Miracle-dz/UniFAR-FTUs huggingface dataset:
STM2CN: A Multi-graph Attention-based Framework for Sensor Data Prediction in Smart Cities

Accurate long-term predictions help governments make decisions and residents travel, which is essential for the development of smart cities. Fortunately, due to the deployment of low-cost sensors, a large amount of time-series data such as parking availability data and air quality data has been stored, which makes it possible for long-term predictions. Many state-of-the-art studies based on multiple graphs have shown excellent performance in long-term prediction tasks. However, few previous studies employ multiple attention mechanisms to their models based on multi-graphs and thus fail to comprehensively capture the dynamic spatio-temporal correlations as well as the inner relationships among graphs. To this end, we propose a spatio-temporal multi-attention multi-graph convolutional network (STM2CN) framework for long-term prediction. We applied four different graphs to mine the potential contextual relationships and employed three attention mechanisms to capture the multiple graph and spatio-temporal correlations. Experiments on two large-scale real-world datasets demonstrate that the proposed STM2CN framework outperformed the state-of-the-art baselines.
Stability of asymptotically AdS wormholes in vacuum against scalar field perturbations

The stability of certain class of asymptotically AdS wormholes in vacuum against scalar field perturbations is analyzed. For a free massive scalar field, the stability of the perturbation is guaranteed provided the squared mass is bounded from below by a negative quantity. Depending on the base manifold of the AdS asymptotics, this lower bound could be more stringent than the Breitenlohner-Freedman bound. An exact expression for the spectrum is found analytically. For a scalar field perturbation with a nonminimal coupling, slow fall-off asymptotic behavior is also allowed, provided the squared mass fulfills certain negative upper bound. Although the Ricci scalar is not constant, an exact expression for the spectrum of the scalar field can also be found, and three different quantizations for the scalar field can be carried out. They are characterized by the fall-off of the scalar field, which can be fast or slow with respect to each asymptotic region. For these perturbations, stability can be achieved in a range of negative squared masses which depends on the base manifold of the AdS asymptotics. This analysis also extends to a class of gravitational solitons with a single conformal boundary.
Optimized quantum error-correction codes for experiments

We identify gauge freedoms in quantum error correction (QEC) codes and introduce strategies for optimal control algorithms to find the gauges which allow the easiest experimental realization. Hereby, the optimal gauge depends on the underlying physical system and the available means to manipulate it. The final goal is to obtain optimal decompositions of QEC codes into elementary operations which can be realized with high experimental fidelities. In the first part of this paper, this subject is studied in a general fashion, while in the second part, a system of trapped ions is treated as a concrete example. A detailed optimization algorithm is explained and various decompositions are presented for the three qubit code, the five qubit code and the seven qubit Steane code.
Multi-modal Face Anti-spoofing Using Multi-fusion Network and Global Depth-wise Convolution

Although elevating the accuracy and efficiency of facial biometric recognition system, it suffers from presentation attacks (PAs) because of its weakness. Currently, popular state- of-the-art face anti-spoofing (FAS) methods using multi-modal learning strategy. Similarly, we propose multi-modal FAS using multi-fusion network (MFN) and global depth-wise convolution (GDConv), FaceBagNetPlus for short. The MFN means that we use the Convolutional Block Attention Module (CBAM) to replace the Squeeze-and-Excitation Network (SE-NET) in the feature extraction part and propose channel spatial cross fusion (CSCF) to cross-fuse modal feature with the pairwise cross approach. Meanwhile, we use the GDConv to replace the global average pooling (GAP) to raise the performance. Then, we use the patch-based strategy to obtain fully feature, the random model feature erasing (RMFE) strategy to avoid over-fitting and multi-stream fusion module to enhance discrimination ability. Next, we perform experiments on the CASIA-SURF dataset, then demonstrate the effectiveness of the MFN and the GDConv. Among all results, we gain the best result of 113 (FP), 4 (FN), 0.2807% (APCER), 0.0229% (NPCER), 0.1518% (ACER), 100.000% (TPR@FPR=10e-2), 100.000% (TPR@FPR=10e-3) and 99.9026% (TPR@FPR=10e-4) on the test set. We also execute experiments on the CASIA-SURF CeFA dataset and receive the best result of 0.0000% (ACER) on the validation set. Finally, two results are superior to the state-of-the-art methods.
Prediction of Key Variables in Wastewater Treatment Plants Using Machine Learning Models

Prediction of key variables is an important part of the monitoring, control, and optimization of industrial processes, since it is important to anticipate certain behaviors so that the correct actions can be taken. To assess which algorithm is best suited to the prediction of a number of key variables at various stages of wastewater treatment plants (WWTP), five computational algorithms were researched: Artificial Neural Network, Long Short-Term Memory, deep learning Transformer model, Adaptive Neuro-Fuzzy Inference System, and Gaussian Mixture Model. With these models, techniques already well established in the state-of-the-art are evaluated, as well as more recent methods that have been exhibiting good performance in variable prediction regression problems. These algorithms were evaluated in four WWTP case studies, in which the objective is to predict the following key variables: total suspended solids, nitrate and nitrite, ammonia and ammonium, and biochemical oxygen demand. The learning process of each algorithm was performed using extensive tests in order to select the input variables, and define the topologies and hyper-parameters of the presented models by cross-validation. The results indicate that it is possible to adequately predict the four variables, and the best results were achieved by the Transformer algorithm, which presents the lower error values in the considered metrics.
Optimal thermo-mechanical reliability design of 2.5D lidless package

2.5 D package has been widely used in electronics packages which using multiple integrated circuit chips placed on an interposer to increase its capacity and performance tremendously than traditional 2D package. The lidless package which using heat sink to contact silicon die directly is an effective option to improve the heat dissipation. In this paper, board-level solder fatigue life was investigated for a lidless 2.5D package in accelerated thermal cycling. A three-dimensional finite element model of a lidless 2.5D package with heat sink was studied under various design parameters. Finite element models were built and validated by warpage measurement using 3D DIC system. Coefficient of thermal expansion (CTE) mismatch between different materials is a main factor in warpage controlling. Due to the demand of increasing bandwidth requirements, extension of silicon interposer is necessary. CTE mismatch between the silicon interposer and substrate and between the substrate and PCB are considered. The heat sink is usually bolted to the board. The influence of number of bolts, pre-load force of bolts and location of bolts are performed. It is found that the pre-load force of bolts and CTE of substrate have important impacts on board-level reliability and design suggestions were given.
An end-to-end approach for scalable real time Anomaly detection in smart buildings

Internet of things (IoT) along with big data technologies can accrue significant added value in several domains and improve people’s everyday life. One of the domains that can be benefitted the most by the aforementioned technologies is Smart Buildings. This is because, several aspects of people’s everyday lives can be improved through IoT services, such as energy consumption, health, heating, building security and more. IoT services can be divided to near real-time, and static based on the time that they require in order to return results. Significant amount of research papers has been dedicated to the second for services such as energy forecasting, while for near real-time services there are not so many publications, while, most of the existing ones focusing mostly on obtaining meaningful results. In this publication we propose a conceptual architecture for building a near real-time Anomaly Detection service for smart buildings using the Fog Computing paradigm, to achieve scalability and low latency. Moreover, we provide a technical glance of the proposed solution, suggesting specific technologies for each functionality as well as restrictions for each technology. It is worth mentioning that the proposed approach can be easily adapted for other near real-time services with little modifications.
@ME: A Fine-grained Route Recommendation System to Grab Impatient Passengers

Data analysis reveals that passengers can only endure a few minutes before taking a taxi. However, most existing route recommendation systems are not adequate to satisfy impatient customers due to two shortcomings: inaccurate demand forecast and the lack of an efficient supply-demand balance mechanism. In this paper, we propose a recommendation system called @ Me, which aims to dispatch vacant taxis to the vicinity of potential customers at the right time. To achieve minute-level demand forecasting, we ensemble a contextualized spatial-temporal network (CSTN) with an LSTM network to optimize prediction accuracy. In addition, we characterize the attractiveness of the road grid to vacant taxis as a force model, on which a taxi scheduling algorithm is proposed to dynamically balance supply and demand. Extensive experiments on real datasets clearly indicate that our method is superior to the selected baselines. Vacant taxis that follow the routes suggested by @ME can catch more impatient customers in a shorter cruising time. The 7 -day experimental results on the Manhattan dataset show that @Me can carry an additional 48,332 passengers and increase drivers' revenue by $570,317.
Kernels on Attributed Networks for Community Detection

Community detection is a primary problem in the study of complex networks. When graphs are enriched with attributes, it has been found that this additional information can help in better understanding the ties among the actors composing the network and provides a deeper insight into group organization. The paper proposes the investigation of a multiobjective genetic algorithm for attributed networks extended with kernel functions for computing node similarity both in terms of structure and features. The commute-time kernel, based on the concept of random walk, is first applied to the adjacency matrix of the graph and then four kernels are applied for computing the similarity between nodes while simultaneously optimizing structure and feature dimensions. Simulations on both synthetic and real-world citation networks show that kernels effectively improve the quality of the resulting partitions.
ClassMate Robot: A Robot to Support Teaching and Learning Activities in Schools

Educational robotics is a field aiming at investigating the use of robots in schools to support teaching and learning activities. While several robotic solutions exist in support of the STEM teaching activities, in this work, we present "Classmate Robot" as a new social robot to be used in the classrooms as a support to the learning experience through interaction. Classmate Robot has been designed and developed to improve the effectiveness of the activities by providing a framework where the robot’s behaviors can be personalized, and learning applications can be easily integrated on top of the robot interaction capabilities. This approach aims to increase the engagement of learners. We introduce the ROS-based architecture developed that is divided into three main layers plus an application layer. As a social robot, it combines several multimodal social cues to interact and communicate with students and teachers. Moreover, the robot is endowed with a set of behaviors designed to be compliant with its role of "classmate" in the interaction with the students.
Moving away from robotic interactions: Evaluation of empathy, emotion and sentiment expressed and detected by computer systems

Social robots are often critiqued as being too ‘robotic’ and unemotional. For affective human-robot interaction (HRI), robots must detect sentiment and express emotion and empathy in return. We explored the extent to which people can detect emotions, empathy and sentiment from speech expressed by a computer system, with a focus on changes in prosody (pitch, tone, volume) and how people identify sentiment from written text, compared to a sentiment analyzer. 89 participants identified empathy, emotion and sentiment from audio and text embedded in a survey. Empathy and sentiment were best expressed in the audio, while emotions were the most difficult detect (75%, 67% and 42% respectively). We found moderate agreement (70%) between the sentiment identified by the participants and the analyzer. There is potential for computer systems to express affect by using changes in prosody, as well as analyzing text to identify sentiment. This may help to further develop affective capabilities and appropriate responses in social robots, in order to avoid ‘robotic’ interactions. Future research should explore how to better express negative sentiment and emotions, while leveraging multi-modal approaches to HRI.
Motivational Gestures in Robot-Assisted Language Learning: A Study of Cognitive Engagement using EEG Brain Activity

Social robots have been shown effective in pedagogical settings due to their embodiment and social behavior that can improve a learner’s motivation and engagement. In this study, the impact of a social robot’s motivational gestures in robot-assisted language learning (RALL) was investigated. Twenty-five university students participated in a language learning task tutored by a NAO robot under two conditions (within-subjects design); in one condition the robot provided positive and negative feedback on participant’s performance using both verbal and non-verbal behavior (Gesture condition), in another condition the robot only employed verbal feedback (No-Gesture condition). To assess cognitive engagement and learning in each condition, we collected EEG brain activity from the participants during the interaction and evaluated their word knowledge during an immediate and delayed post-test. No significant difference was found with respect to cognitive engagement as quantified by the EEG Engagement Index during the practice phase. Similarly, the word test results indicated an overall high performance in both conditions, suggesting similar learning gain regardless of the robot’s gestures. These findings do not provide evidence in favor of robot’s motivational gestures during language learning tasks but at the same time indicate challenges with respect to the design of effective social behavior for pedagogical robots.
Channel Shortening Phenomenon Due to Redox Reaction in a Lateral Direction on In–Ga–Zn–O Thin Film Transistors

We investigated low-resistivity regions in In–Ga–Zn–O layer formed by a redox reaction during post-passivation annealing. The low-resistivity regions formed inside the source and drain electrodes significantly affect the performance parameters of a thin film transistor (TFT), such as the electrically effective channel length and the external resistance of TFT channels. The TFT performance parameters depend on the gate voltage because the low-resistivity regions exhibit a gradual carrier distribution. We approximated the distributions using a function derived from the diffusion equation by fitting the calculated resistance to the measured resistance for several channel lengths. In addition, we acquired the diffusion coefficients for different annealing temperatures from distribution functions. The activation energy of the diffusion coefficients suggest that hydrogen diffusion results in the formation of the low-resistivity region by a reduction reaction in the In–Ga–Zn–O film.
A foundation for analytical developments in the logarithmic region of turbulent channels

An analytical framework for studying the logarithmic region of turbulent channels is formulated. We build on recent findings (Moarref et al., J. Fluid Mech., 734, 2013) that the velocity fluctuations in the logarithmic region can be decomposed into a weighted sum of geometrically self-similar resolvent modes. The resolvent modes and the weights represent the linear amplification mechanisms and the scaling influence of the nonlinear interactions in the Navier-Stokes equations (NSE), respectively (McKeon & Sharma, J. Fluid Mech., 658, 2010). Originating from the NSE, this framework provides an analytical support for Townsend’s attached-eddy model. Our main result is that self-similarity enables order reduction in modeling the logarithmic region by establishing a quantitative link between the self-similar structures and the velocity spectra. Specifically, the energy intensities, the Reynolds stresses, and the energy budget are expressed in terms of the resolvent modes with speeds corresponding to the top of the logarithmic region. The weights of the triad modes -the modes that directly interact via the quadratic nonlinearity in the NSE- are coupled via the interaction coefficients that depend solely on the resolvent modes (McKeon et al., Phys. Fluids, 25, 2013). We use the hierarchies of self-similar modes in the logarithmic region to extend the notion of triad modes to triad hierarchies. It is shown that the interaction coefficients for the triad modes that belong to a triad hierarchy follow an exponential function. The combination of these findings can be used to better understand the dynamics and interaction of flow structures in the logarithmic region. The compatibility of the proposed model with theoretical and experimental results is further discussed.
Explainable Retinopathy Diagnosis and Localisation by means of Class Activation Mapping

Diabetic retinopathy is a disease afflicting the retina and currently is manually diagnosed by specialists through eye tomography inspection. In order to assist the clinician in this time-consuming task, in this paper, we propose a method aimed to automatically diagnose the (proliferative and non-proliferative) diabetic retinopathy by exploiting deep learning. Furthermore, we investigate the possibility to automatically localise the areas related to the disease by exploiting class activation maps. We evaluate different deep learning models from a quantitative point of view (i.e, using metrics like accuracy, precision and recall) and a qualitative point of view (by exploiting class activation maps and image similarity metrics) with the aim to understand the quality of predictions performed by a model in retinopathy diagnosis, reducing the amount of knowledge required to assess the model performance. From the experimental analysis is emerging that deep learning shows an interesting diagnostic potential in the retinopathy disease localisation and can effectively help the clinician in retinopathy diagnosis. Moreover, the adoption of the class activation maps and its comparison evaluation can help the developers to debug the training step of the model without medical expertise.
An Explainable Deep Neural Framework for Trustworthy Network Intrusion Detection

In recent years, there has been an increase in cyber attacks in mobile cloud environment. Intrusion Detection Systems (IDS) have played an important role in protecting mobile cloud security. Many techniques have been utilized to implement IDS, among them, machine learning-based techniques have generated promising results. Especially, complex deep neural networks show a higher detection rate than traditional machine learning models. However, the interpretation of the decision made by a neural network becomes harder to understand as its architectural complexity increases. This challenge makes it difficult for the human experts to fine-tune their detection systems, trust the detection system’s results, and make decisions accordingly when IDS systems are deployed. To address this issue, we propose an explainable intrusion detection framework that employs deep learning mechanisms to identify cyber-attacks and utilizes knowledge graph as the knowledge foundation to add human understanding of machine learning and explain the machine learning results. The use case study demonstrates that the proposed framework can not only successfully identify network intrusions but also effectively reveal important information about its internal working mechanisms of the mysterious deep learning Blackbox.
Speaking Rationally by Gestures: Information Theoretic Insights from Multi-Modal Language Models

The multi-modality nature of human commu-001 nication can be utilized to enhance the per-002 formance of computational language models. 003 However, few studies have explored the non-004 verbal channels with finer theoretical lens. 005 We use multi-modal language models trained 006 against monologue video data to study how the 007 non-verbal expression contributes to communi-008 cation, by examining two aspects: first, whether 009 incorporating gesture representations can im-010 prove the language model’s performance (per-011 plexity), and second, whether the gesture chan-012 nel demonstrates the similar pattern of entropy 013 rate constancy (ERC) found in verbal language, 014 which is governed by Information Theory. We 015 have positive results to support both assump-016 tions. The conclusion is that speakers indeed 017 use simple gestures to convey information that 018 enhances verbal communication, and how this 019 information is organized is a rational process. 020
Region-Speciﬁc Remote-Sensing Models for Predicting Burn Severity, Basal Area Change, and Canopy Cover Change following Fire in the Southwestern United States

: Estimates of burn severity and forest change following wildﬁre are used to determine changes in forest cover, fuels, carbon stocks, soils, wildlife habitat, and to evaluate fuel and ﬁre management strategies and effectiveness. However, current remote-sensing models for assessing burn severity and forest change in the U.S. are generally based on data collected from California, USA, forests and may not be suitable in other forested ecoregions. To address this problem, we collected ﬁeld data from 21 wildﬁres in the American Southwest and developed region-speciﬁc models for assessing post-wildﬁre burn severity and forest change from remotely sensed imagery. We created indices (delta normalized burn ratio (dNBR), relative delta normalized burn ratio (RdNBR), and the relative burn ratio (RBR)) from Landsat and Sentinel-2 satellite imagery using pre- and post-ﬁre image pairs. Burn severity models built from southwest U.S. data had clear advantages compared to the current California-based models. Canopy cover and basal area change models built from southwest U.S. data performed better as continuous predictors but not as categorical predictors.