Miracle-dz/UniFAR-FTUs huggingface dataset:
A Traffic Sign Text Detection System for Pratical Natural Scenes

Up to now, object detection capabilities have recently made extraordinary leaps, thanks to the application of deep learning in the field of computer vision. Currently, most of the object detection algorithms are variations of RPN (Region Proposal Network), and have basically met the requirements of industrial applications. However, in the specific business deployment, there will be many problems, such as more complex actual images, large number of small objects, poor-quality images and so on. To solve these problems, some well-designed operations need to be implemented before the algorithm can be deployed successfully. Aiming at many problems in the actual deployment of traffic sign text detection, this paper proposed a multi-stage detection pipeline, which solves many problems of traffic sign text detection in the actual road conditions. Finally, the AP (Average Precision) in our own test set is 0.9918 (IOU=0.5), which meets the practical requirements.
Low Latency Driven Effective Capacity Analysis for Non-Orthogonal and Orthogonal Spectrum Access

In this paper, we theoretically investigate the performance of non-orthogonal and orthogonal spectrum access protocols (more generically known as NOMA) in supporting ultra-reliable low-latency communications (URLLC). The theory of effective capacity (EC) is adopted as a suitable delay-guaranteed capacity metric to flexibly represent the users' delay requirements. Then, the total EC difference between a downlink user-paired NOMA network and a downlink orthogonal multiple access (OMA) network is analytically studied. Exact closed-form expressions and the approximated closed-forms at high signal-to-noise ratios (SNRs) are derived for both networks and validated through simulation results. It is shown that for a user pair in which two users with the most distinct channel conditions are paired together, NOMA still achieves higher total EC (compared to OMA) in high SNR regime as the user group size becomes larger, although the EC performance of both NOMA and OMA reduces with the increase in group size. It is expected that the derived analytical framework can serve as a useful reference and practical guideline for designing favourable orthogonal and nonorthogonal spectrum access schemes in supporting low-latency services.
Dynamic Network Densification: Overcoming Spatio-Temporal Variability in Wireless Traffic

Network densification, i.e., increasing the density of fixed infrastructure nodes, is the key approach underlying gains in infrastructure-based network capacity and coverage. These gains are a natural result of reducing the distance (and thus improving wireless channels) between end devices and infrastructure and possibly reducing the number of devices per infrastructure node. Unfortunately, wireless traffic demands exhibit high spatio-temporal variability, whence densification may result in deployment of poorly utilized infrastructure resources. One alternative is to leverage the availability of wireless resources whose presence aligns with the mobile traffic demand variations, e.g., high density of vehicles where there is high demand. The promise of such dynamic densification is to both decrease infrastructure costs and deliver improved capacity. This paper proposes a stochastic geometry-based modelling framework to evaluate how the network performance (per user rate superquantiles) depends on the alignment of resources and traffic demands for dynamic network densification.
Towards Personalized Learning Through Class Contextual Factors-Based Exercise Recommendation

The Big Data era and intelligent educational systems have empowered personalized learning. As one of the most effective personalized learning tools, Recommender Systems (RS) are applied for student performance prediction, and personalized content replenishment for learning remediation. A wide variety of context-aware RS for personalized learning have been devised and implemented, adherent with student's learning contexts such as location, time, and activity. Due to the physical constraints, today's education is still carried out at schools, making classes the indispensable and easily achievable context. Leveraging such information can be beneficial for performance improvement and effective learning recommendation in common classroom settings. In this work, we propose a novel approach, ‘Class Contextual Factor’ (CCF)-based RS that synthesizes students' personal and class-level factors for better performances. More specifically, we first derive the CCF from a weighted Q-matrix to estimate students' mastery levels over KCs using an attribute-based recommendation technique. Then, we ensemble an item-based collaborative filtering algorithm for remedial exercise recommendation. By using a real world dataset from an online intelligent tutoring system, evaluations show that our CCF -based method outperforms the popular counterparts (i.e., IRT, RS with collaborative filtering), and is able to provide interpretable results for traceable learning remediation.
Network Coding on Wireless Multipath for Tactile Internet with Latency and Resilience Requirements

Many envisioned applications for the Tactile Internet require latencies that cannot be achieved by current wireless networks. In this paper, we present a method to optimize the reliability of a live stream using multiple paths. Compared to other approaches, our method does not rely on feedback to achieve the reliability and thus does not add delay for retransmission attempts. We use the diversity of multiple paths to compensate for the loss of capacity in individual paths. By using an FEC code to generate redundant data the transmission is more robust against fluctuations in capacity of single paths. We present an optimization problem to find a rate allocation for the paths that minimizes the interruptions of the stream. The performance is evaluated in an emulated network based on real-world measurements. The evaluation shows that the theoretical values can be achieved with a small margin. The optimized allocation of rates to the paths increases the reliability by up to 12%. With coding the reliability can be increased by up to 20%, depending on the code rate.
Reduced Space and Time-Delay Channel State Representation for 5G FDD Massive MIMO Systems

Channel State Information (CSI) feedback is a very relevant topic for Frequency Division Duplexing (FDD) Multiple Input&#x002F; Multiple output (MIMO) on 4G&#x002F;LTE systems and it will be also the case for the development of future Massive MIMO 5G standards. In the following article, we present a novel CSI compression technique based on the potential of Karhunen-Loeve Transform (KLT) to reduce the spatial domain representation by means of a description based on a number of principal eigenvector projections, which is combined with the channel sparsity in time-delay after exploiting frequency domain correlation. The reduced spatial domain information obtained from KLT can be further compressed by using a truncated Fourier component representation of the principal Eigendirections obtained. The algorithm is compared with respect to other CSI description schemes including modern standards like 3GPP Rel 14Adv CSI for FDD. Results show the feasibility of implementation and the benefits of this technique for the future 5G standards.
Soybean response to copper applied to two soils with different levels of organic matter and clay

Abstract Increasing the amount of soil organic matter (OM) alters the availability of copper (Cu) for plants under tropical and subtropical conditions. With the aim of evaluating the effects of the OM/Cu interaction on the soybean crop, a trial was conducted with a fully randomized 2 × 5 factorial design and four replicates. The treatments consisted of five Cu rates (0, 1, 2, 4 and 8 mg kg−1) and two soil types: Typic Oxisol and Typic Ultisol. The soybean responded to fertilization with Cu, producing the highest estimated grain yield at a rate of 4.1 mg kg−1. Similarly were also observed for shoot dry weight, number of pods and root length. The soil chemical properties and nutrient levels in the leaves and grain were influenced only by the soil type, whereas physiological components were affected in terms of photosynthetic rate and intercellular CO2 concentration.
Narrowband Internet of Things (NB-IoT) and LTE Systems Co-Existence Analysis

In this paper, we establish a comprehensive uplink system model for in-band and guard-band Narrowband Internet of Things (NB-IoT) with arbitrary sample duration in the NB-IoT device. The mathematical expressions of received LTE and NB-IoT signals are derived. Moreover, the close-form interference power on the LTE signal from the adjacent NB-IoT signal is given analytically. The result shows that the sample duration of NB-IoT device has significant impact on its desired signal and on the interference to the LTE user equipment (UE). Numerical results show that the analytical expressions match the simulated ones perfectly, which verifies the effectiveness of proposed system model and derivations. The work in this paper provides a valid guidance for NB-IoT system deployment and co-existence analysis.
ZigZag Decodable Frameless ALOHA

We propose to apply ZigZag decoding (ZD) into frameless ALOHA. To fully exploit the benefit of ZD, the transmission probability of users is adaptively updated so as to increase chances for users to transmit their packets. To this end, a feedback beacon signal from the base station is utilized to acknowledge the retrieval of users. We address the transmission probability that maximizes the average throughput. Moreover, computer simulation confirms that our proposed scheme with suitably selected transmission probability outperforms conventional frameless ALOHA schemes in terms of throughput performance.
Translucent Provisioning in Elastic Optical Networks with Sliceable Bandwidth Variable Transponders

Elastic Optical Networks (EONs) permit a trade-off between spectrum and regeneration use. Provisioning strategies for translucent EONs can reduce spectrum requirements at the cost of additional Bandwidth Variable Transponders (BVTs) used for regeneration. In this context, Sliceable Bandwidth Variable Transponders (SBVTs) can enable transponder sharing among multiple demands, potentially reducing regeneration costs. In this paper, we analyze the impact of SBVTs on the translucent provisioning process of EONs. In particular, we demonstrate that the use of SBVTs can improve blocking probability at almost no extra cost. We discuss simulation results for dynamic traffic over an EON when using different provisioning strategies that use SBVTs with respect to BVTs. Results reflect that the translucent strategy can significantly reduce the blocking probability even without the need of extra transponder deployment.
Energy Efficient Secure MISO Non-Orthogonal Multiple Access Transmission

In this paper, we consider a multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system in the presence of an external single-antenna eavesdropper. We study secure downlink transmission strategies. The challenge is that the multiple antenna configurations make the successive interference cancellation (SIC) condition, which is necessary for applying NOMA schemes, much complex. We formulate the transmission power minimization problem under the quality of service (QoS), SIC and secrecy constraints. In particular, the transmission power minimization problems are formulated under both perfect channel state information (CSI) and the bounded CSI error models. These problems are non-convex, hence they are challenging to solve. Using semidefinite relaxation (SDR) and successive convex approximation (SCA), we propose an iterative algorithm for solving these problems. Our numerical results show that the proposed scheme succeeds in establishing secure communications and NOMA is capable of outperforming the conventional orthogonal multiple access schemes in terms of its power efficiency.
Inter-Numerology Interference Analysis for 5G and Beyond

One of the defining characteristics of 5G is the flexibility it offers for supporting different services and communication scenarios. For this purpose, usage of multiple numerologies has been proposed by the 3rd Generation Partnership Project (3GPP). The flexibility provided by multi-numerology system comes at the cost of additional interference, known as inter-numerology interference (INI). This paper comprehensively explains the primary cause of INI, and then identifies and describes the factors affecting the amount of INI experienced by each numerology in the system. These factors include subcarrier spacing, number of used subcarriers, power offset, windowing operations and guard bands.
Efficient Sum-of-Sinusoids-Based Spatial Consistency for the 3GPP New-Radio Channel Model

Spatial consistency was proposed in the 3GPP TR 38.901 channel model to ensure that closely spaced mobile terminals have similar channels. Future extensions of this model might incorporate mobility at both ends of the link. This requires that all stochastic processes in the model must be correlated in 3 (single-mobility) and up to 6 spatial dimensions (dual-mobility). Existing filtering methods cannot be used due to the large requirements of memory and computing time. The sum-of-sinusoids model promises to be an efficient solution. To use it in the 3GPP channel model, we extended the existing model to a higher number of spatial dimensions and propose a new method to calculate the sinusoid coefficients in order to control the shape of the autocorrelation function. The proposed method shows good results for 2, 3, and 6 dimensions and achieves a four times better approximation accuracy compared to the existing model. This provides a very efficient implementation of the 3GPP proposal and enables the simulation of many propagation scenarios that were thought to be impossible to realize with geometry-based stochastic channel models.
Intelligent Large-Scale AP Control with Remarkable Energy Saving in Campus WiFi System

Full WiFi coverage is more and more prevalent in many places such as university, enterprise, big mall, etc. To achieve full WiFi coverage in a wide area is very costly. Not only extensive AP deployments are expensive, to operate and maintain such large-scale APs every day can also cost much, e.g., the huge power consumption. In this paper, we collect large-scale AP status data in our campus WiFi system, which contains over 8,000 APs and serves about 40,000 active end-users in the area of 3.0925 km2 • After conducting empirical studies on AP loads, we find Idle Phenomenon prevails throughout the trace. A large portion of APs are running without any user association, which will inevitably lead to unnecessary energy consumption. Inspired by this, we propose an intelligent large-scale AP control scheme, named as ACE (i.e., AP Control with Energy saving), to dynamically control large-scale APs (On or Off for energy saving meanwhile without loss of WiFi coverage. In ACE, the load of each AP is predicted first by the random forest algorithm, and those APs whose idle durations last for more than the length of the pre-defined sliding window will be turned off. We conduct extensive trace-driven simulations to demonstrate the efficiency of the ACE scheme; specifically, more than 70% of power energy can be saved with over 92 % of user WiFi coverage guaranteed in average.
STEAM-ME: A Novel Model for Successful Kaizen Implementation and Sustainable Performance of SMEs in Vietnam

The current trend of international integration urges every business organizations to continuously improve their competitive advantage for their survival and sustainable growth. And Kaizen has been a preferable approach in practice. Due to the special role of SMEs in the Vietnam economy, improving their competitiveness is critical. Thus, this study is aimed at identifying determinants of the successful Kaizen implementation and sustainable performance so that SMEs can have proper actions and prioritize their operations within their available resources. Through a formal survey of 213 participants from 62 SMEs which have been successful in implementing Kaizen and appropriate statistical analyses, seven important determinants have been identified, namely, (1) supports from senior management; (2) training; (3) environment; (4) assessment; (5) motivation; (6) mindset; and (7) engagement of all members in the organization. Among them, “mindset” is newly proposed in this study through a qualitative research and found as crucial component in the model. The finding obviously fulfills the existing literature. Moreover, the first letters of the identified factors are orderly congregated as “STEAM-ME” which is a novel model for the successful Kaizen implementation and the sustainable performance of SMEs in Vietnam. “STEAM-ME” implies that organizations need to have a new airflow as “steam” to make all of its members refreshed and brimful of energy to gain significant success in implementing Kaizen, and improve their business performance as well as competitive advantage for their sustainable development. Notably, the novel model can efficiently demonstrate organic relationships among its components which all have positive and significant impacts on the successful Kaizen implementation and sustainable performance of SMEs in Vietnam.
The Evolution of Blockchain: A Bibliometric Study

Blockchain as emerging technology is revolutionizing several industries, and its abundant privileges have opened up a bunch of research directions in various industries; thereby, it has acquired many interests from the research community. The rapid evolution of blockchain research papers in recent years has resulted in a need to conduct research studies that investigate a detailed analysis of the current body of knowledge in this field. To address this need, a few review papers have been published to report the latest accomplishments and challenges of blockchain technology from different perspectives. Nonetheless, there has not been any bibliometric analysis of the state of the art in blockchain where Web of Science (WoS) has been taken into consideration as a literature database. Hence, a thorough analysis of the current body of knowledge in blockchain research through a bibliometric study would be needed. In this paper, we performed a bibliometric analysis of all Blockchain’s conference papers, articles, and review papers that have been indexed byWoS from 2013 to 2018. We have analyzed those collected papers against five research questions. The results revealed some valuable insights, including yearly publications and citations trends, hottest research areas, top-ten influential papers, favorite publication venues, and most supportive funding bodies. The findings of this paper offer several implications that can be used as a guideline by both fresh and experienced researchers to establish a baseline before initiating a blockchain research project in the future.
Mainstream vs. Emerging HPC: Metrics, Trade-Offs and Lessons Learned

Various servers with different characteristics and architectures are hitting the market, and their evaluation and comparison in terms of HPC features is complex and multidimensional. In this paper, we share our experience of evaluating a diverse set of HPC systems, consisting of three mainstream and five emerging architectures. We evaluate the performance and power efficiency using prominent HPC benchmarks, High-Performance Linpack (HPL) and High Performance Conjugate Gradients (HPCG), and expand our analysis using publicly available specialized kernel benchmarks, targeting specific system components. In addition to a large body of quantitative results, we emphasize six usually overlooked aspects of the HPC platforms evaluation, and share our conclusions and lessons learned. Overall, we believe that this paper will improve the evaluation and comparison of HPC platforms, making a first step towards a more reliable and uniform methodology.
PRDL: Relative Localization Method of RFID Tags via Phase and RSSI Based on Deep Learning

Ultra-high frequency radio frequency identification (UHF RFID) technology has been widely used in many areas, and RFID localization becomes a research hotspot. There are many kinds of research on absolute localization; however, due to some disadvantages of absolute localization, relative localization is more effective in some situations. At present, there are some problems with relative localization: existing methods have low localization accuracy, and it is difficult for them to deal with high-density tags. Aiming at these problems, this paper proposes PRDL: relative localization method of RFID tags via phase and RSSI based on deep learning. By using deep learning, the variation characteristics of RFID phase and RSSI are extracted with limited data accuracy conditions. On this basis, we can infer the relative positional relationship of RFID tags with high accuracy, and design the corresponding sorting algorithm to obtain the sequence arrangement. PRDL has experimented with bare tags and actual books, and the experimental results show that PRDL can achieve better results than the traditional relative localization methods. A series of tests also showed that PRDL has good robustness and generalization ability.
Hierarchical Attention and Knowledge Matching Networks With Information Enhancement for End-to-End Task-Oriented Dialog Systems

Nowadays, most end-to-end task-oriented dialog systems are based on sequence-to-sequence (Seq2seq), which is an encoder-decoder framework. These systems sometimes produce grammatically correct, but logically incorrect responses. This phenomenon is usually due to information mismatching. To solve this problem, we introduce hierarchical attention and knowledge matching networks with information enhancement (HAMI) for task-oriented dialog systems. It contains a hierarchical attention dialog encoder (HADE) that models dialogs at the word and sentence level separately. HADE can focus on important words in a dialog history and generate context-aware representations. HAMI also contains a unique knowledge matching module to detect entities and interact with a knowledge base (KB). Then, dialog history and KB result representations serve as guidance for system response generation. Finally, HAMI’s loss function is designed with an information regularization term to emphasize the importance of entities. The experimental results show that HAMI improves 9.8% in entity F1 compared with vanilla Seq2seq. HAMI also outperforms state-of-the-art models in both the entity F1 and dialog accuracy metrics.
Scheduling independent stochastic tasks under deadline and budget constraints

This article discusses scheduling strategies for the problem of maximizing the expected number of tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The execution times of tasks follow independent and identically distributed probability laws. The main questions are how many processors to enroll and whether and when to interrupt tasks that have been executing for some time. We provide complexity results and an asymptotically optimal strategy for the problem instance with discrete probability distributions and without deadline. We extend the latter strategy for the general case with continuous distributions and a deadline and we design an efficient heuristic which is shown to outperform standard approaches when running simulations for a variety of useful distribution laws.
VIRTUE: a virtual reality museum Experience

The digitization of museum exhibits has raised the question of how to make these data accessible, particularly in light of the ever growing collections being available. In this demo, we present the VIRTUE system which allows curators to easily set up virtual museum exhibitions of static and dynamic 2D (paintings, photographs, videos, etc.) and 3D artifacts. Visitors may navigate through the virtual rooms, inspect the artifacts and interact with them in novel ways. Participants will be able to use the system by creating their own exhibitions, which they tour as a visitor.