Reviewerly/exHarmony huggingface dataset:
Intent-based Deep Reinforcement Learning for Multi-agent Informative Path Planning

In multi-agent informative path planning (MAIPP), agents must collectively construct a global belief map of an underlying distribution of interest (e.g., gas concentration, light intensity, or pollution levels) over a given domain, based on measurements taken along their trajectory. They must frequently replan their path to balance the exploration of new areas with the exploitation of known high-interest areas, to maximize information gain within a predefined budget. Traditional approaches rely on reactive path planning conditioned on other agents’ predicted future actions. However, as the belief is continuously updated, the predicted actions may not match the executed actions, introducing noise and reducing performance. We propose a decentralized, deep reinforcement learning (DRL) approach using an attention-based neural network, where agents optimize long-term individual and cooperative objectives by sharing their intent, represented as a distribution of medium-/long-term future positions obtained from their own policy. Intent sharing enables agents to learn to claim or avoid broader areas, while the use of attention mechanisms allows them to identify useful portions of imperfect predictions, maximizing cooperation even based on imperfect information. Our experiments compare the performance of our approach, its variants, and high-quality baselines across various MAIPP scenarios. We finally demonstrate the effectiveness of our approach under limited communication ranges, towards deployments under realistic communication constraints.
A Graph Convolutional Neural Network for Recommendation Based on Community Detection and Combination of Multiple Heterogeneous Graphs

Graph Convolutional Neural Networks (GCNs) have performed well in many recommendation scenarios. In spite of this, recommendation models based on GCNs still face problems such as insufficient information mining and high complexity for some existing models. To address the above problems, we propose a Graph Convolutional Neural Network for Recommendation Based on Community Detection and the Combination of Multiple Heterogeneous Graphs (GCN-CMHG). This model uses the community detection algorithm to detect the communities in the user-item interaction heterogeneous graph (UIIHG), Finds the regional central nodes of communities, and then creates edges between the regional central node of each community and all other nodes in the UIIHG to construct the heterogeneous partial adjacent graph. Then, a Heterogeneous Partial Adjacent Auxiliary (HPAA) layer is designed to aggregate information on the heterogeneous partial adjacent graph. HPAA layer expands the influence of distant nodes on target nodes, enables target nodes to receive global information, and enhances the ability of GCN-CMHG to mine information. Specially, due to the low complexity of HPAA layer and the abandonment of redundant information, GCN-CMHG is easier to implement and train. Under the exact same experimental setting, GCN-CMHG’s time consumption is only about 1/10 of another model based on GCN called Graph Convolutional Neural Network for Recommendation Based on the Combination of Multiple Heterogeneous Graphs (GCN-MHG). Experiments on multiple real-world datasets show that GCN-CMHG achieves better results compared with several advanced models. The implementation of our work can be found at https://github.com/GCNRSs/GCN-CMHG.
Beyond Lexical Consistency: Preserving Semantic Consistency for Program Translation

Program translation aims to convert the input programs from one programming language to another. Automatic program translation is a prized target of software engineering research, which leverages the reusability of projects and improves the efficiency of development. Recently, thanks to the rapid development of deep learning model architectures and the availability of large-scale parallel corpus of programs, the performance of program translation has been greatly improved. However, the existing program translation models are still far from satisfactory, in terms of the quality of translated programs. In this paper, we argue that a major limitation of the current approaches is the lack of consideration of semantic consistency. Beyond lexical consistency, semantic consistency is also critical for the task. To make the program translation model more semantically aware, we propose a general framework named Preserving Semantic Consistency for Program Translation (PSCPT), which considers semantic consistency with regularization in the training objective of program translation and can be easily applied to all encoder-decoder methods with various neural networks (e.g., LSTM, Transformer) as the backbone. We conduct extensive experiments in 7 general programming languages. Experimental results show that with CodeBERT as the backbone, our approach outperforms not only the state-of-the-art open-source models but also the commercial closed large language models (e.g., textdavinci-002, text-davinci-003) on the program translation task. Our replication package (including code, data, etc.) is publicly available at https://github.com/duyali2000/PSCPT.
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation

Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCVR) prediction. However, the observed feedback usually suffer from two issues: selection bias and data sparsity, where biased and insufficient feedback seriously degrade the performance of recommender systems in terms of accuracy and ranking. Existing solutions for handling the issues, such as data imputation and inverse propensity score, are highly susceptible to additional trained imputation or propensity models. In this work, we propose a novel counterfactual contrastive learning framework for recommendation, named CounterCLR, to tackle the problem of non-random missing data by exploiting the advances in contrast learning. Specifically, the proposed CounterCLR employs a deep representation network, called CauNet, to infer non-random missing data in recommendations and perform user preference modeling by further introducing a self-supervised contrastive learning task. Our CounterCLR mitigates the selection bias problem without the need for additional models or estimators, while also enhancing the generalization ability in cases of sparse data. Experiments on real-world datasets demonstrate the effectiveness and superiority of our method.
Sparse Attacks for Manipulating Explanations in Deep Neural Network Models

We investigate methods for manipulating classifier explanations while keeping the predictions unchanged. Our focus is on using a sparse attack, which seeks to alter only a minimal number of input features. We present an efficient and novel algorithm for computing sparse perturbations that alter the explanations but keep the predictions unaffected. We demonstrate that our algorithm, compared to PGD attacks with $\ell_{0}$ constraint, generates sparser perturbations while resulting in greater discrepancies between original and manipulated explanations. Moreover, we demonstrate that it is also possible to conceal the attribution of the k most significant features in the original explanation by perturbing fewer than k features of the input data. We present results for both image and tabular datasets, and emphasize the significance of sparse perturbation-based attacks for trustworthy model building in high-stakes applications. Our research reveals important vulnerabilities in explanation methods that should be taken into account when developing reliable explanation methods. Code can be found at https://github.com/ahmadajal/sparse_expl_attacks
Hierarchical Label Inference Incorporating Attribute Semantics in Attributed Networks

Node attribute label inference is an important problem in attributed networks. Most existing works assume that node labels are at a single level, but in practice, the attribute labels can always be organized in a hierarchical structure according to their semantics. In this paper, we propose a novel hierarchical label inference model for attributed networks. Specifically, we propose a triple attention mechanism to extract fine-grained label semantics from three levels: hierarchical, sibling and global. Next, we propose the semantic fully-connected layer to explicitly exploit label semantics for attribute inference. We also propose semantic label propagation to enhance the interaction between the label semantics and the attributed network, and this interaction enables nodes in the attributed network to realise the proximity assumption at the label semantic level. Finally, we combine the semantic fully-connected layer with semantic label propagation for top-down hierarchical attribute inference. Extensive experiments demonstrate the superiority of our model.
Adaptive Student Inference Network for Efficient Single Image Super-Resolution

Recent advances in single image super-resolution (SISR) have achieved remarkable performance through deep learning. However, the high computational cost hinders the deployment of SISR models on edge devices. Instead of proposing new SISR models, a new trend is emerging to improve network efficiency by reducing parameters, FLOPs, and inference time through slight modifications to the original models. However, recent methods usually focus on reducing only one of three metrics, i.e., FLOPs, parameters and inference time, which inevitably increases the other two metrics. In this paper, we propose a novel Adaptive Student Inference Network (ASIN) on popular SISR models, which aims at reducing FLOPs and inference time while maintaining the number of parameters and restoring clearer high-resolution images. Specifically, our ASIN divides a SISR model into three components (head, body and tail) and adopts various strategies for each part. For head and tail parts, to ensure the restored images contain more detailed information, a novel auxiliary Enhanced Teacher Network (ETNet) is designed, which is trained with the ground-truth images to obtain more prior knowledge to guide student network to extract more accurate textures using a new knowledge distillation method. For the body part, owing to the varying difficulties of the reconstructions in different regions, we propose an Adaptive Depth Predicted Module (ADPM) to dynamically shorten average depth of network to reduce the computational cost of overall network. Extensive experiments on two datasets demonstrate the effectiveness and state-of-the-art performance of our ASIN compared to its counterparts.
A model for the selection of security system alarms

It was found that search engines provide a wide selection of detectors with technical characteristics. A significant range of characteristics ensures the use of detectors for solving problems in security systems. A search and sorting mode for security system alarms has been developed. It is based on the necessary and sufficient characteristics of detectors, which ensure their selection depending on the tasks set for security systems. Criteria for their selection are formed on the basis of user requirements and technical characteristics of detectors. Priorities of criteria for the selection of security system detectors have been introduced. The model creates an ordered list of notifiers that meet the specified criteria. Each notifier of the list is assigned a unique individual number. According to the importance of the criteria, the notifiers are ranked, the number of iterations of which depends on the number of selected criteria. a The maximum change in position of the list detector is defined as the ratio of the number of list detectors to the priority number of the criterion. The application of the developed model for the selection of security system alarms is provided as an example. In the first iteration, the ranking of alarms depends on the type of the first criteria field, which can be descriptive or numerical. If the field with the criterion is descriptive, its content is verified and the alarms are sorted. Otherwise, the alarms are ranked by the value of the alarm parameter. The permissible ranking of alarms covers all positions in the table. For all other criteria, the maximum change in the positions of the alarms depends both on the value of the criterion and on the priority of the criterion, which limits the maximum change in positions. After all the rankings, at the top of the formed list is an alarm with one of the best first, second, and fourth criteria. The third criterion meets the consumer's request. According to the fifth criterion, this sensor is average. Considering the development of ranking technologies, the relation-based ranking may not meet the needs of customers. At the same time, tools and methods for the potential hacking of alarms are evolving. Therefore, in further research, an alternative to ranking by criteria is proposed, but not by criteria relations, but by user choice. This information technology will influence the ranking of the list depending on the compromises on the criteria.
Tandem Neural Networks for the Inverse Programming of Linear Photonic Processors

Linear programmable photonic integrated processors have emerged as an alternative hardware platform for quantum, deep learning, and microwave photonic systems. Calibration and control of the photonic processor using deep learning techniques has proven to be challenging due to the one-to-many problem. This means that a given functionality of the processor can be achieved using different settings of the controllers. In this paper, we demonstrate how tandem neural networks can overcome this limitation in meshes of Mach-Zehnder interferometers by employing forward and inverse networks. This approach is independent of the mesh architecture and introduces a novel method for controlling photonic linear processors using deep neural networks. We provide an experimental demonstration using a 3x3 linear processor, achieving a control resolution higher than 7 bits.
Enhancing Post-Disaster Mapping Assessment: Agent-Based Simulation Modeling Integrating Ground Vehicles and Drones (Case Study: Mount Merapi's Volcanic Eruption)

This research presents an agent-based simulation model for post-disaster location mapping, considering land vehicles and drones along with access road availability and depot locations. The study examines the impact of bridge facility damage on depot selection and time indicators. Results reveal that damage to bridge facilities affects depots differently based on their location, leading to increased total processing and completion times due to interactions between land vehicles and bridges. Depot 7 emerges as the optimal location for undamaged and KRB II and III damage scenarios based on total processing time. Depot 3 performs best for KRB III damage, while Depot 8 exhibits the shortest completion time across all scenarios. These findings emphasize the importance of selecting depots with resilient road access and alternative routes, improving post-disaster logistics efficiency.
COMPREHENSION, VIEWS AND UTILIZATION OF MHEALTH TECHNOLOGY AMONG INDIAN STUDENTS AGED 18 AND ABOVE: A CROSS-SECTIONAL STUDY

COVID-19, a once in the century event, has changed the way we look at the world around us. In a span of a week, we went from preparing for our exams in college to being locked in our homes for three months. The country went into a sudden lockdown on 24th March 2020. The word ?National Lockdown? was something which most probably none of us had ever heard or had witnessed in real life. The news came as a vacation for some and as a challenge for some. But as the saying goes ?This too shall pass?. Eventually the time passed, and it has now been more than a year since the first lockdown, and things are now looking better. We now have 2 vaccines i.e., COVISHIELD and COVAXIN (an indigenously developed vaccine) currently being administered in India, and 4 indigenous vaccines i.e., CORBEVAX, ZyCOV-D, GENNOVA and BBVI154 Intranasal vaccine currently in development phase [1].
Research of Convents Channel Pruning Based on MCP-L1 Regularization

Due to the advancement of mobile and wearable devices, large models with tens of billions or even trillions of parameters cannot be deployed on mobile devices, despite the excellent performance of high-performance GPUs. Therefore, compressing large models to operate efficiently on platforms with limited resources is a contemporary area of research. This study proposes a MCP-L1 filter reduction method based on the minimax concave penalty (MCP) function preprocessing to reduce model duplication and memory consumption. The new method improves the individual L1 penalty and is vulnerable to “outlier point” defects, while can execute feature selection with various levels of penalty for various parameters. For the ResNet34 model, under two different tailoring strategies, compared to the original model, the top 1 accuracy of MCP-L1 compressed model only decreased by 1.34% and 0.05%, but its floating-point calculation quantity has decreased by 26.63% and 15.62%. Furthermore, in all scenarios, when the pruning rate is the same, the MCP-L1 method has higher accuracy than L1-Norm method.
MULTI-CRITERIA ASSESSMENT OF THE CORRECTNESS OF DECISION-MAKING IN INFORMATION SECURITY TASKS

Theoretical optimization models assume the presence of a single criterion. Therefore, the solution of the problem by the method of vecto (multi-criteria) optimization is of particular interest in the problems of cybersecurity and information security. Especially when it is necessary to evaluate the correctness of the made decisions (CMD). In the paper this problem is solved so that it can be asserted that the decision was made correctly in this particular case when solving a problem while ensuring the information security of a particular object.
Physical Reservoir Computing using HZO-based FeFETs for Edge-AI Applications

we have proposed and demonstrated reservoir computing (RC) by using HZO/Si FeFETs. In our FeFET reservoir, the time response of currents of Si FeFETs is utilized as the virtual nodes. We experimentally present the fundamental properties of RC performance and address several methods to enhance RC performance. This scheme is applied to two typical AI tasks, prediction of nonlinear time- series data and speech recognition. We experimentally show a classification accuracy of 98.1 % in a speech recognition task to classify the audio waveforms of ‘0’ to ‘9’ spoken digits.
2238 When a doctor and a nurse share their view in a simulated emergency care interaction: developing an automatic gaze annotation method for multiple eye-tracking data analysis

<h3>Aims and Objectives</h3> Collective competence of a healthcare team is key to patient safety and quality in healthcare. This presentation reports part of a UK-Japan interdisciplinary research project on emergency care team interactions, focusing on gaze behaviour. The study investigates how healthcare professionals achieve joint attention for joint action. To describe the complex practice, we are developing a programme to automatically annotate multiple gaze data. <h3>Method and Design</h3> For a pilot study, a small-scale simulation session was filmed in a large teaching hospital in the UK with two pairs of eye-tracking glasses (Tobii Pro Glasses 2) worn by an ED consultant and an ED nurse. The scenario was a collapse in a ward, which involved two simulated patients, one acting as a female patient aged 72 and the other as her sister. Video images of the first three minutes of the recording were extracted from two eye-trackers, and the moments when a doctor and a nurse shared a field of view were automatically annotated by utilizing the feature point detection technology. <h3>Results and Conclusion</h3> Three forms of joint gaze behaviours were recognised: joint attention (two people look at the same part of an object), narrow joint field view (two people look at different parts of an object, see figure 1), and broad joint field view (two people share a view field but look at different objects). Although it is still preliminary, the technology enables researchers to analyse shared field view, in addition to joint attention, between team members. This has great potential for further research on health team interactions and its application to medical education.
Classification of Topics Using Bi-LSTM and CNN with the Feature Expansion on Twitter

Twitter is one of the social media that is still widely used today. Messages on Twitter are limited to only 280 characters, restricting users’ ability to exchange information, and forcing users to use various strategies, such as slang and abbreviations, for condensed details. This makes it difficult for users to understand the context or topic of the information, so a solution is needed for this problem. Many studies have classified topics on Twitter but have provided low accuracy results due to limitations and have yet to apply the hybrid deep learning method to topic classification. Therefore, this study involves an expansion feature to overcome these limitations by looking for similar words using gloves and applying a hybrid deep learning method to the classification model to outperform a single model. The data used in this research is Indonesian language tweet data and the models used are Bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and a hybrid of the two models. The research results show that the Bi-SLTM and CNN models with feature expansion produce accuracy of 93.82% and 93.75%. The best model for topic classification in this study with the best ratio (80:20), the best N-gram variation (unigram + bigram + trigram), and the glove expansion feature with top 5 similarity, namely the CNN-BILSTM hybrid with an accuracy of 94.34% resulted in an accuracy increase of 1.73% for Bi-LSTM and 1.47% for CNN.
A Linear Driver Model of Local Path Planning for Lane Driving

Modern lane centering assistance systems often use the mid-lane as a reference line for tracking. However, human drivers prefer to keep an offset to the mid-lane. Therefore, we have proposed in our previous work a driver model which calculates human-like offset values and enable the path planner to plan human-like paths. The driving data of 15 drivers have been analyzed. It has been revealed that drivers behave differently in left or right curves and choose to drive on either the left or the right side in straight road sections. Therefore, we extend our model to handle these traits. We have shown that the extended model outperforms the previous symmetric model.
Network Vulnerability Assessment based on Knowledge Graph

Vulnerability assessment is a critical aspect of cybersecurity, and its importance has grown significantly. However, traditional methods based on attack graph are expensive and lack interpretability. And emerging methods based on knowledge graph, there is currently no widely accepted scheme in the industry. To address this issue, we propose a new scheme named KG-Rank, which leverages the correlations between vulnerabilities and assets through a knowledge graph and improves PageRank to assess and rank vulnerabilities. The KG-Rank scheme involves constructing Vulnerability Knowledge Graph (VKG) and Weakness Knowledge Graph (WKG), and obtaining new relations between weaknesses through relational reasoning on WKG by using a relational reasoning model called Doc2TransR to complete VKG. We then transform the nodes and edges in VKG and assess nodes on the transformed graph using a designed random walk strategy. Our experimental results demonstrate the effectiveness and reliability of KG-Rank, which considers not only the severity of vulnerabilities but also the importance of assets and the exposure scope of vulnerabilities and assets.