Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Personalized Stock Recommender System

This research paper introduces a personalized recommender system tailored specifically for the stock market. With the increasing complexity and variety of investment options, individual investors face significant challenges in making informed decisions. Traditional stock market recommendations often offer generic advice that fails to account for investors' unique preferences and risk appetites. We propose a personalized recommender system that utilizes deep learning techniques to provide customized stock recommendations. Our approach combines collaborative filtering (CF) and content-based (CB) filtering methodologies which form a hybrid system capable of generating personalized recommendations. Collaborative filtering utilizes the behaviour of similar investors to identify stocks that are likely to appeal to the user, while content-based filtering matches stock characteristics with the user's preferences and investment history. Experimental evaluations demonstrate that the proposed personalized recommender system outperforms existing algorithms and approaches trained on user interaction data taken from the stock market domain, providing investors with tailored stock recommendations that align with their personal needs and preferences.
Explainable Sparse Associative Self-optimizing Neural Networks for Classification

Supervised models often suffer from a multitude of possible combinations of hyperparameters, rigid nonadaptive architectures, underfitting, overfitting, the curse of dimensionality, etc. They consume many resources and slow down the optimization process. As real-world objects are related and similar, we should adapt not only network parameters but also network structure to represent patterns and relationships better. When the network reproduces the most essential and frequent data patterns and relationships and aggregate similarities, it becomes not only efficient but also explainable and trustworthy. This paper presents a new approach to detect and represent similarities of numerical training examples to self-adapt a network structure and its parameters. Such a network will facilitate the classification by identifying hyperspace regions associated with the defined classes in a training dataset. Our approach demonstrates its ability to automatically reduce input data dimension by removing features that produce distortions and do not support the classification process. The presented adaptation algorithm uses only a few optional hyperparameters and produces a sparse associative neural network structure that fits contextually any given dataset by detecting data similarities and constructing hypercuboids in data space. The explanation of these associative adaptive techniques is followed by the comparisons of the classification results against other state-of-the-art models and methods.
An Approach to Mongolian Neural Machine Translation Based on RWKV Language Model and Contrastive Learning

Low-resource machine translation (LMT) is a challenging task, especially for languages with limited resources like Mongolian. In this paper, we propose a novel Mongolian-to-Chinese machine translation approach based on the RWKV language model and augmented with contrastive learning. Traditional methods that perturb the embedding layer often suffer from issues such as semantic distortion and excessive perturbation, leading to training instability. To address these problems, we introduce a contrastive learning approach combined with adversarial perturbation. Additionally, the RWKV language model, as a new architecture, has shown to be more efficient in terms of training and inference time compared to traditional transformer models in various natural language processing tasks. In this work, we employ the RWKV language model as the core of our machine translation model. We evaluate our approach on a benchmark dataset of Mongolian-to-Chinese parallel sentences. The experimental results demonstrate that our method outperforms the state-of-the-art approaches in Mongolian machine translation. Furthermore, our research indicates that the proposed approach significantly mitigates the training instability caused by adversarial perturbation and demonstrates the effectiveness of employing the RWKV language model in improving translation performance.
The Effect of Time Pressure on Visual Search Eye Tracking Metrics in Virtual Reality

The study of visual search has focused on various guiding factors, but less attention has been given to how environmental factors affect visual search in virtual reality (VR). The visual search literature has primarily been based on 2D laboratory tasks, which lack the complexity of real-life search tasks. Thus, this study studies the effect of time pressure on visual search in a naturalistic environment. To do that, participants were immersed in a virtual living room using VR and tasked with finding objects under a time constraint. Eye gaze data was collected, and convex hull volumes and scanning rates were calculated and analyzed. The results show that time pressure reduced convex hull volume and increased scanning rate, indicating faster search speed and a gaze tunneling effect. Understanding how time pressure affects visual search can help improve training strategies and design better user interfaces for visual search critical domains.
Dataset Generation and Study of Deepfake Techniques

The consumption of multimedia content on the Internet has nowadays been expanded exponentially. These trends have contributed to fake news can become a very high influence in the current society. The latest techniques to influence the spread of digital false information are based on methods of generating images and videos, known as Deepfakes. This way, our research work analyzes the most widely used Deepfake content generation methods, as well as explore different conventional and advanced tools for Deepfake detection. A specific dataset has also been built that includes both fake and real multimedia contents. This dataset will allow us to verify whether the used image and video forgery detection techniques can detect manipulated multimedia content.
An Effective Morphological Analysis Framework of Intracranial Artery in 3D Digital Subtraction Angiography

Acquiring accurate anatomy information of intracranial artery from 3D digital subtraction angiography (3D-DSA) is crucial for intracranial artery intervention surgery. However, this task often comes with challenges of large-scale image and memory constraints. In this paper, an effective two-stage framework is proposed for fully automatic morphological analysis of intracranial artery. In the first stage, the proposed Region-Global Fusion Network (RGFNet) achieves accurate and continuous segmentation of intracranial artery. In the second stage, the 3D morphological analysis algorithm obtains the access diameter, the minimum inner diameter and the minimum radius of curvature of intracranial artery. RGFNet achieves state-of-the-art performance (93.36% in Dice, 87.83% in mIoU and 15.64 in HD95) in the 3D-DSA intracranial artery segmentation dataset, and the proposed morphological analysis algorithm also shows effectiveness in obtaining accurate anatomy information. The proposed framework is not only helpful for surgeons to plan the procedures of interventional surgery but also promising to be integrated to robotic navigation systems, enabling robotic-assisted surgery.
Debiasing Medication Recommendation with Counterfactual Analysis

The AI-driven medication recommendation has emerged as a crucial undertaking in the field of healthcare research. Recent literature has focused on leveraging patients' diagnoses, procedures, and historical visit information for medication recommendation. However, this approach can lead to recommendation biases due to spurious correlations among the historical visit information. Previous studies have either failed to address this bias issue or attempted to mitigate recommendation biases through dataset manipulation, albeit at the expense of increased computational costs. In this study, we propose CAMeR (Counterfactual Analysis based Medication Recommendation), which is a novel debiasing model based on counterfactual analysis. The model preserves medications information while emphasizing the primary influence of diagnoses and procedures. Unlike traditional factual reasoning approaches that address biases before or during training, counterfactual reasoning mitigates the impact of post-training spurious correlations. Additionally, we incorporate contrastive loss computation in the embedding module of our model to calibrate the feature construction for patients with multiple visit information. We validate the CAMeR on widely adopted datasets, MIMIC-III and MIMIC-IV, and experimental results unequivocally demonstrate its superiority over state-of-the-art methods.
Region-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning

Learning from imbalanced data sets usually produces biased classifiers that have a higher predictive accuracy over the majority class, but poorer predictive accuracy over the minority class. In this paper, we present a new over-sampling method, Region-SMOTE, to improve class imbalance and classification accuracy. In order to combine the boundary distribution information, the vote rule is formulated, which divides the minority samples into safety samples, intermediate samples and dangerous samples, and adopts different processing strategies for different samples. We evaluate Region-SMOTE method from two aspects, F-value and G-mean. We conduct extensive experiments on four data sets, including Diabetes, which is extracted from real-world physical examination database, and another three data sets, which are extracted from UCI, to verify the method. The evaluation results demonstrate that our proposed over-sampling method has better performance.
A Supervised Spatio-Temporal Contrastive Learning Framework with Optimal Skeleton Subgraph Topology for Human Action Recognition

Human action recognition (HAR) is a hotspot in the field of computer vision, the models based on Graph Convolutional Network (GCN) show great advantages in skeleton-based HAR. However,most existing GCN based methods do not consider the diversity of action trajectories, and not highlight the key joints. To address these issues, a supervised spatio-temporal contrastive learning framework with optimal skeleton subgraph topology for HAR (SSTCL-optSST) is proposed. SSTCL-optSST uses the samples with the same lablel as the target action (anchor) to build a positive sample set, each of them represents a trajectory of an action. The sample set is used to design a loss function to guide the model recognize different poses of the action. Furthermore, the subgraphs of an original skeleton graph are used to construct a skeleton subgraph topology space, each subgraph in it is evaluated, and the optimal one is selected to highlight the key joints. Extensive experiments have been conducted on NTU RGB+D 60 and Kinetics datasets, the results show that our model has competitive performance.
Dynamical Graph Echo State Networks with Snapshot Merging for Spreading Process Classification

The Spreading Process Classification (SPC) is a popular application of temporal graph classification. The aim of SPC is to classify different spreading patterns of information or pestilence within a community represented by discrete-time temporal graphs. Recently, a reservoir computing-based model named Dynamical Graph Echo State Network (DynGESN) has been proposed for processing temporal graphs with relatively high effectiveness and low computational costs. Inspired by DynGESN, we propose a novel reservoir computing-based model called the Grouped Dynamical Graph Echo State Network (GDGESN) for dealing with SPC tasks. In this model, a novel augmentation strategy named the snapshot merging strategy is designed for forming new snapshots by merging neighboring snapshots over time, and then multiple reservoir encoders are set for capturing spatiotemporal features from merged snapshots. After those, the logistic regression is adopted for decoding the sum-pooled embeddings into the classification results. Experimental results on six benchmark SPC datasets show that our proposed model has better classification performances than the DynGESN and several kernel-based models.
Analysing Requirements Specification Languages for Self-adaptive AAL Systems

Ambient Assisted Living (AAL) systems are usually deployed in complex environments and should behave autonomously. So, self-adaptation is an essential concern of these systems that should be part of their requirements specification. The modelling of self-adaptation requirements is a challenging task because it is strongly related to other system requirements. The achievement of self-adaptation requirements typically implies prioritisation or denial of different requirements, so it is crucial to have mechanisms for their modelling and analysis. We study the suitability of requirement specification languages for self-adaptation in the context of a project to develop AAL systems. Specifically, we compare specifications made in RELAX, Tropos4AS and GODA. Our study with 14 people concludes that understanding these specifications is similar for the three languages but poses some difficulties for users. Specifically, users perceive the understanding of RELAX specification requires less effort and makes them feel less frustrated.
A Formal Approach for Traceability Preservation in Software Development Process

Traceability is the ability to trace the usage of artifacts during the software lifecycle process. Though the benefits of establishing a traceability software system have been widely recognized, it is difficult to be applied well in actual development. In this paper, we propose a new method for traceability preservation which may be used in the practical software development process. A formal model for the traceability of software artifacts is first presented, which consists of variable traceability relations, classification and version number controls. We then present the composition, restriction and refinement operations in the software development process. Next, the preservation of traceability under these three operations is discussed respectively. To demonstrate the effectiveness of our approach, we finally develop a prototype tool named Formalized Software Management System (FSMS).
Diff-Writer: A Diffusion Model-Based Stylized Online Handwritten Chinese Character Generator

Online handwritten Chinese character generation is an interesting task which has gained more and more attention in recent years. Most of the previous methods are based on autoregressive models, where the trajectory points of characters are generated sequentially. However, this often makes it difficult to capture the global structure of the handwriting data. In this paper, we propose a novel generative model, named Diff-Writer, which can not only generate the specified Chinese characters in a non-autoregressive manner but also imitate the calligraphy style given a few style reference samples. Specifically, Diff-Writer is based on conditional Denoising Diffusion Probabilistic Models (DDPM) and consists of three modules: character embedding dictionary, style encoder, and an LSTM denoiser. The character embedding dictionary and the style encoder are adopted to model the content information and the style information respectively. The denoiser iteratively generates characters using the content and style codes. Extensive experiments on a popular dataset (CASIA-OLHWDB) show that our model is capable of generating highly realistic and stylized Chinese characters.
TCNet: Texture and Contour-Aware Model for Bone Marrow Smear Region of Interest Selection

Bone marrow smear cell morphology is the quantitative analysis of bone marrow cell images. Due to the cell overlap and adhesion in bone marrow smears, it is essential to select uniformly distributed and clear sections as regions of interest (ROIs). However, current ROI selection models have not considered the characteristics of bone marrow smears, resulting in poor performance in practical applications. By comparing bone marrow smear ROIs and non-ROIs, we have identified significant differences in fundamental features, such as texture and contour. Therefore, we propose a texture and contour-aware bone marrow smear ROI selection model (TCNet). Inspired by multi-task learning, this model enhances its feature extraction capabilities for texture and contour by constructing different prediction modules to learn feature representations of texture and contour, and applying multi-level deep supervision with pseudo labels. To validate the effectiveness of the proposed method, we evaluate it on a self-built dataset. Experimental results show that the proposed model achieves a 2.22% improvement in classification accuracy compared to the baseline model. In addition, we verify the proposed module's generalizability by testing it on different backbone networks, and the results demonstrate its strong universality.
NeeMAS: A Need-Based Multi-agent Simulator of Human Behavior for Long-Term Drifts in Smart Environments

Early identification of long-term changes in the behaviour of people monitored with smart environment solutions is essential to prevent health decline. However, data collection and analysis of human behaviour are challenging and time-consuming. A potential solution consists in creating digital twins of the individuals to replicate the typical behaviours for advanced data analytics. The Assistive Technology Group (ATG) at Politecnico di Milano has developed NeeMAS (NEEd-based Multi-Agent Simulator), a novel simulator that effectively simulates human behaviour with physiological and social needs, cognitive decay, and behavioural drifts due to ageing or disease onset such as apathy or depression. NeeMAS simulates a senior care facility with several individuals, spending part of their time in their rooms and in part sharing common indoor and outdoor spaces interacting with other people. Experimental results show the feasibility and flexibility of the proposed approach for the generation of typical human behaviours and their drifts.
Paper Recommendation with Multi-view Knowledge-Aware Attentive Network

The paper recommendation system aims to recommend potential papers of interest to users from massive data. Many efforts introduced knowledge graphs to solve problems such as data sparsity faced by traditional recommendation methods and used GNN-based techniques to mine the features of users and papers. However, existing work has not emphasized the quality of the knowledge graph construction, and has not optimized the modeling method from the scenario of paper recommendation, which makes the quality of recommendation results have room for improvement. In this paper, we proposed a Multi-View Knowledge-aware Attentive Network (MVKAN). Specifically, we first designed a knowledge graph construction method based on keynote extraction for better recommendation assistance. We then designed mechanisms for aggregation and propagation of graph attention from three views: the connectivity importance of entities, user's time preferences, and short-cut links to users based on tag similarity. This helps to model the representation of users and papers more effectively. Results from the experiments show that our model outperforms the baselines.
Weakly Supervised Temporal Action Localization Through Segment Contrastive Learning

Weakly-supervised temporal action localization (WS-TAL) aims to learn localizing action with only video-level labels. In general, traditional methods process each snippet individually, thus ignoring both relationship between different action snippets and the productive temporal contextual relationships which are critical for action localization. In this paper, we propose Bidirectional Exponential Moving Average (BEMA) to utilize contextual information to obtain more stable feature representation. In addition to that, we introduce Inter-Segment Loss to refine the snippet representation in feature space to prevent misidentification of similar actions for accurate action classification, and Intra-Segment Loss to separate action from background in feature space to locate precise temporal boundaries. Substantial analyses show that our strategy is effective and brings comparable performance gain with current state-of-the-art WS-TAL methods on THUMOS'14 and ActivityNet v1.2 datasets. Our code can be found at https://github.com/JiangZhouHao/SCL .
Effective Domain Adaptation for Robust Dysarthric Speech Recognition

By transferring knowledge from abundant normal speech to limited dysarthric speech, dysarthric speech recognition (DSR) has witnessed significant progress. However, existing adaptation techniques mainly focus on the full leverage of normal speech, discarding the sparse nature of dysarthric speech, which poses a great challenge for DSR training in low-resource scenarios. In this paper, we present an effective domain adaptation framework to build robust DSR systems with scarce target data. Joint data preprocessing strategy is employed to alleviate the sparsity of dysarthric speech and close the gap between source and target domains. To enhance the adaptability of dysarthric speakers across different severity levels, the Domain-adapted Transformer model is devised to learn both domain-invariant and domain-specific features. All experimental results demonstrate that the proposed methods achieve impressive performance on both speaker-dependent and speaker-independent DSR tasks. Particularly, even with half of the target training data, our DSR systems still maintain high accuracy on speakers with severe dysarthria.
Curiosity Enhanced Bayesian Personalized Ranking for Recommender Systems

Curiosity affects the users' selections of items, motivating them to explore the items regardless of their preferences. However, the existing social-based recommendation methods neglect the users' curiosity in the social networks, and it may cause the accuracy decrease in the recommendation. Moreover, only focusing on simulating the users' preferences can lead to users' information cocoons. To tackle the problems above, we propose a Curiosity Enhanced Bayesian Personalized Ranking (CBPR) model for the recommender systems. The experimental results on two public datasets demonstrate the advantages of our CBPR model over the existing models.
A Deep Learning and Probabilistic Approach to Recognising Activities of Daily Living with Privacy Preserving Thermal Sensors

A privacy preserving approach for recognising Activities of Daily Living (ADLs) is proposed within this work. Low-resolution thermal sensors and Convolutional Neural Network (CNN) models were utilised for detecting positions within a smart environment and classifying human poses. A Hidden Markov Model (HMM) was implemented for which the position and pose data acted as the model’s observable information. An average F-score of 0.8171 was achieved for the poses on a test dataset. From a separate test dataset, the times in which each ADL began and ended were estimated with a maximum of 30 s between estimations and ground truth. Each ADL was correctly classified from the test dataset. Further discussion on the results are presented in this article.