Miracle-dz/UniFAR-FTUs huggingface dataset:
The Dynamic Influence of ICT on Modern Logistic Industry in Developing Economy and CO2 Emission Using ARDL Approach: A Case of Pakistan

Artificial intelligence applications have opened new opportunities for the development of the modern logistics industry. Accordingly, Information and Communication Technology (ICT) concerning economic growth and carbon emission need comprehensive insight into the logistic industry— an area mainly analyzed from a general perspective by existing literature. Against this backdrop, this paper analyzes the short-run and long-run effects of dynamic variables and their interaction on investment in the logistics industry in connection with economic prosperity, Information and communication technology (ICT), trade, and carbon emission. Accordingly, the autoregressive distributive lag model (ARDL) is applied to observe a close regional perspective of a developing economy like Pakistan using time-series data from 1990 to 2019. The study findings observe a unidirectional long-run causality running from economic prosperity, logistics industry, Information and communication technology (ICT), and trade development to carbon emission. Moreover, regional economic growth exerts a significant demand-pull effect on corresponding regional economic development. These findings imply that high-income geographical regions will continue to face the higher long-run futuristic risks of the contemporary investment in logistic industry development when adhering to ever-evolving carbon emission standards under regional preferences. Accordingly, these regions should be given urgent attention concerning high-technology and artificial intelligence applications in developing economies. Given the availability of technological and general resource efficiencies in developing economies, such applied business-oriented scope of this statistical analysis might facilitate geo-specific strategies concerning future outcomes of current investment in the traditional logistic industry in those countries. We believe the overall analysis can be applied to other developing economies to facilitate sustainable development goals.
An Improved Triangular Facets based Angular Z-Buffer Algorithm for IM Ray Tracing Channel Modeling

Ray tracing is a promising technology for the deployment of the sixth generation (6G) wireless communications. Among the effective methods to realize ray tracing, the shooting and bouncing rays (SBR) method is fast but not accurate enough, while the image method (IM) is accurate and fits point-to-point communications. Angular Z-buffer (AZB) algorithm, as a method to speed up IM, has been widely used. However, because of the simplified processing in the traditional AZB algorithm, there exist calculation errors that may lead to incomplete results. To overcome this problem, an improved AZB algorithm is proposed and realized. The original IM and IM with the traditional AZB algorithm are also realized on the same platform to compare the results. Results show that the traditional AZB algorithm tends to miss some paths in different conditions. As expected, the proposed AZB algorithm outperforms the traditional one by completing the missing paths while retaining the computation efficiency.
R-Secure: A system based on crowdsourcing platforms to improve road safety in the smart city

The number of accidents is increasing over time, which has a social and economic impact. According to the World Health Organization (WHO) road accidents are now the main cause of death among 5-29 year old’s. In addition, Every day, roads cause roughly 40 serious and minor injuries and 4 fatalities in Tunisia, warns the National Observatory of Road Safety(NORS). The statistics show the seriousness of the current situation of road safety at the global and national level. In order to enhance road safety in Tunisia we propose a new system baptized R-Secure based on data from our crowdsourcing platforms. Our system has two functionalities, the first one consists in collecting rich road anomaly data such as images and GPS location from citizens and road safety experts. And the second is to analyze the information gathered and to inform resident’s about the risk areas through a vulnerability map of the city.
AtmoVis: Web Based Visualization of Air Quality Data with Interconnected Windows

Air quality has an adverse impact on the health of people living in areas with poor quality air. Hence monitoring is needed to understand the extent of poor air quality. Little work has been done on the effectiveness of visualization techniques for air quality data analysis. Few tools are developed speciﬁcally for air quality analysis and many practitioners use general purpose tools, such as spreadsheets or programming. This paper investigates which visualization techniques are most effective in analysing air pollution data. A user study was performed with 20 experienced or expert participants. The participants used a domain speciﬁc prototype visualization tool we developed, AtmoVis, to compare spatio-temporal trends among air quality variables using pre-existing visualization techniques. AtmoVis allows experts to explore data without the difﬁculties of programming, or working with spreadsheets. AtmoVis has a windowed layout that connects 6 different visualizations: heat calendar, line plot, monthly rose, site view, monthly averages, and data comparison. The results of the study demonstrated that the heat calendar, line plot, site view, monthly averages, and monthly rose visualizations were effective for analyzing the air quality through AtmoVis. The line plot and the heat calendar were particularly effective for temporal data analysis. AtmoVis was also effective for accessing air quality visualizations and inferring relationships among air quality variables at different monitoring sites. This research can help inform the design of future domain speciﬁc interactive tools for air quality analysis. AtmoVis could be extended to include other datasets in the future.
Cooperative Location and Orientation Estimation of Asynchronous Networks Using a Single Anchor

Location and orientation information is essential for many applications in the Internet of Things (IoT). In this paper, we propose a single-anchor scheme for location and orientation estimation of asynchronous cooperative IoT, where only the agents need to in turn broadcast their measurement signals and no round trip communications are required between agents and the anchor. The Fisher information matrix (FIM) is derived for the location, orientation and clock offset parameters based on the received signals. The FIM can be divided into those obtained from time delays and angles, then the Cramér-Rao lower bound is obtained for the parameters of interest. Numerical results illustrate that cooperation between agents can achieve significant performance gain and the proposed single-anchor scheme can be a potential solution for joint location and orientation estimation of asynchronous IoT.
Object Detection as Gram Positive Cocci in Gram Stained Smear Images

In this paper, we detect Gram positive cossi in Gram stained smear images as object detection. As detectors, we adopt Faster R-CNN, RetinaNet and YOLOv5. Then, we give experimental results for detecting Gram positive cocci, and the detection by YOLOv5 is more successful than that by Faster R-CNN and RetinaNet.
A new Bayesian approach to quantile autoregressive time series model estimation and forecasting

This paper proposes a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. We establish that the joint posterior distribution of the model parameters and future values is well defined. The associated Markov chain Monte Carlo algorithm for parameter estimation and forecasting converges to the posterior distribution quickly. We also present a combining forecasts technique to produce more accurate out‐of‐sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to check the quality of the estimated conditional quantiles is developed. We verify our methodology using simulation studies and then apply it to currency exchange rate data. The results obtained show that an unequally weighted combining method performs better than other forecasting methodology.
Bayesian Online Learning for Sensory Data Analysis in Internet of Vehicles

By building a road information sharing system at the edge, namely EdgeSharing, the traffic safety problems caused by the perception blind spots of self-driving vehicles can be effectively alleviated. EdgeSharing is inherently a crowdsourcing system where sensory data are firstly uploaded from vehicles and then analyzed at the edge to maintain information updating. As such, uploading latency and analysis accuracy are two imperative components to measure the performance of EdgeSharing system, which however challenges the traditional Internet of Vehicles (IoV) that have no consideration for the diverse quality requirements of the content of sensory data. In this paper, we take video captured by on-board camera as an example and devise an adaptive configuration strategy for effectively data uploading and analysis at the edge. Naturally, configuration parameters affect system performance in an unknown and time-varying fashion because of the varying video content and network conditions. To address that, we adopt a Bayesian online learning method that learns the optimal configuration timely by observing historical system performance and real-time environmental information. Our strategy is able to maximize the data analysis accuracy at the edge under the constraint of desired frame rate, which makes EdgeSharing system accommodate complex vehicular environments. Simulation results indicate that compared to other schemes, our proposal finds the optimal configuration fastest and results in about 50% regret reduction.
Nonparametric Probabilistic Forecasting for Wind Power Generation Using Quadratic Spline Quantile Function and Autoregressive Recurrent Neural Network

Compared with commonly-used point forecasting, probabilistic forecasting provides quantitative information on the uncertainty associated with wind power output. However, most studies focus on modelling only a fixed set of quantiles or intervals, which cannot provide the entire information of wind power distribution. In this paper, we propose a non-parametric and flexible method for probabilistic wind power forecasting. First, the distribution of wind power output is specified by spline quantile function, which avoids assuming a parametric form and also provides flexible shape for wind power density. Then, autoregressive recurrent neural network is used to build the non-linear mapping from input features to the parameters of quadratic spline quantile function. A novel loss function based on continuous ranked probability score (CRPS) is designed to train the forecasting model. We also derive the closed-form solution of the integral required in computing the CRPS-based loss function in order to improve the computational efficiency in training. The effectiveness of our proposed method has been validated with public real-world data from Global Energy Forecasting Competition 2014. Compared with some advanced benchmarks, our proposed approach outperforms them at least six percent in terms of CRPS, indicating that it can provide probabilistic wind power forecasting with high quality.
Circularly Polarized Excitation for the Silver/ Polypropylene Hollow Waveguide

A millimeter-wave (mm-wave) circularly polarized excitation for the silver/polypropylene (Ag/PP) hollow waveguide is proposed in this manuscript. The excitation consists of a WR-15 waveguide to substrate integrated waveguide (SIW) transition, a section of SIW, and an end-fire circularly polarized antenna structure. Circular polarization propagation is realized, avoiding high order modes and polarization mismatch problems. The circularly polarized excitation performs well in the 52.2-63.4 GHz band, indicating a fractional bandwidth of 19.4%. This design offers a solution for the waveguide integrated with planar circuits in the mm-wave band.
A Pilot Directional Protection Scheme for LCC-HVDC Lines Based on Grounding Resistance

In LCC-HVDC system, existing DC line protection methods have poor immunity to the fault resistance in internal fault cases, and easily mal-operate in external fault cases. In view of this problem, a new pilot directional protection scheme for HVDC lines based on grounding resistance is proposed. First, the impedance characteristic equations of the converter considering different commutation failure scenes are established. Then, by combining the impedance characteristics of different components, the fault transient network of LCC-HVDC system is built. On this basis, the frequency band where the fault component network is inductive is derived, and the equation of grounding resistance is constructed using the voltages and currents on two line ends in the inductive network in different frequency bands. And then, a protection criterion is formed according to the consistency between the grounding resistance and the actual fault resistance. Finally, simulation tests on the RT-LAB platform verify that, the proposed method can correctly identify different types of internal and external faults, unaffected by the variations in the operating status of the converter and the operation mode of AC system. It is simple, reliable and strongly immune to the fault resistance. Besides, it does not require the interaction of synchronous information, thus it is easy to implement.
Design and Implementation of System for URL Signature Construction and Impact Assessment

The attacker’s server plays an important role in sending attack orders and receiving stolen information, particularly in the more recent cyberattacks. Under these circumstances, it is important to use network-based signatures to block malicious communications in order to reduce the damage. However, in addition to blocking malicious communications, signatures are also required not to block benign communications during normal business operations. Therefore, the generation of signatures requires a high level of understanding of the business, and highly depends on individual skills. In addition, in actual operation, it is necessary to test whether the generated signatures do not interfere with benign communications, which results in high operational costs. In this paper, we propose SIGMA, a system that automatically generates signatures to block malicious communication without interfering with benign communication and then automatically evaluates the impact of the signatures. SIGMA automatically extracts the common parts of malware communication destinations by clustering them and generates multiple candidate signatures. After that, SIGMA automatically calculates the impact on normal communication based on business logs, etc., and presents the final signature to the analyst, which has the highest blockability of malicious communication and non-blockability of normal communication. Our objectives with this system are to reduce the human factor in generating the signatures, reduce the cost of the impact evaluation, and support the decision of whether to apply the signatures. In the preliminary evaluation, we showed that SIGMA can automatically generate a set of signatures that detect 100% of suspicious URLs with an over-detection rate of just 0.87%, using the results of 14,238 malware analyses and actual business logs. This result suggests that the cost for generation of signatures and the evaluation of their impact on business operations can be suppressed, which used to be a time-consuming and human-intensive process.
An Efficient Dual-band Rectifier for Ambient RF Energy Harvesting

Since the ambient RF energy is widely distributed with different frequencies in the free space, the rectifier is demanded to maintain high efficiency within several bands to harvest as much energy as possible. However, it will be quite difficult for the most conventional rectifiers to convert RF to DC effectively at various frequencies when only low input power is supplied. To solve this problem, an efficient dual-band rectifier is implemented. The proposed topology owns two operating frequencies by introducing an impedance compensation network to counterbalance the diode capacitive impedance. For validation, a rectifier using Schottky diode SMS7630 has been fabricated and measured. When the input power level is -20 dBm, the implemented rectifier can provide an efficiency of 27% at 2.35 GHz as well as 16.2% at 5 GHz. These results not only demonstrate the accurateness in the theoretical analysis of the working mechanism but also indicate the validity of the proposed topology.
Robust DOA Estimation Based on Deep Neural Networks in Presence of Array Phase Errors

Deep learning (DL) framework is gradually applied to solve the problem of DOA estimation in array signal processing. DL-based DOA estimation methods are much more efficient than conventional model-based methods in the testing stage. However, the generalization of DL-based methods is limited in the presence of array phase errors, because array phase errors may change in different environments, leading to the difference between the phase errors in the training and the ones in testing. In this paper, we explore the magnitude property of array received signal to develop robust deep neural network (DNN)-based framework for DOA estimation, named as magnitude-based DNN method (shorten as MDNN). The proposed MDNN method performs independently of array phase errors and enjoys a simpler network than the original DNN method. Simulation results in different scenarios demonstrate that the MDNN method behaves much more robust to array phase errors than the original DNN-based method.
Optical convolutional neural network with atomic nonlinearity

Due to their high degree of parallelism, fast processing speeds and low power consumption, analog optical functional elements offer interesting routes for realizing neuromorphic computer hardware. For instance, convolutional neural networks lend themselves to analog optical implementations by exploiting the Fourier-transform characteristics of suitable designed optical setups. However, the efficient implementation of optical nonlinearities for such neural networks still represents challenges. In this work, we report on the realization and characterization of a three-layer optical convolutional neural network where the linear part is based on a 4f-imaging system and the optical nonlinearity is realized via the absorption profile of a cesium atomic vapor cell. This system classifies the handwritten digital dataset MNIST with 83.96% accuracy, which agrees well with corresponding simulations. Our results thus demonstrate the viability of utilizing atomic nonlinearities in neural network architectures with low power consumption.
The Effects of the Combination of Online Visual and Audio Cues on Salesperson Credibility

There has been great concern about online business communication since the Covid-19 pandemic occurred. Salespeople using online tools have been facing difficulties conveying credibility to their customers, which is considered essential for business. Some studies have claimed that online tools allow one to change one's nonverbal cues in real-time; that is, many combinations of visual and audio cues can be applied that are not possible in face-to-face situations. This study was undertaken to clarify the effect of the combination of visual and audio cues on salesperson credibility in online tools. In this study, each male and female salesperson made a business presentation using Zoom with eight patterns of visual and audio combinations: three kinds of still images as visual (Name only /Profile photo with no expression/Profile photo with a smile) x two kinds of voices as audio (Dull/Bright) and two kinds of videos (with no facial expression and dull voice/ with facial expression and bright voice). Eight patterns of presentations by the male salesperson were watched by 55 participants, and the ones by the female salesperson were watched by 52 participants. Then, a questionnaire about salesperson credibility was conducted. As a result, Analysis of Variance (ANOVA) with the Tukey-Kramer test showed that salesperson credibility was affected by the combinations of visual and audio cues for both the male and female. Salesperson credibility consists of three factors: Trustworthiness, Competence, and Attraction. As for the male salesperson, audio cues affected credibility more than visual cues in most cases. Especially on the second factor, Competence, it was found that bright audio cues increased the salesperson’s credibility, whichever visual cues were used. As for the female salesperson, not only audio cues but also visual cues affected her credibility. In particular, "Name x dull voice" significantly reduced her credibility.
Resource Allocation for End-to-End Delay Minimization in UAV-Enabled Mesh Networks

Integrating unmanned aerial vehicles (UAVs) and mesh networks into traditional terrestrial communications is a promising technique to achieve the Internet of everything for next generation communication systems. In this paper, we investigate a time-frequency resource allocation problem in UAV-enabled mesh networks. We first mathematically model the network constraints and objectives for minimizing the total end-to-end (E2E) delay. Then, we transform the original non-convex problem into a series of channel assignment problems in successive time slots. Next, the channel assignment problem in each time slot is sub-optimally solved by the successive convex approximation (SCA) technique. Simulation results confirm that the proposed time-frequency allocation algorithm significantly outperforms three benchmark schemes in terms of total E2E delay.
Comparison of the Results of Suture and Resorbable Plate-Screw Fixation in Craniosynostosis

Background Although many fixation methods are used alone or in combination for craniosynostosis, only few studies have compared the effectiveness and long-term results of these methods. Method In this study, patients in whom suture or resorbable plate-screw system was used for fixation were evaluated in terms of postoperative results and complications. The data of patients who underwent surgery for craniosynostosis between 2002 and 2019 were retrospectively reviewed and evaluated. Results A total of 70 patients, 41 in the suture group and 29 in the resorbable plate-screw group, were included in the study. Whitaker classification was used for head shape evaluation, and anthropometric head circumference measurements were performed in all patients in the preoperative and postoperative periods. In the postoperative period, all patients were compared in terms of the operation time, transfusion requirement, discharge, follow-up period, and complications according to the chosen fixation method. The mean age, postoperative complication rates, anthropometric head circumference measurements, and Whitaker scores of both groups were found to be statistically similar. Although the resorbable plate-screw group had a longer follow-up period, the operation time was longer in the suture group. Furthermore, the suture group had lower transfusion requirement and earlier discharge from the hospital than the resorbable plate-screw group. Conclusions If fixation with suture is chosen in suitable patients, in addition to the low patient cost, this method can be safely applied in centers with limited logistical possibilities, because of the results and complication rates being similar to fixation with resorbable plate-screws.