser scale, improving both the accuracy of boundary segmentation and the integrity of body segmentation for large targets or homogeneous objects but distributed discretely. Besides, enhancing asymmetric convolution block is designed to obtain richer and more robust feature information before SPAM. The effectiveness of EASNet experiments is validated using benchmarks (PASCAL VOC 2012 Aug and Cityscapes). The experimental results demonstrate that our method outperforms or is comparable to state-of-the-art methods by the widely used criteria mean intersection over union.
Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation

Image processing by segmentation technique is an important phase in medical imaging such as MRI. Its objective is to analyze the different tissues in human body. In research area, Fuzzy set is one of the most successful techniques that guarantees a robust classification. Spatial FCM (SFCM); one of the fuzzy c-means variants; considers spatial information to deal with the noisy images. To reduce this iterative algorithm’s execution time, a hard SIMD architecture has been planted named the Graphical Processing Unit (GPU). In this work, a great contribution has been done to diagnose, confront and implement three different parallel implementations on GPU. A parallel implementations’ extensive study of SFCM entitled PSFCM using 3 × 3 window is presented, and the experiments illustrate a significant decrease in terms of running time of this algorithm known by its high complexity. The experimental results indicate that the parallel version’s execution time is about 9.46 times faster than the sequential implementation on image segmentation. This gain in terms of speed-up is achieved on the Nvidia GeForce GT 740 m GPU.
A Dynamic Density Peak Clustering Algorithm Based on K-Nearest Neighbor

The clustering results of the density peak clustering algorithm (DPC) are greatly affected by the parameter <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>d</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> </math> , and the clustering center needs to be selected manually. To solve these problems, this paper proposes a low parameter sensitivity dynamic density peak clustering algorithm based on K-Nearest Neighbor (DDPC), and the clustering label is allocated adaptively by analyzing the distribution of K-Nearest Neighbors around each data. It reduces the parameter sensitivity and eliminates selecting the clustering centers manually from the decision graph. Through the experimental analysis and comparison of the artificial dataset and UCI dataset, the results show that the comprehensive clustering effect of DDPC is better than DPC, DBSCAN, DBC, and other algorithms.
Subgroup mining for performance analysis of regression models

Abstract Machine learning algorithms have shown several advantages compared to humans, namely in terms of the scale of data that can be analysed, delivering high speed and precision. However, it is not always possible to understand how algorithms work. As a result of the complexity of some algorithms, users started to feel the need to ask for explanations, boosting the relevance of Explainable Artificial Intelligence. This field aims to explain and interpret models with the use of specific analytical methods that usually analyse how their predicted values and/or errors behave. While prediction analysis is widely studied, performance analysis has limitations for regression models. This paper proposes a rule‐based approach, Error Distribution Rules (EDRs), to uncover atypical error regions, while considering multivariate feature interactions without size restrictions. Extracting EDRs is a form of subgroup mining. EDRs are model agnostic and a drill‐down technique to evaluate regression models, which consider multivariate interactions between predictors. EDRs uncover regions of the input space with deviating performance providing an interpretable description of these regions. They can be regarded as a complementary tool to the standard reporting of the expected average predictive performance. Moreover, by providing interpretable descriptions of these specific regions, EDRs allow end users to understand the dangers of using regression tools for some specific cases that fall on these regions, thaṯ is, they improve the accountability of models. The performance of several models from different problems was studied, showing that our proposal allows the analysis of many situations and direct model comparison. In order to facilitate the examination of rules, two visualization tools based on boxplots and density plots were implemented. A network visualization tool is also provided to rapidly check interactions of every feature condition. An additional tool is provided by using a grid of boxplots, where comparison between quartiles of every distribution with a reference is performed. Based on this comparison, an extrapolation of counterfactual examples to regression was also implemented. A set of examples is described, including a setting where regression models performance is compared in detail using EDRs. Specifically, the error difference between two models in a dataset is studied by deriving rules highlighting regions of the input space where model performance difference is unexpected. The application of visual tools is illustrated using EDRs examples derived from public available datasets. Also, case studies illustrating the specialization of subgroups, identification of counter factual subgroups and detecting unanticipated complex models are presented. This paper extends the state of the art by providing a method to derive explanations for model performance instead of explanations for model predictions.
Efficient methods for one-shot quantum communication

We address the question of efficient implementation of quantum protocols, with small communication and entanglement, and short depth circuit for encoding or decoding. We introduce two new methods to achieve this, the first method involving two new versions of convex-split lemma that use small amount of additional resource (in comparison to prior version) and the second method being inspired by the technique of classical correlated sampling in computer science. These lead to a series of new consequences, as follows. First, we consider the task of quantum decoupling, where the aim is to apply an operation on a n-qubit register so as to make it independent of an inaccessible quantum system. Many previous works achieve decoupling with the aid of a random unitary. It is known that random unitaries can be replaced by random circuits of size O(n\log n) and depth poly(\log n), or unitary 2-designs based on Clifford circuits of similar size and depth. We show that given any choice of basis such as the computational basis, decoupling can be achieved by a unitary that takes basis vectors to basis vectors. Thus, the circuit acts in a `classical' manner and additionally uses O(n) catalytic qubits in maximally mixed quantum state. Our unitary performs addition and multiplication modulo a prime and hence achieves a circuit size of O(n\log n) and logarithmic depth. This shows that the circuit complexity of integer multiplication (modulo a prime) is lower bounded by the optimal circuit complexity of decoupling. Next, we construct a new one-shot entanglement-assisted protocol for quantum channel coding that achieves near-optimal communication through a given channel. The number of qubits of pre-shared entanglement is exponentially smaller than that in the previous protocol near-optimal in communication. We also achieve similar results for one-shot quantum state redistribution.
A novel data visualization method for the effective assessment of cluster tendency through the dark blocks image pattern analysis

Data clustering is the most promising unsupervised technique, and it can be used to partition the data objects into different clusters. The data objects are placed according to derived similarity features using distance metrics, such as Euclidean, cosine, etc. The top clustering algorithms include k-means, and hierarchical clustering algorithms are extensively used and succeeded in many real-life applications. These algorithms are suffered from cluster tendency problems. They detect the number of clusters before the unlabelled dataset is called cluster tendency. Various pre-cluster tendency methods are studied, and it found that visual assessment of cluster tendency (VAT) solves the issue of cluster tendency. VAT cannot effectively derive the number of clusters for compact-separated (CS) datasets and obtain fewer groups for non-compact separated (non-CS) datasets. The Spectral-based implementations are recommended for the best assessment of cluster tendency, especially for the type of non-CS datasets. With this motivation, current data visualization methods are enhanced with spectral modeling to overcome the problem of cluster tendency over the non-CS datasets, which recognizes the cluster patterns efficiently with the spectral features of non-CS datasets. Comparative demonstrations were presented for the experimental study of existing and proposed data visualization methods using various performance parameters.
Multiple pathway quantum beat spectroscopy

We investigate quantum beats by monitoring cooperative emission from rubidium vapor and demonstrate correlated beats via coupled emission channels. We develop a theoretical model, and our simulations are in good agreement with experimental results. The results pave the way for advanced techniques measuring interactions between atoms that are excited to high energy levels.
What matters in adoption of small-scale rain water harvesting technologies at household level? Evidence from Charco-dam users in Nzega, Tanzania

Any effort to improve irrigation water availability has an advantage in the crop production processes. Small-scale rainwater harvesting technologies like Charco-dams (Malambo) in Swahili (the language used by most the Tanzanians) are among the interventions proven to overcome agricultural water shortage. Despite its importance in overcoming water stress in most arid and semi-arid areas of Tanzania, some farmers in the areas are still reluctant to adopt such technology. In this regard, this analyzed and discussed the factors influencing the adoption of the Charco-dam rainwater harvesting technology by smallholder vegetable producers in Nzega district, Tanzania. A survey was used to collect the required information from 528 smallholder vegetable producers in the district. A structured questionnaire was employed to collect data from 220 Charco-dam adopters and 308 non-adopters who were used for the analysis. The data collected was substantiated by six focus group discussions (FGDs); one at council headquarters and five at the village level. The Probit model with instrumental variables was used to identify and analyze factors influencing adoption. The study revealed that socioeconomic, farm level and information sharing factors are all important to improve the adoption of Charco-dam technology. The results suggest that any strategy, innovation or policy aimed at increasing the adoption rate of small-scale rainwater harvesting technologies should be designed or formulated by considering socioeconomic, farm-level, environmental, and information sharing aspects.
Bert-QAnet: BERT-encoded hierarchical question-answer cross-attention network for duplicate question detection

Community Question Answering (CQA) provides platforms for users with different backgrounds to share information and knowledge. With the increasing popularity of CQA, more and more question-answer (Q-A) pairs, with numerous duplicates, have accumulated. Therefore, many researchers focus on detecting duplicate questions in CQA. However, most existing techniques utilize only questions to solve the duplicate question detection task, while paired answers which may also contain necessary information are not considered. In this paper, we propose a BERT-encoded Hierarchical Question-Answer Cross-Attention Network for Duplicate Question Detection (Bert-QAnet) for detecting duplicate questions. Our model applies BERT to encode text and extract text features. Further, we use cross-attention to integrate word-level features both in question and answer. Also, inner attention is used to capture the interaction between question and answer. Hence, our model Bert-QAnet makes full use of semantic information in paired answers at both word-level and sentence-level. We evaluate our model on two datasets: the Yahoo! Answers dataset and the Stack Overflow dataset. To meet the special requirements of this study, both datasets are extended by paired answers. Experimental results demonstrate that our proposed model achieves state-of-the-art performance.