Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissociating Language and Thought in Human Reasoning

What is the relationship between language and complex thought? In the context of deductive reasoning there are two main views. Under the first, which we label here the language-centric view, language is central to the syntax-like combinatorial operations of complex reasoning. Under the second, which we label here the language-independent view, these operations are dissociable from the mechanisms of natural language. We applied continuous theta burst stimulation (cTBS), a form of noninvasive neuromodulation, to healthy adult participants to transiently inhibit a subregion of Broca’s area (BA44) associated in prior work with parsing the syntactic relations of natural language. We similarly inhibited a subregion of dorsomedial frontal cortex (BA8) which has been associated with core features of logical reasoning. There was a significant interaction between task and stimulation site. Post hoc tests revealed that performance on a linguistic reasoning task, but not deductive reasoning task, was significantly impaired after inhibition of BA44, and performance on a deductive reasoning task, but not linguistic reasoning task, was decreased after inhibition of BA8 (however not significantly). Subsequent linear contrasts supported this pattern. These novel results suggest that deductive reasoning may be dissociable from linguistic processes in the adult human brain, consistent with the language-independent view.
Diffusion Graph Neural Ordinary Differential Equation Network for Traffic Prediction

Traffic prediction is the cornerstone of the intelligent transportation system (ITS), and accurate prediction is essential for planning route, alleviating traffic pressure, and optimizing public transportation resource allocation. Although many methods have been proposed, they still have deficiencies in capturing the spatial-temporal dependence of traffic data. In specific, their network structures are usually discrete, making it is challenging to continuously model the dynamic spatial-temporal patterns of the road network. Besides, static graph structure of the road network is not sufficient to express dynamic traffic patterns. In this paper, we propose a diffusion graph neural ordinary differential equation network (DGODE) to address the above challenges for traffic prediction. Firstly, DGODE represents the node relationships of the road network as a bidirectional spatial graph and the node relationships of the time as a unidirectional temporal graph, and then generates an adaptive diffusion matrix to explore potential node relationships and capture spatial and temporal dependencies. Next, the neural ordinary differential equation (NODE) is introduced to contin-uously model the dynamical change of traffic network, making it possible to capture global dependence at a deeper network structure. Since the solution of the ordinary differential equation is determined by the initial state, we design the structure with a jump link to supplement the spatial-temporal information in the historical data. Finally, the experimental evaluation on four real-world datasets shows that DGODE is significantly superior to several baseline methods.
3D Dental Biometrics: Transformer-based Dental Arch Extraction and Matching

The dental arch is a significant anatomical feature that is crucial in assessing tooth arrangement and configuration and has a potential for human identification in biometrics and digital forensic dentistry. In a previous study, we proposed an auto pose-invariant arch feature extraction Radial Ray Algorithm (RRA) and a matching framework [1] based solely on 3D dental geometry. To enhance the identification accuracy and speed of our previous work, we propose in this study a transformer architecture that can extract dental keypoints by encoding both local and global features. The dental arch is then constructed through robust interpolation of the dental keypoints using B-Spline and is compared using the same identification framework. To evaluate the effectiveness of our proposed approach, we conducted experiments by matching the same 11 post-mortems (PM) samples against 200 antemortem (AM) samples. Our results show that our approach achieves higher accuracy and faster speed compared to our previous work. Specifically, 11 samples (100%) achieved a top 6.5% (13/200) accuracy out of the 200-rank list, compared to the top 15.5% (31/200) accuracy previously [1]. Additionally, the time required to identify a single subject from 200 subjects has been reduced from 5 minutes to 3 minutes. The dental arch can be used as a powerful filter feature. Our findings make a significant contribution to the existing literature on dental identification and demonstrate the potential practical applications of our approach in diverse fields such as biometrics, forensic dentistry, orthodontics, and anthropology.
Object View Prediction with Aleatoric Uncertainty for Robotic Grasping

In vision-based robotic grasping the prediction of grasps is made on an RGB-D image of the scene from a pre-grasp position. Due to perspective occlusions the information about the object to be grasped is incomplete. Therefore the reliable prediction of a successful grasp is difficult. In this study we investigate a convolutional neural network that can predict several depth views of an object from a single depth image and camera movements. We show how the heteroscedastic aleatoric uncertainty can be used to predict the uncertainty of the generated object views. As the classical Negative-Log-Likelihood loss function for the aleatoric uncertainty does not converge to a meaningful object view prediction, we investigate the modified $\beta- \mathbf{Negative}$ -Log-Likelihood loss function and the Moment Matching loss. For assessing the predicted uncertainty we introduce a new measure, the uncertainty weighted intersection over union value.
Denoising Task-Based Functional Magnetic Resonance Imaging Data Using Hybrid Deep Neural Network Model

We present a hybrid deep neural network model for Denoising task-based fMRI data. This model is based on the Deep Neural Network (DNN) model [1] and improves its performance by utilizing different layers for Grey Matter and non-Grey Matter related data. The preprocessed data passes through GM and non-GM masks. Then it is fed to the network and passes through temporal filters along with an LSTM layer. The result will be classified using time-distributed fully-connected layers –since the input data is a time-series of voxel values-which have different structures for GM and non-GM input data. This hybrid mechanism helps the model to reduce the correlation between non-GM processed data and the design matrix significantly. We also increase the correlation of GM output, which is equivalent to better cost function and less noise. This new self-supervised model is tested using simulated data and the HCP database [2]. We succeed in denoising task better than the DNN model by an average of at least 4%.
ARMoR: Amphibious Robot for Mobility in Real-World Applications

This paper presents a mobile robot for amphibious surface locomotion called ARMoR. The locomotion system of ARMoR consists of two wheel-and-leg transformable mechanisms and a customizable balancing tail. A sphere body chassis containing electronic components assembles the wheels and the tail. A combination of chassis design and transformable wheels allows ARMoR to safely navigate various environments, including diverse terrains and water surfaces. The robot is controlled and operated using an embedded microprocessor interfacing with sensing, communicating, and powering modules, including the Global Positioning System (GPS), camera, Inertial Measurement Unit (IMU), wireless communication module, and batteries. ARMoR was tested for its locomotion capabilities on concrete, dirt, grass, rocky surface, low brush, stairs, and water. On concrete, dirt, and grass, ARMoR operated in the wheeled mode; on other surfaces, the wheels transformed into the legged configuration enabling the robot to traverse challenging surface conditions effectively. ARMoR successfully traversed all terrains, and the traversal speeds were measured.
LogKT: Hybrid Log Anomaly Detection Method for Cloud Data Center

Log anomaly detection is a fairly indispensable log analysis task for reliability and maintainability in cloud data center. By performing tasks such as log parsing and feature extraction on logs, which are common and valid data, a model with self-judgment capability can be trained for log anomaly detection. Improving the model used for anomaly detection is the main line of research in the current anomaly detection field. However, the data set partitioning method during anomaly detection also has an important impact on the results of anomaly detection, which should be given more considerations. Most of the existing anomaly detection models are single-architecture models, which cannot make full use of the multiple forms of information that logs have. This paper proposes a hybrid anomaly detection method, named LogKT, which is divided into two parts. First, a new dataset partitioning method is constructed based on time-series, randomness and imbalances of logs. It is a dataset partitioning method that fits the characteristics of log anomaly detection from the aspects of time-series feature preservation, sampling range expansion and training method change. Then, we further propose a hybrid anomaly detection model based on a Transformer and Bi-LSTM models, which can extract features from multiple information of logs and can fit well with the dataset partitioning method. Finally, we perform validation experiments on two public datasets, and the experimental results show that our LogKT approach has superior anomaly detection accuracy compared with baseline methods.
A Review on Community Detection Approach for Recommender Systems

In the last few years, Recommender systems have become extremely popular for users to extract relevant information. The applicability of recommender systems is in several domains, including e-commerce sites, social media platforms and several others. The Community detection approaches aim to detect the collective users that have similar behavior, interest, and choice. Community detection technique helps in creating a new dimension for recommender systems. This paper presents a thorough review of the Community detection approaches for recommender systems. The challenges and research directions are also discussed in the later part of the paper.
Matching asymptotic method and nucleation of a defect at a notch

In the present study, we use matching asymptotic expansion to treat the elastic problem associated with a small defect located at the tip of a notch. The use of such asymptotic methods is necessary because the presence both of small parameters and singularities leads to inaccurate computations by classical finite element methods. The method is applied here in an antiplane setting.
Collision Detection between Spheres and B-spline Surfaces using Distance Functions from Curves

Collision detection is one of the most important aspects of Computer Graphics, CAD, games, or particle simulations. This paper presents collision detection between spheres and B-spline surfaces. The representative surfaces, Bézier, B-spline surfaces and NURBS, are defined by control points and they are often rendered by polygonal approximation. This paper introduces accurate and effective collision detection between spheres (circles) and surfaces (curves) without polygonal approximation. The distance between curves and points is represented by a Bézier function. The calculation of the shortest distance using the convex hull obtained from the control points of this function enables a more efficient collision detection. This proposed method is installed to the commercial software to confirm this is a software to confirm this is a practical and effective algorithm.
Benchmarking and Reconciliation of Time Series: An Applied Bayesian Method

The present article features a hierarchical Bayes method applied to solving problems of benchmarking and contemporaneous reconciliation across time series. This method enables the use of high frequency series to be either approximations or one or several related indicators. This method may be applied when facing flow or index disaggregation problems. The authors compare their results to classical procedures (viz., Denton univariate and Rossi multivariate methods) through the use of indicators. This article concludes that the suggested method bestows greater importance on the low frequency series profile, consequently providing smoother solutions than its counterparts.
Understanding the Influence of Social Hubs in Diffusion Processes Driven by Incentivized Friend Invitation

With the prevalence of social media, rewarding users to invite their friends to use an application or adopt a service has become one of the most successful designs in recent years. As the application or service spreads, a diffusion process driven by incentivized invitation forms. However, despite the extensive attention from both industry and academia, this newly-emerging diffusion process remains under-explored. To bridge this gap between academic research and industrial practice, we set out to examine the influence of social hubs, i.e., people with a considerable number of social ties, in the diffusion process driven by incentivized friend invitation. Through a large-scale empirical data analysis, we found that under three different measures, the influence of social hubs on the growth of the diffusion process is saturated as their number of contacts increases. Following this observation, we further explored possible causes of this phenomenon by a mixed method that combines a large-scale empirical data analysis and a semi-structured interview to deepen our understanding of social hubs’ influence in the diffusion process. We revealed two main causes. First, large social hubs lacked the will to keep engaging in sending invitations, and the anchoring effects on the cost-utility trade-off could explain their behaviors. Second, social hubs’ broadcasting capability decreased in the invitation-based diffusion process because hubs are better at informing rather than persuading compared with non-hubs. Our findings provide in-depth insights into the understanding of the nature of social hubs’ influence, and we further discuss practical implications for designs to facilitate invitation-based online diffusion processes.
Profit Prediction for Businesses using Machine Learning Algorithms

A framework to predict profit is used to help businesses in a profit-friendly decision making process. This framework is an end-to-end machine learning model which processes the existing data set and compares the result with help of machine learning algorithms and gives the best-predicted value of economic profit. The variables such as expenditure incurred on Research and Development, Marketing, Operations, Sales, Administration etc. that affect the profit margin of a business are determined. Machine learning algorithms such as Linear Regression, Multiple Linear Regression, Support Vector Regression, Random Forest Regression have been used to predict the profit. A random dataset is generated and a comparative study of the algorithms has been carried out and the best accuracy score is obtained to predict the profit using Multiple Linear Regression.
INFLATION TARGETING: TO FORECAST OR TO SIMULATE?

Inflation targeting is a regime based to a great extent on communication and, more specifically, on using and communicating assessments of future inflation. The central banking literature, however, devotes surprisingly little attention to some important issues connected with such assessments. There are some non-trivial choices that need to be made regarding future inflation assessments on three distinct levels: construction, decision making and communication. One of the most important choices relates to the treatment of the central bank's behaviour within the assessment. We first differentiate between two basic ways of assessing future inflation: forecast and simulation. A forecast is the most likely picture of the future. In a forecast, all agents are assumed to behave in the most likely way. A simulation, on the other hand, is the most likely picture of the future if the behaviour of one agent follows a predetermined path or is generated using a selected reaction function. The path or reaction function ascribed to the agent does not have to be the most likely one. After differentiating between a forecast and a simulation, we discuss the pros and cons of using the two ways of assessing future inflation on the three abovementioned levels.
Early alert of an outbreak based on event detection and tracking on social networks: The case of Covid-19 and meningitis

In this paper, we propose an early warning approach for the occurrence of meningitis or Covid-19 epidemics based on the analysis of Twitter messages (tweets). To achieve this, we initially employed automatic classification techniques using Natural Language Processing and domain ontologies to filter the tweets. Subsequently, we utilized the metadata of the tweets to geolocate them and extract their publication dates. The real-time collection of tweets is facilitated through Kafka’s ecosystem.For the real-time identification of tweets, we trained SVM and CNN models. Our best results were obtained with the CNN model, which yielded an accuracy of 0.99. Tweets pertaining to infections are then geolocated by considering various key elements within the tweet that can provide information about the location.
Extracting Decision Tree From Trained Deep Reinforcement Learning in Traffic Signal Control

Deep reinforcement learning (DRL) has achieved impressive success in traffic signal control systems (TSCS). However, since a key component of many DRL models is the complex deep neural networks (DNNs), it hinders humans or experts from understanding and explaining the learned policy. Recently, many works have focused on developing interpretable techniques to compress or distill complex DNNs into smaller, faster, or more understandable models. The decision trees (DTs) are viewed as the de facto technique for interpretable and transparent machine learning, and can provide an easy-understanding decision path from the root to the leaf node. In this work, we utilize modified DTs to extract models with simpler hierarchical structures from premium policy achieved by DRL methods. First, we use a DRL algorithm to learn a premium policy for traffic signal control. Then, we collect a dataset with the learned premium policy by interacting with the environment. Finally, we extract the DTs with the collected dataset of state-actions pairs. We evaluate our method on a Simulation of Urban Mobility simulator in a simulation way. Simulation results show that the extracted DTs can generate human-understandable decision processes and provide explicit knowledge from the DNNs reference of the DRL.
Symbiotic Artificial Intelligence: Order Picking And Ambient Sensing

Using egocentric video and head motion data from 67 order picking tasks (244 picks;149 orders), we learn visual models of the 10 objects picked to fulfill the orders. Boundary segmentations of the four actions (pick, carry, place, carry empty) of order picking had an average test RMSE of 1.11 seconds using computer vision and 5.53 seconds using only head motion $( \approx 39.8$ seconds/task). The 10 objects were clustered with 93.8% accuracy using weak supervision provided by the picks (which could occur in any order) specified in the tasks. We apply the 10 resulting models on independent test data to recognize three objects involving 50 tasks (185 picks;98 orders) and 10 objects involving 10 tasks (35 picks;24 orders). Accuracy was up to 90.3% and 69.1%, respectively. We propose order picking as a practical use case of egocentric Symbiotic AI, where ambient sensing is used without explicit supervision to train an agent which can then help the user improve task speed and accuracy.1
Evolving deep neural networks by multi-objective particle swarm optimization for image classification

In recent years, convolutional neural networks (CNNs) have become deeper in order to achieve better classification accuracy in image classification. However, it is difficult to deploy the state-of-the-art deep CNNs for industrial use due to the difficulty of manually fine-tuning the hyperparameters and the trade-off between classification accuracy and computational cost. This paper proposes a novel multi-objective optimization method for evolving state-of-the-art deep CNNs in real-life applications, which automatically evolves the non-dominant solutions at the Pareto front. Three major contributions are made: Firstly, a new encoding strategy is designed to encode one of the best state-of-the-art CNNs; With the classification accuracy and the number of floating point operations as the two objectives, a multi-objective particle swarm optimization method is developed to evolve the non-dominant solutions; Last but not least, a new infrastructure is designed to boost the experiments by concurrently running the experiments on multiple GPUs across multiple machines, and a Python library is developed and released to manage the infrastructure. The experimental results demonstrate that the non-dominant solutions found by the proposed method form a clear Pareto front, and the proposed infrastructure is able to almost linearly reduce the running time.
Dynamic Edge Caching via Online Meta-RL

The content request patterns perceived by edge devices are becoming highly dynamic, especially for emerging short video platforms compared to traditional video platforms. This calls for caching policies that can continuously adapt to dynamic environments, challenging previously popular reinforcement learning (RL)-based policies. A straightforward solution, i.e., repeatedly restarting and training RL agents, would fail to converge timely while meeting the observed adaptation process. Offering transferable knowledge is considered a possible method to speed up the adaptation process. Unfortunately, it fails to outperform the RL-based approach as an alternative solution in these scenarios. To alleviate this drawback, we 1) design a sequential-pair meta-learning for edge caching that captures the meta-knowledge of dynamic changes from sequential-pair-wise intervals, which are segmentations from the whole dynamic episode, and 2) develop an online meta-RL-based solution called Online Meta Actor-Critic (OMAC), which updates the meta-knowledge in an online manner. To evaluate the proposed framework, we conduct trace-driven experiments to demonstrate the effectiveness of our design: it improves the average cache hit rate by up to 37.4% (normalized) compared with other baselines.