Miracle-dz/UniFAR-FTUs huggingface dataset:
Removal of the extracellular matrix biases auditory cortical layer dynamics towards supragranular corticocortical integration

In the adult vertebrate brain, long-lasting structural tenacity of synaptic networks is supported by the extracellular matrix (ECM), which is formed during brain maturation. Enzymatic removal of the ECM in cortical areas has been increasingly recognized to promote or modulate juvenile forms of plasticity, learning and memory recall in the adult brain. However, the underlying impact of local removal of the ECM on the cortical circuit processing is still not understood. Here, we removed the ECM in the primary auditory cortex (ACx) of adult Mongolian gerbils using local injections of hyaluronidase (HYase). We performed laminar current-source density (CSD) analysis during spontaneous and evoked columnar activity and found layerspecific changes with higher activation of supragranular layers I/II and lower activity in infragranular layers Vb. Further, ECM removal induced a higher activity spread via lateral corticocortical circuits, which led to increased spectral integration at a cortical patch. A multitaper spectral analysis of layer-specific CSD responses revealed increased evoked oscillatory power in the beta band (25-36 Hz) selectively within infragranular layers Vb. Finally, to reveal the dynamic nature of translaminar interactions, we used timedomain conditional Granger causality (GC) confirming altered translaminar input-output dynamics with a supragranular lead of the columnar response profile. Our findings thus reveal new insights on how ECM modulation affects laminar cortical network dynamics, which help to interpret existing behavioral findings and to design more targeted translative applications in neurorehabilitation. Significance statement The brains ability to change in response to sensory stimuli is the substrate for learning and memory. During maturation, synaptic networks are stabilized by the extracellular matrix (ECM) closing windows of juvenile plasticity. Enzymatic removal of the adult ECM in the cortex promotes juvenile forms of plasticity, learning and restorative rehabilitation. However, the underlying cortical circuit mechanisms and particularly the role of different cortical layers beyond such treatment are barely understood. We enzymatically removed the ECM in primary auditory cortex of adult Mongolian gerbils and describe layer-dependent changes of the columnar physiology. Our data help to understand the cortical circuit changes supporting the promotive effects of ECM removal on sensory processing and brain plasticity.
MULTI LINKED RELEVANT FEATURE SET MARKING MODEL USING CONVOLUTION NEURAL NETWORK FOR DIABETIC RETINOPATHY GRADE DETECTION

Diabetic Retinopathy (DR) is the most common cause of blindness in 12% of the population every year. One of the most effective ways to prevent vision loss and reduce health care costs is to screen for DR and monitor disease progression in the early stages, especially in the asymptomatic stages. Nonmydriatic digital colour fundus cameras are commonly used to take colour images of the retina in most screening programmes. Early diagnosis and treatment can significantly reduce the risk of severe vision loss. DR screening has been found to be a cost-effective method of reducing the burden on the health care system. The development of automated tools to aid in the detection and evaluation of DR lesions that has been a major focus of recent research. Patients who are diagnosed with retinopathy early on have a better chance of preserving their vision. The diabetic retinopathy grade severity level assessment is proposed in this paper that uses Multi Linked Relevant Feature SetMarking Model using Convolution Neural Network (MLRFSMM-CNN). The standard DIARECTDB1 datasets are used to obtain the colour retina images. Images of the retina are first analysed to identify lesions, such as blood vessels and haemorrhages, as well as exudate and microaneurysms, on the retina. This is followed by extraction of various relevant features, such as area of the segmented exudates, the quantity of microaneurysms in the segmented image, the mean and standard deviation of segmented lesions, to determine the grade severity level of the disease. The proposed model is compared with the traditional methods and the results represent that the proposed model performance is enhanced.
APPLICATION OF A CONVOLUTIONAL NEURAL NETWORK WITH A MODULE OF ELEMENTARY GRAPHIC PRIMITIVE CLASSIFIERS IN THE PROBLEMS OF RECOGNITION OF DRAWING DOCUMENTATION AND TRANSFORMATION OF 2D TO 3D MODELS

This paper presents the results of the research related to the design of a convolutional neural network with a module of graphic primitives elementary classifiers (EC) in the tasks of drawing documentation recognition and transformation of the 2D into 3D models. An architecture of a convolutional neural network with an elementary classifiers module of graphic primitives was proposed for solving the drawing recognition and 2𝐷 → 3𝐷 transformation problem. A graphic image classifier model based on covered classes and elementary primitive classifiers has been developed to increase the effectiveness of CNN training.
IMPLEMENTATION OF RECOMMENDATION SYSTEM IN E-COMMERCE USING APPROXIMATE NEAREST NEIGHBOR

At least 20 e-commerce businesses in Indonesia have stopped operating due to human resource problems. The recommendation system is an important feature in e-commerce which was initially done manually. However, a computing field has emerged that can replace human labor in recommendation systems, reduce human errors and work more efficiently. This paper aims to implement an efficient and scalable recommendation system using Machine Learning techniques. The content-based filtering used in this paper uses the Approximate Nearest Neighbor algorithm with different indexes that provide similar product recommendations. Data is collected from Tokopedia product information, which is divided into five categories. The optimal Nprobe is found at 6% which is the standard Nprobe for each index. The index that has the best recall@R parameter, fastest prediction time and training time respectively are IDMap,Flat, IVF1024
Imposing Ratios of Outlet Flow Rates on Large Arterial Networks with Two-Element Windkessel Model: Parametric Analysis

Substantial effort is being invested in the creation of a virtual human — a model which will improve our understanding of human physiology and diseases and assist clinicians in the design of personalised medical treatments. A central challenge of achieving blood ﬂow simulations at full-human scale is the development of an efﬁcient and accurate approach to imposing boundary conditions on many outlets. A previous study proposed an efﬁcient method for implementing the two-element Windkessel model to control the ﬂow rate ratios at outlets. However, no study to date has examined the conditions for this approach to hold in complex geometries. Here we clarify the general role of the resistance and capacitance in this approach. We show that the error of the ﬂow rate ratios decreases exponentially as the resistance increases. The errors fall below 4% in a simple ﬁve-outlets model and 7% in a human artery model comprising 10 outlets. Moreover, the ﬂow rate ratios converge faster and suffer from weaker ﬂuctuations as the capacitance decreases. Our ﬁndings also establish constraints on the parameters controlling the numerical stability of the simulations. The ﬁndings from this work are directly applicable to larger and more complex vascular domains encountered at full-human scale.
DETERMINANTS OF BYOD PROTECTION BEHAVIOR: AN EMPLOYEE’S PERSPECTIVE

Bring Your Own Device (BYOD) allows employees to access the organizational network via their devices/technology. This trend is beneficial to the employees in terms of greater flexibility, apart from productivity and cost savings for the company. Enabling employees to use their own devices at the workplace may lead the company to become vulnerable to information security threats as employees do not possess the right understanding of protecting their devices. This study analyzed the factors that determine employees’ behavioral intention and their actual protection behavior in protecting their devices in BYOD environment. A self-administered questionnaire was conducted with 383 government employees in Oman. The results indicated that perceived vulnerability, perceived severity, response cost, subjective norm, perceived behavioral control, and knowledge influenced employees’ BYOD intention protection behavior while perceived vulnerability, perceived severity, response efficacy, response cost, attitude, subjective norm, and perceived behavioral control were found to influence BYOD protection behavior. Contrarily, response efficacy, security self-efficacy, attitude, and information security awareness were found to be nonsignificant on protection intention. The findings also revealed that the mediator (protection intention) has a considerable beneficial impact on the dependent variable (protection behavior). Hence, employers should develop an all-encompassing approach to improve their employees' BYOD usage protection behavior to secure the organization’s assets.
Fighting Unconventional War In The Tribal Areas Of Pakistan Through Conventional Means

The US invasion of Afghanistan on October 7, 2001 left profound impact on the security situation in Pakistan. Pakistan’s tribal areas became a hub for different local and international militant organisations. As an ally of the US in the war against terror Pakistan security forces conducted military operations against these non-state actors. This infuriated the people of the tribal areas which had extended support to these different organisations in the Soviet-Afghan war. They started an armed struggle against the state and the security forces. The terrain of the tribal areas provided the militant an enabling environment which helped them to expand their area of influence. The militants effectively exploited the administrative weaknesses of the state in the tribal areas which helped them to obtain the support of the people. The government on the other side heavily relied on the conventional use of force to fight an unconventional enemy. The irregular warfare warranted a comprehensive strategy based on the principles of unconventional warfare to defeat the insurgency in the area. This contributed to the failure of initial military operation in Waziristan. However, the government later changed its strategy from conventional to unconventional warfare in Bajaur and Swat which helped them to defeat the militancy.
Vortex-induced vibrations of a deformable splitter plate behind a square cylinder controlled by active pitch oscillation

Vortex-induced vibrations (VIV) widely occur in nature and are of interest for energy harvesting and bio-inspired propulsion. To understand passive VIV coexisting with active structure motions, this paper is motivated to numerically investigate the use of pure pitch oscillation to control a square cylinder mounted with a deformable splitter plate at the Reynolds number of 333. Direct numerical simulations using a partitioned method with a semi-implicit coupling algorithm are performed. According to the trajectories of the splitter-plate tip displacement with respect to the lift or drag force coefficient, a specific lock-in regime determined by the frequency of the enforced pitch oscillation is identified. Further spectral analyses of the tip displacement and lift force show that the lock-in frequencies are equal to the enforced frequencies. Next to the lock-in regime, semi-lock-in regimes with narrow bandwidths are distinguished, exhibiting both lock-in and non-lock-in features. In the non-lock-in regimes, the frequencies of the most predominant peaks in the spectra are found near the natural frequency of the splitter plate, and the frequencies of the two secondary peaks are distributed along the characteristic lines following the ratios of these frequencies to the enforced frequency, which are ± 1 . Thus, the interaction is dependent on the combined effects of the passive VIV and the actively enforced pitch oscillations. Moreover, the intersection points of the characteristic lines are located
CylinderTag: An Accurate and Flexible Marker for Cylinder-Shape Objects Pose Estimation Based on Projective Invariants

High-precision pose estimation based on visual markers has been a thriving research topic in the field of computer vision. However, the suitability of traditional flat markers on curved objects is limited due to the diverse shapes of curved surfaces, which hinders the development of high-precision pose estimation for curved objects. Therefore, this paper proposes a novel visual marker called CylinderTag, which is designed for developable curved surfaces such as cylindrical surfaces. CylinderTag is a cyclic marker that can be firmly attached to objects with a cylindrical shape. Leveraging the manifold assumption, the cross-ratio in projective invariance is utilized for encoding in the direction of zero curvature on the surface. Additionally, to facilitate the usage of CylinderTag, we propose a heuristic search-based marker generator and a high-performance recognizer as well. Moreover, an all-encompassing evaluation of CylinderTag properties is conducted by means of extensive experimentation, covering detection rate, detection speed, dictionary size, localization jitter, and pose estimation accuracy. CylinderTag showcases superior detection performance from varying view angles in comparison to traditional visual markers, accompanied by higher localization accuracy. Furthermore, CylinderTag boasts real-time detection capability and an extensive marker dictionary, offering enhanced versatility and practicality in a wide range of applications. Experimental results demonstrate that the CylinderTag is a highly promising visual marker for use on cylindrical-like surfaces, thus offering important guidance for future research on high-precision visual localization of cylinder-shaped objects. The code is available at: https://github.com/wsakobe/CylinderTag.
LASER: Linear Compression in Wireless Distributed Optimization

Data-parallel SGD is the de facto algorithm for distributed optimization, especially for large scale machine learning. Despite its merits, communication bottleneck is one of its persistent issues. Most compression schemes to alleviate this either assume noiseless communication links, or fail to achieve good performance on practical tasks. In this paper, we close this gap and introduce LASER: LineAr CompreSsion in WirEless DistRibuted Optimization. LASER capitalizes on the inherent low-rank structure of gradients and transmits them efficiently over the noisy channels. Whilst enjoying theoretical guarantees similar to those of the classical SGD, LASER shows consistent gains over baselines on a variety of practical benchmarks. In particular, it outperforms the state-of-the-art compression schemes on challenging computer vision and GPT language modeling tasks. On the latter, we obtain $50$-$64 \%$ improvement in perplexity over our baselines for noisy channels.
EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View

Multi-view aggregation promises to overcome the occlusion and missed detection challenge in multi-object detection and tracking. Recent approaches in multi-view detection and 3D object detection made a huge performance leap by projecting all views to the ground plane and performing the detection in the Bird's Eye View (BEV). In this paper, we investigate if tracking in the BEV can also bring the next performance breakthrough in Multi-Target Multi-Camera (MTMC) tracking. Most current approaches in multi-view tracking perform the detection and tracking task in each view and use graph-based approaches to perform the association of the pedestrian across each view. This spatial association is already solved by detecting each pedestrian once in the BEV, leaving only the problem of temporal association. For the temporal association, we show how to learn strong Re-Identification (re-ID) features for each detection. The results show that early-fusion in the BEV achieves high accuracy for both detection and tracking. EarlyBird outperforms the state-of-the-art methods and improves the current state-of-the-art on Wildtrack by +4.6 MOTA and +5.6 IDF1.
Tailoring Adversarial Attacks on Deep Neural Networks for Targeted Class Manipulation Using DeepFool Algorithm

Deep neural networks (DNNs) have significantly advanced various domains, but their vulnerability to adversarial attacks poses serious concerns. Understanding these vulnerabilities and developing effective defense mechanisms is crucial. DeepFool, an algorithm proposed by Moosavi-Dezfooli et al. (2016), finds minimal perturbations to misclassify input images. However, DeepFool lacks a targeted approach, making it less effective in specific attack scenarios. Also, in previous related works, researchers primarily focus on success, not considering how much an image is getting distorted; the integrity of the image quality, and the confidence level to misclassifying. So, in this paper, we propose Enhanced Targeted DeepFool, an augmented version of DeepFool that allows targeting specific classes for misclassification and also introduce a minimum confidence score requirement hyperparameter to enhance flexibility. Our experiments demonstrate the effectiveness and efficiency of the proposed method across different deep neural network architectures while preserving image integrity as much and perturbation rate as less as possible. By using our approach, the behavior of models can be manipulated arbitrarily using the perturbed images, as we can specify both the target class and the associated confidence score, unlike other DeepFool-derivative works, such as Targeted DeepFool by Gajjar et al. (2022). Results show that one of the deep convolutional neural network architectures, AlexNet, and one of the state-of-the-art model Vision Transformer exhibit high robustness to getting fooled. This approach can have larger implication, as our tuning of confidence level can expose the robustness of image recognition models. Our code will be made public upon acceptance of the paper.
A distributed approach to meteorological predictions: addressing data imbalance in precipitation prediction models through federated learning and GANs

The classification of weather data involves categorizing meteorological phenomena into classes, thereby facilitating nuanced analyses and precise predictions for various sectors such as agriculture, aviation, and disaster management. This involves utilizing machine learning models to analyze large, multidimensional weather datasets for patterns and trends. These datasets may include variables such as temperature, humidity, wind speed, and pressure, contributing to meteorological conditions. Furthermore, it’s imperative that classification algorithms proficiently navigate challenges such as data imbalances, where certain weather events (e.g., storms or extreme temperatures) might be underrepresented. This empirical study explores data augmentation methods to address imbalanced classes in tabular weather data in centralized and federated settings. Employing data augmentation techniques such as the Synthetic Minority Over-sampling Technique or Generative Adversarial Networks can improve the model’s accuracy in classifying rare but critical weather events. Moreover, with advancements in federated learning, machine learning models can be trained across decentralized databases, ensuring privacy and data integrity while mitigating the need for centralized data storage and processing. Thus, the classification of weather data stands as a critical bridge, linking raw meteorological data to actionable insights, enhancing our capacity to anticipate and prepare for diverse weather conditions.
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding

The recent success of large pre-trained language models (PLMs) heavily hinges on massive labeled data, which typically produces inferior performance in low-resource scenarios. To remedy this dilemma, we study self-training as one of the predominant semi-supervised learning (SSL) approaches, which utilizes large-scale unlabeled data to generate synthetic examples. However, too many noisy labels will hurt the model performance, and the self-training procedure requires multiple training iterations making it more expensive if all the model parameters of the PLM are updated. This paper presents UPET, a novel Uncertainty-aware Parameter-Efficient self-Training framework to effectively and efficiently address the labeled data scarcity issue. Specifically, we incorporate Monte Carlo (MC) dropout in Bayesian neural network (BNN) to perform uncertainty estimation for the teacher model and then judiciously select reliable pseudo-labeled examples based on confidence and certainty. During the student training, we introduce multiple parameter-efficient learning (PEL) paradigms that allow the optimization of only a small percentage of parameters. We also propose a novel Easy-Hard Contrastive Tuning to enhance the robustness and generalization. Extensive experiments over multiple downstream tasks demonstrate that UPET achieves a substantial improvement in terms of performance and efficiency. Our codes and data are released at https: //github.com/wjn1996/UPET.
Social Robot Mediator for Multiparty Interaction

A social robot acting as a 'mediator' can enhance interactions between humans, for example, in fields such as education and healthcare. A particularly promising area of research is the use of a social robot mediator in a multiparty setting, which tends to be the most applicable in real-world scenarios. However, research in social robot mediation for multiparty interactions is still emerging and faces numerous challenges. This paper provides an overview of social robotics and mediation research by highlighting relevant literature and some of the ongoing problems. The importance of incorporating relevant psychological principles for developing social robot mediators is also presented. Additionally, the potential of implementing a Theory of Mind in a social robot mediator is explored, given how such a framework could greatly improve mediation by reading the individual and group mental states to interact effectively.
Multi-level Contrastive Learning for Script-based Character Understanding

In this work, we tackle the scenario of understanding characters in scripts, which aims to learn the characters' personalities and identities from their utterances. We begin by analyzing several challenges in this scenario, and then propose a multi-level contrastive learning framework to capture characters' global information in a fine-grained manner. To validate the proposed framework, we conduct extensive experiments on three character understanding sub-tasks by comparing with strong pre-trained language models, including SpanBERT, Longformer, BigBird and ChatGPT-3.5. Experimental results demonstrate that our method improves the performances by a considerable margin. Through further in-depth analysis, we show the effectiveness of our method in addressing the challenges and provide more hints on the scenario of character understanding. We will open-source our work on github at https://github.com/David-Li0406/Script-based-Character-Understanding.
RMap: Millimeter-Wave Radar Mapping Through Volumetric Upsampling

Millimeter Wave Radar is being adopted as a viable alternative to lidar and radar in adverse visually degraded conditions, such as the presence of fog and dust. However, this sensor modality suffers from severe sparsity and noise under nominal conditions, which makes it difficult to use in precise applications such as mapping. This work presents a novel solution to generate accurate 3D maps from sparse radar point clouds. RMap uses a custom generative transformer architecture, UpPoinTr, which upsamples, denoises, and fills the incomplete radar maps to resemble lidar maps. We test this method on the ColoRadar dataset to demonstrate its efficacy.