Reviewerly/exHarmony huggingface dataset:
Exploiting textual and relationship information for fine-grained financial sentiment analysis

Sentiment analysis aims to identify the way in which sentiments are expressed in texts. State-of-the-art approaches base their analyses solely on the given text, which complicates the detection of implicit sentiments and ignores the role of sentiment contagion. In the financial domain, sentiment could be spread across multiple platforms, such as in company and analyst reports, news articles, and microblogs. Thus, to capture implicit sentiments and the contagion process, we introduce a novel approach that leverages the text and contextual information of a record for fine-grained sentiment analysis. Based on this information, we generate a record representation, which is used in an adapted feed-forward neural network. Our proposed solution improves the performance by as much as 15% and 234% relative to multiple baselines. Our work demonstrates the impact of implicit sentiment as well as the importance of different relationships for sentiment prediction on company and analyst reports, news articles, and microblogs. For example, we identified timestamp information as being non-essential for the fine-grained sentiment analysis of company and analyst reports. Although we are able to showcase improvements in financial sentiment analysis, sentiment contagion and limited context are two common problems that continue to prevail. Therefore, by re-defining sentiment analysis as a multi-text problem, our proposed solution can be applied across multiple domains and text types, such as product reviews.
Adaptive Label Correlation Based Asymmetric Discrete Hashing for Cross-modal Retrieval

Hashing methods have captured much attention for cross-modal retrieval in recent years. Most existing approaches mainly focus on preserving the semantic similarity across heterogeneous modalities in a shared Hamming subspace, while the label information and potential correlations of multi-label semantics are not fully excavated. In this article, a novel Adaptive Label correlation based asymmEtric Cross-modal Hashing method, i.e., ALECH, is proposed for cross-modal retrieval. ALECH decomposes hash learning into two steps, hash codes learning and hash functions learning. For hash codes learning, the high-order semantic label correlations are adaptively exploited to guide the latent feature learning, while simultaneously generating the binary codes in a discrete manner. The asymmetric strategy is utilized to connect the latent feature space and Hamming space, and preserve the pairwise semantic similarity. Different from other two-step methods that directly adopt simple least-squares regression to learn hash functions based on binary codes, ALECH leverages both hash codes and semantic labels for hash functions learning which further preserves the similarity. Experiments on several benchmark datasets demonstrate that the proposed ALECH method outperforms the state-of-the-art cross-hashing methods.
Metaheuristic Ant Lion and Moth Flame Optimization-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks

In the online social networks, blogs, microblogs, social bookmarking services and sharing sites, and various web forum pages; the sharing of knowledge, opinions, ideas, etc. are spreading very quickly. This situation brings very dangerous problems in social networks. One of these problems is hate speech detection (HSD) problem which is covering issues such as insults, swearing, humiliation, discrimination, exclusion, detest, abhor, blast, damn, and intolerance. These can be reactions to a person, a group, an organization, an order, or an event. Although few machine learning methods have been used in the literature to solve this important problem in online social media, the performance of the HSD models in terms of many metrics needs to be increased. In this study, an automatic HSD system based on metaheuristic methodology was proposed for better results in this new and important problem. In the proposed optimization approach, Ant Lion Optimization (ALO) algorithm and Moth Flame Optimization (MFO) algorithm were designed for the HSD problem. This is the first attempt to use optimization algorithms as solution search strategies for automatic HSD. An efficient representation scheme and flexible fitness function were designed for this purpose. Many metrics can easily be embedded into the designed fitness function in order to be simultaneously optimized. Firstly, the basic natural language processing (NLP) steps were carried out. Feature extraction was performed using Bag of Words (BoW), Term Frequency (TF), and document vector (Word2Vec). Then, the performances of the proposed novel approaches were analyzed in detail on the three different real-world data. The obtained results were also checked against eight popular supervised machine learning algorithms, Social Spider Optimization (SSO) algorithm, and state-of-the-art Tunicate Swarm Algorithm (TSA). Considering the evaluation criteria for three sets of experiments, it was observed that the accuracy, sensitivity, precision, and f-score results of the ALO and MFO algorithms were superior to machine learning methods. As a result of the experimental studies, the highest accuracy value was 92.1% for ALO, while this value was 90.7% for MFO. Other numerical values obtained in the study were given in the experiments and results section with tables and graphics in detail. Due to the promising results of the proposed approaches, they are anticipated to be used in the solution of many social media and networking problems.
Deep Learning in Healthcare

Machine learning is quickly becoming an important tool for diagnosis and prognosis of various medical conditions. Complex input output mappings are dealt in deep learning, which is developed based on machine learning approach. Due to its efficiency and similarity to the working of the human brain, deep neural networks are a preferred method of processing and analysing medical data. In addition to diagnosis, deep learning is used to study the progression of disease, develop a personalised treatment plan and for overall patient management. This chapter discusses the architecture and working of deep neural networks and focus on its application in the detection and treatment of various diseases like cancer, diabetes, Alzheimer’s and Parkinson’s disease.
A new wrapper feature selection method for language-invariant offline signature verification

Among various biometric systems, an offline signature verification system has been widely used in all fields such as in banks, educational institutes, legal procedures and, criminal investigation where authentication and verification are utmost required. Despite the popularity of the online signature verification system, its offline counterpart still has great importance in developing countries, especially in rural areas, where easy availability of smart devices along with fast internet connection is not available. In this work, we have developed a language invariant offline signature verification model which is almost equally applicable for both writer dependent and writer independent scenarios. At first, an offline signature is collected as an image, following which a corresponding signal is generated using singular value decomposition. Then four different kinds of features namely, statistical, shape-based, similarity-based, and frequency-based are extracted from the transformed signal of the signature image. Next, to reduce the feature dimension, we have designed a novel wrapper feature selection method based on Red Deer Algorithm, a recently proposed meta-heuristic method, to keep only the relevant features to be used during signature authentication and verification process. Finally, a confidence score from the Naïve Bayes classifier has been used to perform the authentication and verification process. Our model has been evaluated on CEDAR (English), UTSig (Persian), Sigcomp 2011 Dutch, Sigcomp 2011 Chinese, and SigWIcomp 2015 Bengali signature datasets. Obtained results confirm that the proposed model can outperform many of its predecessors.
3D image recognition using new set of fractional-order Legendre moments and deep neural networks

3D Image representation is an important topic in computer vision and pattern recognition. Recently, 3D image analysis by fractional-order orthogonal moments has provided a new research direction, which has prompted researchers to think about efficient and fast classification. In this paper, the authors derived novel sets of fractional-order Legendre moment invariants (FrLMIs), for 3D object description and recognition. Therefore, an analysis of the performance of reconstruction and classification based on fractional-order moment invariants and Deep Neural Networks (DNNs) by changing the number of descriptors was presented. Accordingly, the performance of these proposed fractional-order moments and moment invariants are evaluated through several appropriate experiments, including 3D image reconstruction, region of interest feature extraction, invariance with respect to the geometric transformations and noisy, and 3D Object classification using different fractional parameters. The superiority of the proposed method is verified by comparing the classification percentages obtained by varying the amount of data used during the training process in comparison with the existing methods. The work presented will help to create new neural network architectures that take advantage of the descriptive capacity of 3D fractional-order moments. Finally, these fractional-order moments are very fast and computationally inexpensive which could be useful in many computer vision applications. Based on these characteristics, the proposed FrOLMs and FrLMIs outperformed all existing orthogonal moments.
LifeSeeker 3.0: An Interactive Lifelog Search Engine for LSC'21

In this paper, we present the interactive lifelog retrieval engine developed for the LSC'21 comparative benchmarking challenge. The LifeSeeker 3.0 interactive lifelog retrieval engine is an enhanced version of our previous system participating in LSC'20 - LifeSeeker 2.0. The system is developed by both Dublin City University and the Ho Chi Minh City University of Science. The implementation of LifeSeeker 3.0 focuses on searching and filtering by text query using a weighted Bag-of-Words model with visual concept augmentation and three weighted vocabularies. The visual similarity search is improved using a bag of local convolutional features; while improving the previous version's performance, enhancing query processing time, result displaying, and browsing support.
Multilabel Feature Selection With Constrained Latent Structure Shared Term

High-dimensional multilabel data have increasingly emerged in many application areas, suffering from two noteworthy issues: instances with high-dimensional features and large-scale labels. Multilabel feature selection methods are widely studied to address the issues. Previous multilabel feature selection methods focus on exploring label correlations to guide the feature selection process, ignoring the impact of latent feature structure on label correlations. In addition, one encouraging property regarding correlations between features and labels is that similar features intend to share similar labels. To this end, a latent structure shared (LSS) term is designed, which shares and preserves both latent feature structure and latent label structure. Furthermore, we employ the graph regularization technique to guarantee the consistency between original feature space and latent feature structure space. Finally, we derive the shared latent feature and label structure feature selection (SSFS) method based on the constrained LSS term, and then, an effective optimization scheme with provable convergence is proposed to solve the SSFS method. Better experimental results on benchmark datasets are achieved in terms of multiple evaluation criteria.
Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach

In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete a priori certainty for channels’ statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, and Adadelat optimisation algorithms are used to evaluate the performance of the proposed estimator using each classification layer. In terms of symbol error rate and accuracy metrics, the proposed estimator outperforms long short-term memory (LSTM) neural network-based channel state information, least squares and minimum mean square error estimators under different simulation conditions. The computational and training time complexities for deep learning BiLSTM- and LSTM-based estimators are provided. Given that the proposed estimator relies on the deep learning neural network approach, where it can analyse massive data, recognise statistical dependencies and characteristics, develop relationships between features and generalise the accrued knowledge for new datasets that it has not seen before, the approach is promising for any 5G and beyond communication system.
Paired-unpaired Unsupervised Attention Guided GAN with transfer learning for bidirectional brain MR-CT synthesis

Medical image acquisition plays a significant role in the diagnosis and management of diseases. Magnetic Resonance (MR) and Computed Tomography (CT) are considered two of the most popular modalities for medical image acquisition. Some considerations, such as cost and radiation dose, may limit the acquisition of certain image modalities. Therefore, medical image synthesis can be used to generate required medical images without actual acquisition. In this paper, we propose a paired–unpaired Unsupervised Attention Guided Generative Adversarial Network (uagGAN) model to translate MR images to CT images and vice versa. The uagGAN model is pre-trained with a paired dataset for initialization and then retrained on an unpaired dataset using a cascading process. In the paired pre-training stage, we enhance the loss function of our model by combining the Wasserstein GAN adversarial loss function with a new combination of non-adversarial losses (content loss and L1) to generate fine structure images. This will ensure global consistency, and better capture of the high and low frequency details of the generated images. The uagGAN model is employed as it generates more accurate and sharper images through the production of attention masks. Knowledge from a non-medical pre-trained model is also transferred to the uagGAN model for improved learning and better image translation performance. Quantitative evaluation and qualitative perceptual analysis by radiologists indicate that employing transfer learning with the proposed paired-unpaired uagGAN model can achieve better performance as compared to other rival image-to-image translation models.
Using fuzzy cognitive maps to identify better policy strategies to valorize organic waste flows: An Italian case study

In Europe, there is a vast amount of municipal waste available. The organic fraction of municipal solid waste (OFMSW) represents a particularly valuable part of this waste, due to its potential to be employed to produce a range of value-added products. While several studies have addressed the utilization of the OFMSW in the Italian context, an overall picture of how the circular bioeconomy (CBE) model is being implemented in Italy is lacking. Accordingly, the present study investigated the status quo of the Italian bioeconomy sector, focusing on the use of the OFMSW as feedstock. The research aimed at increasing our understanding of barriers to the effective adoption of the CBE and identifying effective policy strategies. Specifically, a fuzzy cognitive mapping technique using an artificial neural network model was used to assess the impact of both single policy measures and policy mixes on a sample of selected outcomes, including human health, the environment, profitability and biorefinery approach. The results clearly showed that excessive bureaucracy, linear logic and technology-based solutions ignoring the complex characteristics of waste planning activities were the most important variables influencing the implementation of the CBE in Italy. Moreover, the results suggested that a policy mix combining economic and financial support policies for sustainable activities alongside improvements to waste collection systems could generate the highest positive effect on all considered outcomes.
Temporal Memory Attention for Video Semantic Segmentation

Video semantic segmentation requires to utilize the complex temporal relations between frames of the video sequence. Previous works usually exploit accurate optical flow to leverage the temporal relations, which suffer much from heavy computational cost. In this paper, we propose a Temporal Memory Attention Network (TMANet) to adaptively integrate the long-range temporal relations over the video sequence based on the self-attention mechanism without exhaustive optical flow prediction. Specially, we construct a memory using several past frames to store the temporal information of the current frame. We then propose a temporal memory attention module to capture the relation between the current frame and the memory to enhance the representation of the current frame. Our method achieves new state-of-the-art performances on two challenging video semantic segmentation datasets, particularly 80.3% mIoU on Cityscapes and 76.5% mIoU on CamVid with ResNet-50.
Optimal Deployment Mechanism of Blockchain in Resource-Constrained IoT Systems

The recently emerging blockchain technology provides a promising tool to enable endogenous security in Internet-of-Things (IoT) systems. However, when applying the legacy blockchain technology to the existing IoT systems, some technical bottlenecks due to resource constraints, such as storage resource, should be carefully addressed. In this article, we propose an optimal blockchain deployment mechanism for wireless IoT systems, where IoT devices have limited resources and the wireless links connection IoT devices are vulnerable, to improve the storage efficiency of massive blockchain data and realize the organic integration of blockchain technology and the communication process. We propose to maintain a complete blockchain by a set of proximity IoT nodes in a collaborative way on the premise of ensuring that each node can check every transaction. Through dynamically adjusting optimal block assignment, the tradeoff between the length of the blockchain to be stored and the security level provided can be well made. Moreover, a chaotic-based genetic algorithm is developed to obtain the near-optimal block assignment solution efficiently. Simulation results show that our proposed blockchain-based mechanism can effectively address the security issue in wireless IoT systems.
End-to-End Learning for OFDM: From Neural Receivers to Pilotless Communication

The benefits of end-to-end learning has been demonstrated over AWGN channels but has not yet been quantified over realistic wireless channel models. This work aims to fill this gap by exploring the gains of end-to-end learning over a frequency- and time-selective fading channel using OFDM. With imperfect channel knowledge at the receiver, the shaping gains observed on AWGN channels vanish. Nonetheless, we identify two other sources of performance improvements. The first comes from a neural network-based receiver operating over a large number of subcarriers and OFDM symbols which allows to reduce the number of orthogonal pilots without loss of BER. The second comes from entirely eliminating orthogonal pilots by jointly learning a neural receiver together with either superimposed pilots (SIPs), combined with conventional QAM, or an optimized constellation. The learned constellation works for a wide range of signal-to-noise ratios, Doppler and delay spreads, has zero mean and does hence not contain any form of SIP. Both schemes achieve the same BER as the pilot-based baseline with 7% higher throughput. Thus, we believe that a jointly learned transmitter and receiver are a very interesting component for beyond-5G communication systems which could remove the need and associated overhead for demodulation reference signals.
Unsharp Mask Guided Filtering

The goal of this paper is guided image filtering, which emphasizes the importance of structure transfer during filtering by means of an additional guidance image. Where classical guided filters transfer structures using hand-designed functions, recent guided filters have been considerably advanced through parametric learning of deep networks. The state-of-the-art leverages deep networks to estimate the two core coefficients of the guided filter. In this work, we posit that simultaneously estimating both coefficients is suboptimal, resulting in halo artifacts and structure inconsistencies. Inspired by unsharp masking, a classical technique for edge enhancement that requires only a single coefficient, we propose a new and simplified formulation of the guided filter. Our formulation enjoys a filtering prior from a low-pass filter and enables explicit structure transfer by estimating a single coefficient. Based on our proposed formulation, we introduce a successive guided filtering network, which provides multiple filtering results from a single network, allowing for a trade-off between accuracy and efficiency. Extensive ablations, comparisons and analysis show the effectiveness and efficiency of our formulation and network, resulting in state-of-the-art results across filtering tasks like upsampling, denoising, and cross-modality filtering. Code is available at \url{https://github.com/shizenglin/Unsharp-Mask-Guided-Filtering}.
Robots for Elderly Care in the Home: A Landscape Analysis and Co-Design Toolkit

Abstract Over the last two decades, several deployments of robots for in-house assistance of older adults have been trialled. However, these solutions are mostly prototypes and remain unused in real-life scenarios. In this work, we review the historical and current landscape of the field, to try and understand why robots have yet to succeed as personal assistants in daily life. Our analysis focuses on two complementary aspects: the capabilities of the physical platform and the logic of the deployment. The former analysis shows regularities in hardware configurations and functionalities, leading to the definition of a set of six application-level capabilities ( exploration , identification , remote control , communication , manipulation , and digital situatedness ). The latter focuses on the impact of robots on the daily life of users and categorises the deployment of robots for healthcare interventions using three types of services: support , mitigation , and response . Our investigation reveals that the value of healthcare interventions is limited by a stagnation of functionalities and a disconnection between the robotic platform and the design of the intervention. To address this issue, we propose a novel co-design toolkit, which uses an ecological framework for robot interventions in the healthcare domain. Our approach connects robot capabilities with known geriatric factors, to create a holistic view encompassing both the physical platform and the logic of the deployment. As a case study-based validation, we discuss the use of the toolkit in the pre-design of the robotic platform for an pilot intervention, part of the EU large-scale pilot of the EU H2020 GATEKEEPER project.