Miracle-dz/UniFAR-FTUs huggingface dataset:
An Implementation of Reinforcement Learning in Assembly Path Planning based on 3D Point Clouds

3D point clouds consisting of a lot of informatively geometric data have been playing critical roles in many applications such as 3D segmentation, polyline annotation for lane tracking, and especially in manufacturing industry. In particular, this paper proposes to apply Reinforcement Learning (RL) to resolve an automated assembly task based on 3D point cloud data. To address this task, the proposed structure is separated into 2 stages including registration stage and assembly path planning stage. Firstly, in the registration stage, one of the objects is matched to an assembled model to determine the transformation between two 3D point clouds by using RANdom Sample Consensus (RANSAC) and Iterative Closet Point (ICP). Secondly, we employ Q-learning method to train a model to make optimal decisions in assemble path planning task. The entire optimized assembly path planning task has been successfully accomplished for typical objects. Finally, the performance of the approach developed in this paper has been validated by experiments.
Building Vietnamese Linguistic Resources for Social Network Text Analysis

In this paper, we report our work on building linguistic resources for Vietnamese social network text analysis in multiple domains. We first describe our annotation methodology including guidelines development, annotation softwares and quality assurance. We then present results of the first pilot phase of the project. Finally, we outline some perspectives of our ongoing corpus development and their expected results.
Designing Basic Ontologies for Training Specialists in Interdisciplinary Knowledge Domains

Ontologies are the basis for representing knowledge domain structures. The design of ontologies is a multistage process with the participation of a number of specialists: analysts, experts in areas of knowledge, knowledge engineers, linguists, etc. The stages of ontology design and examples of knowledge areas for training in an integrated automated environment are presented.
Building a Spelling Checker for Documents in Khmer Language

The practice of Khmer language teaching at Tra Vinh University shows that students often deal with many difficulties when making reports on subjects of Khmer language. The need to print, edit Khmer text with MS Word by using conventional typing on the ASCII keyboard and the Unicode fonts available made students have many spelling errors, ranging from syllable level to vocabulary level, the syntax of the sentence. Students have not correctly identified the various syllables in word structures, encountered errors in pronunciation and spellings in Khmer. In fact, Khmer-Vietnamese community in Vietnam has few people, scattered, culturally and economically limited. Moreover, Khmer writing system is complex, from the rich alphabet to the multi-syllable structure of the syllable, with no whitespace separated from the sentences. There are many borrowed words which have changed much from the spoken words in Cambodian origins, so the users face many difficulties. These reasons have been a major obstacle in the process of dealing with Khmer language in Vietnam. So far, there have been no solutions to meet the demand for spelling checking in the Khmer scripts. Based on the actual analysis of the students' common misspellings, it can be proposed a solution to the application of the syllable model, the syllabus processing algorithm using the Khmer vocabulary that has been authored by the authors. Previously built KSC (KhmerSpellChecker), an automatic spell checker, integrated into MS Word, helps learners to compose Khmer texts. The approach is feasible. KSC was installed and test results were tested by Khmer language experts at Tra Vinh University for high accuracy.
Simultaneous Wavelength and Format Conversions Based on the Polarization-Insensitive FWM in Free-Space Optical Communication Network

Free-space optical (FSO) network is an effective way to enhance the interoperability and improve the resource utilization of the FSO communication systems. Since the structure is relatively simple and the nodes are not so many, wavelength conversion-based optical circuit switching (WC-OCS) is an appropriate candidate for the FSO network to solve the blocking problem. Meanwhile, format conversion to lower the data rate is also necessary in case the atmospheric channel deteriorates. In this paper, we construct a 3-node FSO network and experimentally investigate the feasibility of simultaneous WC-OCS and format conversion based on straight-lined single-pump four-wave mixing (FWM) process. The bit-error-ratio (BER) results of the on-off key, binary phase-shift keying (BPSK), and quadrature phase-shift keying (QPSK) signals show that comparing to the corresponding back-to-back (B2B) transmission at the reference BER level of ${10^{-7}}$, power penalties caused by WC-OCS are less than 1-dB for all the three formats. In the format conversion experiment from QPSK to BPSK, the power penalties, comparing to the original BPSK signal after B2B and WC-OCS transmission, are also less than 1 dB at the ${10^{ - 7}}$ reference BER level. Consequently the feasibility and practicability of the FWM-based optical signal processing at FSO network nodes are experimentally validated.
Correlation Analysis of Popularity and Interoperability in Open Source Projects

In order to examine the success of open source projects, we analyzed 252,008 projects in the SourceForge database. We restricted our study to projects that are written in the top ranked programming languages. We measured the correlation between popularity, interoperability, productivity and the success rate of each project. The developers' contribution to the project was also examined in terms of the developer’s team size, and the success in terms of the total number of committers (contributors). Our results indicate that using multiple programming languages requires additional team members. Also, there is a significant increase in the functionality of the developed project. The results also demonstrated that there is a positive correlation between the development team size, and the total number of committers.
CoinMingle: A Decentralized Coin Mixing Scheme with a Mutual Recognition Delegation Strategy

Bitcoin is the first decentralized cryptocurrency proposed by Satoshi Nakamoto. Although transactions are conducted between pseudonyms, Bitcoin cannot offer strong privacy guarantees. Mixing coins, to some extent, can address this drawback through different technologies, but also meets flexibility, storage and anonymity problems. This paper proposes a decentralized coin mixing scheme CoinMingle, based on ring signature, one-time output address, CoinJoin and our mutual recognition delegation strategy. Our mutual recognition delegation strategy is simple but effective: when a user prepares to submit messages, he will delegate it to other users for anonymity. Users in CoinMingle don’t need pre-compute. And CoinMingle can tolerate different input amounts and plug the leak of personal information in shuffling phase. In addition, CoinMingle owns parallel structure which is more efficient than other coin mixing schemes.
Recurrent Metric Networks and Batch Multiple Hypothesis for Multi-Object Tracking

Multi-object tracking aims to recover the object trajectories, given multiple detections in video frames. Object feature extraction and similarity metric are the two keys to reliably associate trajectories. In this paper, we propose the recurrent metric network (RMNet), a convolutional neural network-recurrent neural network-based similarity metric framework for the multi-object tracking. Given a reference object, the RMNet takes as input random positive and negative detections and outputs similarity scores over time. The RMNet handles the long-term temporal object variations and false object detections by its long–short memory units. With the scores from RMNet, we introduce a batch multiple hypothesis (BMH) strategy, a simple yet efficient data association method for the batch multi-object tracking. BMH generates a hypothesis tree for each object with a dual-threshold hypothesis generation approach and, then, selects the best branch (or hypothesis) for each object as the batch tracking result. Specially, we model the hypothesis selection as a 0–1 programming problem and introduce a reward function to re-find the objects in case of missing detection. We evaluate our RMNet and BMH strategy on several popular datasets: 2DMOT2015, PETS2009, TUD, and KITTI. We achieve a performance comparable or superior to those of the state-of-the-art methods.
Secrecy Performance of Multi-Antenna Wiretap Channels With Diversity Combining Over Correlated Rayleigh Fading Channels

This paper presents a detailed secrecy performance analysis of correlated multi-antenna wiretap channels with three popular diversity combining schemes, namely maximal ratio combining, selection combining, and equal gain combining at the legitimate receiver. For single-input multiple-output wiretap channels, both exact and asymptotic expressions are derived for the secrecy outage probability (SOP) of these systems. The findings suggest that, compared with the scenario where all the channels are independent, correlation of the main channels alone increases the SOP by a factor of <inline-formula> <tex-math notation="LaTeX">$1/\det (U)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\det (U)$ </tex-math></inline-formula> is the determinant of correlation matrix <inline-formula> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula>. In contrast, when the average SNR at the legitimate receiver is much larger than that at the eavesdropper, correlation between the main channels and the eavesdropper channels has a positive effect on SOP. Moreover, in the case of multiple-input and multiple-output wiretap channels, two transmit antenna selection schemes with or without the eavesdropper’s channel state information are evaluated by the SOP and the average secrecy capacity for the considered correlated channel model, respectively. Finally, numerical simulations are conducted to corroborate the analytical results.
Path Tracking Error Analysis for Underwater Glider Navigation in a Spatially and Temporally Varying Flow Field

In this paper, we presented path tracking error comparison between two navigation schemes for underwater glider that combine path planning and a path tracking controller. Path planning is performed with the time-averaged flow field for both schemes, while path tracking is performed with the instantaneous or the time-averaged flow field. Mathematical analysis and numerical simulation show that performing path tracking control with instantaneous flow results in smaller path tracking error when the strength of spatially varying flow is small compared to glider speed, while it leads to larger path tracking error if spatially varying flow is the same order as the glider speed. Performing path tracking control with instantaneous flow leads to less path tracking error when the spatially varying flow is of relatively high frequency, but it produces larger path tracking error if the spatially varying flow is of low frequency. Simulated experiments near Cape Hatteras, NC show that in this specific region, performing path tracking control with instantaneous flow results in less path tracking error, which is consistent with the analytical result.
Learning Bilevel Layer Priors for Single Image Rain Streaks Removal

Rain streaks removal is an important issue of the outdoor vision system and recently has been investigated extensively. In the past decades, maximum a posterior and network-based architecture have been attracting considerable attention for this problem. However, it is challenging to establish effective regularization priors and the cost function with complex prior is hard to optimize. On the other hand, it is still hard to incorporate data-dependent information into conventional numerical iterations. To partially address the above limits and inspired by the leader–follower gaming perspective, we introduce an unrolling strategy to incorporate data-dependent network architectures into the established iterations, i.e., a learning bilevel layer priors method to jointly investigate the learnable feasibility and optimality of rain streaks removal problem. Both visual and quantitative comparison results demonstrate that our method outperforms the state of the art.
Error Samplers for Lattice-Based Cryptography -Challenges, Vulnerabilities and Solutions

Lattice based cryptography (LBC) stands out today as one of the most promising types of post-quantum cryptography, and a strong contender in the ongoing NIST post-quantum cryptography standardisation process. LBC algorithms are advantageous due to their efficiency, versatility and the hardness of their underlying lattice problems. In this work, the practicality of LBC is explored by surveying one of the critical components, the error samplers, and highlighting the challenges associated with their efficient, secure implementation. Side channel attack (SCA) vulnerabilities and associated countermeasures are considered, concluding with error sampler recommendations, to aid the practicality, security and future widespread deployment of LBC.
Exploring Augmented Reality Interaction for Everyday Multipurpose Wearable Robots

Multipurpose wearable robots are an emerging class of devices, which offer intriguing interaction potential. The interoperability of multipurpose wearable robots and other types of wearable devices remains largely uninvestigated. We take the first steps to bridge this gap by presenting a framework for integrating augmented reality (AR) and multipurpose wearable robots. Our framework uses the publisher-subscriber model to expose different robot functionalities as services on a network. These can be invoked by the AR system. This model is advantageous in coping with different robot morphologies and various interaction methods. We implemented a prototype system using our framework by integrating an AR head-mounted display (HMD) and a wrist-worn robot, and demonstrate four experiences utilizing our prototype solution: 1) Robot status display, 2) shape-changing menus, 3) a media player and 4) a robot pose controller. To evaluate our approach, we performed a user study, which gauged user impressions and usability of developed experiences. Results indicate that our approach was well received, though participants highlighted a number of challenges in AR tracking when interacting within some of the experiences. Lastly, we discuss limitations and future research direction for our project.
A Low-Power High Linear RF Mixer for 2.4GHz Low-Rate WPAN Applications

The IEEE 802.15. 4 Low-Rate Wireless Personal Area Network (LR-WPAN) standard dictates stringent requirements on performance and cost. The standard is designed to cover a small area of coverage (10m), within the personal accessible space of the user. The LR-WPAN systems must be smaller in size to fit in the accessible area and also must be able to communicate with other wireless systems nearby, thus creating a wireless personal area network for the user. This personal communication network is the base for the Internet-of-Things (IoT) applications. These wireless systems must be of low cost and also provide the highest quality of performance to the user. A low cost chip poses the challenge of reduction in the area of the Silicon chip without affecting the performance of the system. This challenge is addressed in this paper by introducing a low cost, low-power, high-performance mixer for the LR-WPAN application. The proposed mixer circuit employs bulk biasing and active degeneration for linearity improvement. The analysis of the circuit for the gain and linearity is given for the understanding of the circuit operation. The mixer is designed and implemented in 180nm UMC CMOS technology using Cadence SpectreRF tool with probe pads for bare die measurement. The mixer offers a conversion gain of 15. 08dB with the output referred third-order intercept point (OIP3) at 21. 28dBm and a single-sideband (SSB) noise figure (NF) of 14.01dB. The performance is achieved by consuming only 3. 52mW of power from a 1. 8V supply. The Figure-of-Merit is calculated for performance comparison, and it is found to be better than some of the other mixer circuits reported in the literature. The proposed mixer circuit occupies a core area of 23.98 μm × 49.95 μm without the power rails and the probe pads.
Modeling Techniques for Electronic Noise and Process Variability in Nanoscale Devices

Modeling random variations in semiconductor devices becomes increasingly important since at the nanometer scale the intrinsic granularity of matter makes the use of average quantities to be affected by high uncertainty. In this paper we present a unified framework to address statistical variations of a semiconductor device performance due to both microscopic noise sources (noise analysis) and random variations of technological parameters (variability analysis), highlighting that often the two phenomena are intermixed. We present the simulation through variability analysis of Random Telegraph Noise (RTN), traced back to randomly occupied localized traps located close to the Si/SiO2interface, especially relevant for advanced floating gate non-volatile memories.
On Improving Recovery Performance in Multiple Measurement Vector Having Dependency

The multiple measurement vector (MMV) problem is applicable in a wide range of applications such as photoplethysmography (PPG), remote PPG measurement, heart rate estimation, and directional arrival estimation of multiple sources. Measurements in the aforementioned applications exhibit a dependency structure, which is not considered in the general MMV algorithms. Modeling the dependency or the correlation structure of the solution matrix to MMV problems can increase the recovery performance. The solution matrix <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> can be decomposed into a mixing matrix <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> and a sparse matrix with independent columns <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>. The key idea of this model is that the matrix S can be sparser than the mixing matrix <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula>. Previous MMV algorithms did not consider such a structure for <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>. This paper proposes two algorithms, which are based on orthogonal matching pursuit and basis pursuit, and derives the exact recovery guarantee conditions for both approaches. We compare the simulation results of the proposed algorithms with the conventional algorithms and show that the proposed algorithms outperform previous algorithms especially in the case of the low number of measurements.
CXNet-m1: Anomaly Detection on Chest X-Rays With Image-Based Deep Learning

Detecting anomaly of chest X-ray images by advanced technologies, such as deep learning, is an urgent need to improve the work efficiency and diagnosis accuracy. Fine-tuning existing deep learning networks for medical image processing suffers from over-fitting and low transfer efficiency. To overcome such limitations, we design a hierarchical convolutional neural network (CNN) structure for ChestX-ray14 and propose a new network CXNet-m1, which is much shorter, thinner but more powerful than fine-tuning. We also raise a novel loss function sin-loss, which can learn discriminative information from misclassified and indistinguishable images. Besides, we optimize the convolutional kernels of CXNet-m1 to achieve better classification accuracy. The experimental results show that our light model CXNet-m1 with sin-loss function achieves better accuracy rate, recall rate, F1-score, and AUC value. It illustrates that designing a proper CNN is better than fine-tuning deep networks, and the increase of training data is vital to enhance the performance of CNN.
A QoS-supported Multi-constrained Routing Strategy Based on Ant-Colony Optimization for Named Data Networking

Named data networking (NDN) is a typical representation and implementation of content-centric networking (CCN) and serves as a basis for the next-generation Internet. To the best of our knowledge, very few studies have considered multi-metric constraints on routing strategies. Therefore, This paper proposes a multi-constrained routing strategy based on ant-colony optimization (ACO) to satisfy the multi-metric requirements for supporting the QoS of NDN. In the proposed routing strategy, namely ACO-McQoS, bandwidth, delay, and cost are defined as metrics. An optimal combination of these metrics is chosen to accommodate different QoS requests. An extensive simulation is conducted among the shortest path routing, maximum bandwidth path routing, random path routing, and ACO-McQoS. The simulation results show that the proposed routing strategy outperforms other.
Spherical Coverage Characterization of 5G Millimeter Wave User Equipment With 3GPP Specifications

Millimeter-wave (mmWave) frequency bands are promising candidate spectrum for the fifth-generation (5G) mobile communication system, but it requires high directional antenna systems to be applied to the base station and the user equipment (UE) for compensating the high path loss. Due to the randomness of mobile wireless channels, antenna systems in a mobile UE must own a large spherical coverage, which raises new challenges for the performance characterization of 5G mmWave UEs. In the latest specification of the Third-Generation Partnership Project (3GPP), the requirement on UE’s spherical coverage in mmWave frequencies is defined, which is evaluated with the cumulative distribution function of the effective isotropic radiated power. In this paper, the spherical coverage of mmWave UEs is characterized based on the specification of 3GPP, where the impact of device integration, antenna topologies, and user body blockage on the spherical coverage of UE will be analyzed with simulation and measurement results.
Lattice Specific Heat of Graphene

Now a day’s graphene is of keen interest for its application in carbon nanotube Field Effect Transistors, Graphene Transistors etc. Graphene is a single layer of graphite which is 2D in structure. In this paper, we are focusing on the thermal property of graphene. For 3D crystalline graphite and 2D graphene layers, the dominant contribution for the heat capacity comes from the phonons. This is because the electronic contribution to the heat capacity is very small and hence can be essentially neglected, even at low temperatures. In this paper we have calculated the lattice specific heat of graphene in a simpler method and studied its variation with respect to the number of nearest neighbors considered for the calculation as well as temperature.