Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart-Hop Arbitration Request Propagation

SMART-based NoC designs achieve ultra-low latencies by enabling flits to traverse multiple hops within a single clock cycle. Notwithstanding the clear performance benefits, SMART-based NoCs suffer from several shortcomings: each router must arbitrate among a quadratic number of requests, which leads to high costs; each router independently makes its own arbitration decisions, which leads to a problem called false negatives that causes throughput loss. In this article, we propose a new SMART-based NoC design called SHARP that overcomes these shortcomings. Our evaluation demonstrates that SHARP increases throughput by up to 19% and average link utilization by up to 24% by avoiding false negatives. By avoiding quadratic arbitration, our evaluation further demonstrates that SHARP reduces the wiring and area overhead significantly.
Checkerboard patterns with black rectangles

Checkerboard patterns with black rectangles can be derived from quad meshes with orthogonal diagonals. First, we present an initial theoretical analysis of these quad meshes. The analysis reveals many possible applications in geometry processing and also motivates the numerical optimization for aesthetic and functional checkerboard pattern design. Second, we describe an optimization algorithm that transforms initial 2D and 3D quad meshes into quad meshes with orthogonal diagonals. Third, we present a 2D checkerboard pattern design framework based on integer programming inspired by the logo design of the 2020 Olympic games. Our results show a variety of 2D and 3D checkerboard patterns that can be derived from 2D or 3D quad meshes with orthogonal diagonals.
Towards a Semantic Social Approach to enrich the Learner's Profile in Human Learning Environments

E-learning systems have benefited greatly from the concepts of the social web and the emerging technologies of the web semantic (2.0). In this context; the research questions that arise are as follows: How to effectively use the learner's social network to derive information from his profile? How to benefit from this information (knowledge) to enrich his profile in the learning environment? How to exploit learners' knowledge for recommending educational resources in a human learning environment? Our goal is to develop automatic learning environment. It is based on the semantic analysis of the social interactions that takes place between users-users and users-resources, the analysis is based on detecting communities of the semantic data that represents the users and their areas of interest. Further, it provides automatically the different recommendations with respect to the users interests, as well it proposes interactions with other users in the system. This system ensures that these recommendations will certainly improve the learning process by providing students with the best learning practices, the desirable collaborators, and the relevant resources that fit better their needs.
Learning a Mixture of Granularity-Specific Experts for Fine-Grained Categorization

We aim to divide the problem space of fine-grained recognition into some specific regions. To achieve this, we develop a unified framework based on a mixture of experts. Due to limited data available for the fine-grained recognition problem, it is not feasible to learn diverse experts by using a data division strategy. To tackle the problem, we promote diversity among experts by combing an expert gradually-enhanced learning strategy and a Kullback–Leibler divergence based constraint. The strategy learns new experts on the dataset with the prior knowledge from former experts and adds them to the model sequentially, while the introduced constraint forces the experts to produce diverse prediction distribution. These drive the experts to learn the task from different aspects, making them specialized in different subspace problems. Experiments show that the resulting model improves the classification performance and achieves the state-of-the-art performance on several fine-grained benchmark datasets.
Learning body shape variation in physics-based characters

Recently, deep reinforcement learning (DRL) has attracted great attention in designing controllers for physics-based characters. Despite the recent success of DRL, the learned controller is viable for a single character. Changes in body size and proportions require learning controllers from scratch. In this paper, we present a new method of learning parametric controllers for body shape variation. A single parametric controller enables us to simulate and control various characters having different heights, weights, and body proportions. The users are allowed to create new characters through body shape parameters, and they can control the characters immediately. Our characters can also change their body shapes on the fly during simulation. The key to the success of our approach includes the adaptive sampling of body shapes that tackles the challenges in learning parametric controllers, which relies on the marginal value function that measures control capabilities of body shapes. We demonstrate parametric controllers for various physically simulated characters such as bipeds, quadrupeds, and underwater animals.
EAPA: Efficient Attestation Resilient to Physical Attacks for IoT Devices

The wide deployment of devices in Internet of Things (IoT) not only brings many benefits, but also incurs some security challenges. Remote attestation becomes an attractive method to guarantee the security of IoT devices. Unfortunately, most current attestation schemes only focus on the software attacks, but cannot detect the physical attacks. Several remote attestation schemes resilient to physical attacks still have some drawbacks in energy consumption, runtime, and security. In this paper, we propose an Efficient Attestation scheme resilient to Physical Attacks (EAPA) for IoT devices. We exploit a distributed attestation mode to make the protocol be executed in parallel, which reduces the total runtime to $O(1)$. Besides, we introduce an accusation mechanism to report compromised devices and design a new key update method, ensuring the efficiency and the security of our scheme. Furthermore, we present the security analysis and the performance evaluation of EAPA. The results indicate that EAPA has the lowest energy and runtime consumption compared with related works. Particularly, it shows a constant value in terms of runtime consumption.
Tomographic projector

Over the past century, as display evolved, people have demanded more realistic and immersive experiences in theaters. Here, we present a tomographic projector for a volumetric display system that accommodates large audiences while providing a uniform experience. The tomographic projector combines high-speed digital micromirror and three spatial light modulators to refresh projection images at 7200 Hz. With synchronization of the tomographic projector and wearable focus-tunable eyepieces, the presented system can reconstruct 60 focal planes for volumetric representation right in front of audiences. We demonstrate proof of concept of the proposed system by implementing a miniaturized theater environment. Experimentally, we show that this system has wide expressible depth range with focus cues from 25 cm to optical infinity with sufficient tolerance while preserving high resolution and contrast. We also confirm that the proposed system provides uniform experience in a wide range of viewing zone through simulation and experiment. Additionally, the tomographic projector has capability to equalize vergence state that varies in conventional stereoscopic 3D theater according to viewing position as well as interpupillary distance. This study is concluded with thorough discussion about tomographic projectors in terms of challenges and research issues.
BxDF material acquisition, representation, and rendering for VR and design

Photorealistic and physically-based rendering of real-world environments with high fidelity materials is important to a range of applications, including special effects, architectural modelling, cultural heritage, computer games, automotive design, and virtual reality (VR). Our perception of the world depends on lighting and surface material characteristics, which determine how the light is reflected, scattered, and absorbed. In order to reproduce appearance, we must therefore understand all the ways objects interact with light, and the acquisition and representation of materials has thus been an important part of computer graphics from early days. Nevertheless, no material model nor acquisition setup is without limitations in terms of the variety of materials represented, and different approaches vary widely in terms of compatibility and ease of use. In this course, we describe the state of the art in material appearance acquisition and modelling, ranging from mathematical BSDFs to data-driven capture and representation of anisotropic materials, and volumetric/thread models for patterned fabrics. We further address the problem of material appearance constancy across different rendering platforms. We present two case studies in architectural and interior design. The first study demonstrates Yulio, a new platform for the creation, delivery, and visualization of acquired material models and reverse engineered cloth models in immersive VR experiences. The second study shows an end-to-end process of capture and data-driven BSDF representation using the physically-based Radiance system for lighting simulation and rendering.
Material-adapted refinable basis functions for elasticity simulation

In this paper, we introduce a hierarchical construction of material-adapted refinable basis functions and associated wavelets to offer efficient coarse-graining of linear elastic objects. While spectral methods rely on global basis functions to restrict the number of degrees of freedom, our basis functions are locally supported; yet, unlike typical polynomial basis functions, they are adapted to the material inhomogeneity of the elastic object to better capture its physical properties and behavior. In particular, they share spectral approximation properties with eigenfunctions, offering a good compromise between computational complexity and accuracy. Their construction involves only linear algebra and follows a fine-to-coarse approach, leading to a block-diagonalization of the stiffness matrix where each block corresponds to an intermediate scale space of the elastic object. Once this hierarchy has been precomputed, we can simulate an object at runtime on very coarse resolution grids and still capture the correct physical behavior, with orders of magnitude speedup compared to a fine simulation. We show on a variety of heterogeneous materials that our approach outperforms all previous coarse-graining methods for elasticity.
Assessing E-Learning Applications as an Effective Tool in Engaging Auditory Processing Disorder learners for Educators

With the limited resources and training for educators handling APD learners, numerous educators struggle in understanding the AP disorder. Also, they are struggling in providing mechanism and strategies to engage students who has APD. After conducting assessment on the different e-learning tools for auditory disorders, it was assumed that the teacher's confidence level in handling students with APD would increase and would be effective in engaging learners. Seven (7) LSEN teacher validated the different e-learning applications, four (4) e-learning applications were assessed based on the content design principle. Based on the assessment conducted, the respondents tested the application and compare the level of engagement feature the different e-learning tools could provide. It was found out that the utilization of e-learning tool specifically the Hearbuilder e-learning tool proves to be helpful and encourages engagement with ADP learners.
Experimental research on emotion recognition based on brain-computer interface and brain waves

Emotion recognition and classification are important research contents in the field of emotional computing. The current research focuses on the visual field and the speech field, but the accuracy of the emotion recognition and the classification which can be achieved so far is low, which is not enough for commercial applications. At present, due to the rapid progress in research on the brain waves and the brain-computer interfaces, and the great application value in the fields of the medicine and the military, this paper uses the brain electrode caps to collect the brain waves of the human brains under the seven different emotional states. The brain-computer interface transmits the brain wave patterns and the data to the computer, observes the brain waves in the OpenBCI_GUI graphical interface and records the changes in real time. After obtaining the brainwave data under the different emotional states, this paper uses the three statistical methods, such as the AdaBoosting algorithm, to perform the emotional classification on the recorded brainwave data. The experimental results show that the classification effect is good.
Recognizing sound signals through spiking neurons and spike-timing-dependent plasticity

Spiking Neural Networks (SNNs) are regarded as brain-inspired neural networks. Most SNNs described spiking neurons with the leaky integrate-and-fire model, which does not incorporate biological properties of real neurons. In this paper, a model motivated by the human auditory pathway is proposed to explore the possible sound signals recognition mechanism based on the biological dynamic properties of Hodgkin-Huxley (HH) neurons and the spike-timing-dependent-plasticity (STDP) rule of synapses. The first mechanism is that HH neurons have the property of frequency selective response. They only respond to their characteristic frequencies in burst spike trains, which makes the recognition of sound intensity based on the dynamic neurons become possible. The second mechanism is that according to the STDP rule, a synaptic connection structure is formed, and the frequency and the intensity information of input signals are stored in the synaptic delay times. Finally, the neural networks recognize sound signals with spatiotemporal firing patterns.
PoTN: A Novel Blockchain Consensus Protocol with Proof-of-Trust Negotiation in Distributed IoT Networks

As one of the fundamental characteristics of distributed IoT networks, blockchain has attracted tremendous interests from both the academics and industries. Recently, the key to support distributed IoT networks through blockchain is the consensus protocol with multi-miners selection. However, most of them are fixed-miners based, and thus offering opportunities for attackers to launch DoS attack against the fixed miners or manipulate the creation of blocks by bribing the fixed miners. In this paper, we propose a novel consensus protocol called proof-of-trust negotiation (PoTN). With negotiation rules, trust management is introduced to evaluate the trustworthiness of miners. In this basis, the random-honest miners selection can be achieved by the design of trusted random selection algorithm on the miner team. Simulation results show that PoTN is more effective than traditional consensus protocols in block creation.
Security for Internet of Things: A State of the Art on existing Protocols and Open Research issues

Internet of Things (IoT) consists of devices, smart objects, and people that are given a unique identifier connected to the internet via wireless network. These devices are usually resources limited such as battery powered, memory and processing capability etc. The migration of these objects to the internet make them susceptible to various attacks related to external or internal unauthorized devices, which affect physical links, radio hops and routing discovery, give rise to malfunction and resources exhausting. The security mechanisms widely used on the Internet are too heavy to be integrated on small constrained objects. Therefore, we need secure protocols or add a security features to the existing ones with a minimum overhead. Routing also is one of the factors that may impact the security. In this paper, we will focus our work on the State of the art of existing protocols and study their vulnerabilities. Potential solutions based on the blockchain technology are also discussed in this paper.
SoftCon: simulation and control of soft-bodied animals with biomimetic actuators

We present a novel and general framework for the design and control of underwater soft-bodied animals. The whole body of an animal consisting of soft tissues is modeled by tetrahedral and triangular FEM meshes. The contraction of muscles embedded in the soft tissues actuates the body and limbs to move. We present a novel muscle excitation model that mimics the anatomy of muscular hydrostats and their muscle excitation patterns. Our deep reinforcement learning algorithm equipped with the muscle excitation model successfully learned the control policy of soft-bodied animals, which can be physically simulated in real-time, controlled interactively, and resilient to external perturbations. We demonstrate the effectiveness of our approach with various simulated animals including octopuses, lampreys, starfishes, stingrays and cuttlefishes. They learn diverse behaviors such as swimming, grasping, and escaping from a bottle. We also implemented a simple user interface system that allows the user to easily create their creatures.
hTPM: Hybrid Implementation of Trusted Platform Module

Hardware-based TPM provides hardware-backed security solutions and a root of trust for various mission critical applications. However, hardware-based TPM has several intrinsic problems such as extremely low performance, off-chip security vulnerability, and a lack of incident response agility. In the upcoming Quantum computing era, it is critical to provide Quantum-Resistant (QR) cryptography functions without harming performance. Unfortunately, hardware-based TPM's rigid hardware and software architecture model makes it extremely difficult for hardware-based TPM to transition to accommodate future QR cryptographic systems. On the other hand, software-based TPMs (e.g., firmware-based TPM) provide a CPU-based, on-chip security solution. They utilize low-level on-chip primitives offered by chipsets such as ARM TrustZone or Intel Software Guard Extensions (SGX) to build a system with a high-level of trust computing environment. A software-based TPM solution provides higher performance, on-chip security, and incident response agility. However, it is lacking in hardware-backed protection and several vital features such as secure key storage, robustness against side-channel attacks, true random number generation, among others. In addition, its implementation is highly dependent on low-level primitives provided by each hardware vendor, which makes it difficult for it to be provided as a generalized solution. In this paper, we propose hybrid-TPM (hTPM), which fully utilizes the advantages of a hardware-based TPM and diminishes a hardware-based TPM's weaknesses through software-based TPM solutions inside a secure container, e.g., Virtualization-Based Security (VBS). We implemented hTPM as a fully dual mode TPM, i.e., giving end-users full control in choosing between a hardware TPM mode and a software TPM mode based on their needs. We performed and will provide a full risk analysis of the proposed hTPM to show how to best overcome security challenges in realizing hTPM. Finally, we provide a performance analysis of our proposal to show the drastic improvements in cryptographic operations.
Web Application for Generating Image Descriptions Using Natural Language Processing and Computer Vision Techniques

This project involved the creation of a functional, deployable web application that automatically generates descriptions or “captions” for uploaded images. It consists of two parts, the web application that hosts and interfaces with the captioning mechanism and the computational engine that actually handles the image description generation. The web application interface for the caption generation uses the Flask web framework (Python) for the back-end, ReactJS (JavaScript) for the front-end interactions, and ReactBootstrap (CSS) for the user interface styling. The caption generation engine combines some existing CV and NLP engines, namely TensorFlow’s im2txt algorithm and Clarifai’s Image Recognition API in a novel way to generate captions for images.
DReCon

Interactive control of self-balancing, physically simulated humanoids is a long standing problem in the field of real-time character animation. While physical simulation guarantees realistic interactions in the virtual world, simulated characters can appear unnatural if they perform unusual movements in order to maintain balance. Therefore, obtaining a high level of responsiveness to user control, runtime performance, and diversity has often been overlooked in exchange for motion quality. Recent work in the field of deep reinforcement learning has shown that training physically simulated characters to follow motion capture clips can yield high quality tracking results. We propose a two-step approach for building responsive simulated character controllers from unstructured motion capture data. First, meaningful features from the data such as movement direction, heading direction, speed, and locomotion style, are interactively specified and drive a kinematic character controller implemented using motion matching. Second, reinforcement learning is used to train a simulated character controller that is general enough to track the entire distribution of motion that can be generated by the kinematic controller. Our design emphasizes responsiveness to user input, visual quality, and low runtime cost for application in video-games.
Automated Finite State Machine Extraction

Finite state machine (FSM) is a type of computation models widely used in various software programs. Extracting implemented FSMs has many important applications in the networking, software engineering and security domains. In this paper, we first conduct an empirical study to understand how FSMs are implemented in real-world software. Under the guidance of our study results, we then design a static analysis tool, FSMExtractor, to automatically identify and synthesize implemented FSMs. Evaluation using 160 software programs from three sources shows that FSMExtractor can extract all implemented FSMs and report very few false positives.
Repairing man-made meshes via visual driven global optimization with minimum intrusion

3D mesh models created by human users and shared through online platforms and datasets flourish recently. While the creators generally have spent large efforts in modeling the visually appealing shapes with both large scale structures and intricate details, a majority of the meshes are unfortunately flawed in terms of having duplicate faces, mis-oriented regions, disconnected patches, etc., due to multiple factors involving both human errors and software inconsistencies. All these artifacts have severely limited the possible low-level and high-level processing tasks that can be applied to the rich datasets. In this work, we present a novel approach to fix these man-made meshes such that the outputs are guaranteed to be oriented manifold meshes that preserve the original structures, big and small, as much as possible. Our key observation is that the models all visually look meaningful, which leads to our strategy of repairing the flaws while always preserving the visual quality. We apply local refinements and removals only where necessary to achieve minimal intrusion of the original meshes, and global adjustments through robust optimization to ensure the outputs are valid manifold meshes with optimal connections. We test the approach on large-scale 3D datasets, and obtain quality meshes that are more readily usable for further geometry processing tasks.