Miracle-dz/UniFAR-FTUs huggingface dataset:
Asynchronous Subgradient-push Algorithm for Distributed optimization over Directed Graphs

This paper proposes an asynchronous subgradient-push algorithm (AsySPA) to solve an additive cost optimization problem over digraphs where each node only has access to a local convex objective function. The striking feature of our asynchronous algorithm is that every node updates independently, without any coordination with other nodes by using local information. Thus, no global clock or synchronization scheme is needed and nodes can update their iterates under different rates. To address asynchrony among nodes and communication delays, we design a novel mechanism to adaptively adjust stepsizes per update in each node and construct a delay-free augmented system. Moreover, we propose a generalized subgradient algorithm, which provides a unified approach to study the standard subgradient algorithm and the celebrated incremental subgradient algorithm, to prove its exact convergence. Finally, we demonstrate the efficiency and advantages of the AsySPA against the current state-of-the-art by training a multi-class logistic regression classifier on a large real dataset.
Exponential Mean Square Stabilization for Itô Stochastic Systems with Input Delay

This paper is concerned with the Itô stochastic systems with input delay. The main contribution is to establish the equivalent condition for the mean-square stability of the stochastic systems with input delay. The key tool is a parameterized Riccati-ZXL equation. With the solvability of a parameterized Riccati-ZXL equation, we also present the explicit solution of the infinite-horizon optimization problem under a discounted cost function.
Downscaling Census Data for Gridded Population Mapping With Geographically Weighted Area-to-Point Regression Kriging

Understanding human population distribution on the earth at fine scales is an increasingly need to a broad range of geoscience fields, including resource allocation, transport and city planning, infectious disease assessment, disaster risk response, and climate change. Many approaches have been developed to spatially downscale census data to gridded population distribution datasets, which are preferable to integration with natural and socio-economic variables. We present a novel population downscaling approach that geographically weighted area-to-point regression kriging technique is used to downscale census data to gridded population distribution datasets with multisource geospatial and social sensing data. As a case study in Nanjing city, China we evaluated the effectiveness of the proposed population downscaling approach. The experimental results demonstrated that the proposed approach generated more accurate details of population distribution and higher accuracy than existing widely-used gridded population distribution products. Hence, the proposed population downscaling approach is a valuable option in producing gridded population distribution maps.
Spiral Antenna With a Circular HIS Reflector

This paper describes the antenna characteristics of a spiral antenna with a circular high-impedance surface (HIS) reflector (CirHISR). The CirHISR consists of a fan-shaped patch element array printed on the top surface of a circular dielectric substrate, and a perfect electric conducting reflector printed on the bottom surface of the circular dielectric substrate. The aspect ratio of each fan-shaped patch element, which is defined by the ratio of patch width to patch length, is approximately one. The axial ratio characteristics are analyzed using FEKO software based on the Method of Moment. Good axial ratio is obtained across a frequency range of 3 GHz to 10 GHz, where the antenna height is 0.9 cm (0.09 wavelengh at the lowest analysis frequency). In addition, other antenna characteristics, including VSWR, gain, and radiation efficiency are recognized.
Production Model for Irrigation Improvement Projects Implemented in Cusco - Peru Using System Dynamics

The improvements in implementation of irrigation projects in Peruvian Andes needs to give a multidisciplinary approach that involves mainly the participation of anthropologists and engineers. The sustainability of such projects is measured mainly by the economic profit and the organizations strength. Due the complexity of these factors some unexpected effects have appeared for the sustainability of the projects already implemented in the Chumbivilcas province where the overproduction generated an unexpected phenomenon of economic recession. To overcome this problem, a new mathematical model was developed. In the conceptualization phase of the model, approximately 285 variables were identified and system dynamics were used to perform the simulations required for the model developed. The results of the simulation have been compared with historical data showing consistency among themselves in the dimensions of production and price determination among the most importants.
Design and Implementation of an Asynchronous BCI System With Alpha Rhythm and SSVEP

Objective: The aim of this work was to build an asynchronous brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEPs) that outputs continuous, stable and smooth control commands in the up, down, left and right directions. Real-time feedback is presented on the computer screen to enhance collaborative participation in the human-computer interaction. Methods: Four stimulus sources flickering at different frequencies were sequentially fixed around the computer monitor to allow for subjects to complete control tasks by gazing at different stimulus sources. The subjects autonomously switched between the idle and working states by controlling the alpha amplitude. A sliding window voting discrimination (SWVD) strategy was incorporated into the canonical correlation analysis (CCA) algorithm for asynchronous classification. Results: Experiments were performed with 18 subjects using both the synchronous and asynchronous paradigms. The average accuracy was 95.42±3.35% with a data length of 3 s for synchronous operation; in addition, most subjects were able to successfully control a target to move precisely and smoothly with a 1 s sliding window during asynchronous operation. Conclusion: The proposed design scheme is feasible for our online asynchronous BCI system. Significance: By applying the SWVD strategy and optimizing the experimental paradigm, the classical CCA algorithm was successfully applied for continuous control in an asynchronous BCI system. With the developed system, obvious improvements in the information transmission rate (ITR) and sensitivity were achieved, which will be beneficial for the development of practical BCI systems.
The Impact of Software Security Practices on Development Effort: An Initial Survey

Background: Software projects are facing the need to adopt security practices during the software development life cycle (SDLC). Nevertheless, the amount of effort to be invested in order to achieve a certain level of software security is not clear yet. Aims: The goal of this study is to get an overview of the application of software security practices in the industry and to identify the impact of the introduction of such activities in software development projects in terms of effort/cost. Method: We conducted a survey on a software security group of a professional social network by applying a random sampling strategy to establish a representative set of participants. Results: The questionnaire was fully answered by 110 participants, from the 808 profiles that were invited from the sampling frame. The results show that security practices have been applied thoroughly in the projects and revealed high variability in secure software development effort across the participants’ projects. Further research is needed to understand the different professionals’ perspectives regarding security effort in projects. As lessons learned, we found that the professional social network offered a demographically diverse sampling frame, but this comes with hurdles that need to be overcome. Conclusions: The experiences of the participants showed that security is a factor that drives effort in software projects, and security practices need to be taken into account when planning software development initiatives. Our findings about the current state of practices and adoptions can help practitioners and researchers in future endeavors.
A Tool for Bridge Detection in Major Infrastructure Works Using Satellite Images

The identification of bridges in major infrastructure works is crucial to provide information about the status of these constructions and support possible decision-making processes. Typically, this identification is performed by human agents that must detect the bridges into large-scale datasets, analyzing image by image, a time-consuming task. In this paper, we propose a novel tool to perform bridge detection and identification in large-scale remote sensing datasets. This tool implements a deep learning-based algorithm, the Faster R-CNN (Regions with CNN features), a technique that is the current state-of-the-art for many object detection and identification applications. Since deep training usually requires a lot of data, we also created a bridge image dataset, composed of remote sensing images from around the globe. The proposed tool was encapsulated into an ArcGIS plugin in order to facilitate its use by non-programmer users.
Flaw Detection from Ultrasonic Images using YOLO and SSD

Non-destructive ultrasonic testing (UT) of materials is used for monitoring critical parts in power plants, aeronautics, oil and gas industry, and space industry. Due to a vast amount of time needed for a human expert to perform inspection it is practical for a computer to take over that task. Some attempts have been made to produce algorithms for automatic UT scan inspection mainly using older, non-flexible analysis methods. In this paper, two deep learning based methods for flaw detection are presented, YOLO and SSD convolutional neural networks. The methods' performance was tested on a dataset that was acquired by scanning metal blocks containing different types of defects. YOLO achieved average precision (AP) of 89.7% while SSD achieved AP of 84.5 %.
Position-based Fully-Scheduled Precoder Channel Strategy for POMA Structured LTE Network

Increasing number of mobile terminals and their services at a constantly rapid growth at high data rate is becoming the Long Term Evolution (LTE) spectrum allocation a major bottleneck for many cellular network operators. Through physical spectrum sharing the scarcity of spectrum can limited, among them are orthogonal and non-orthogonal spectrum sharing. In orthogonal spectrum sharing, user terminals (UEs) are scheduled with different operators, and in non-orthogonal spectrum sharing, UEs are allowed with different operators to allocate the same spectrum frequency simultaneously. These two spectrum strategies potentially interfere with UEs and operators, causing the performance degradation in realistic scenarios at multi-terminals and operators based on spectrum assignment. To overcome these, this paper propose a strategy through numerical assessments, Fully-Scheduled Precoder Channel (FSPC) Strategy, to tackle the problem of primary uplink, which can be obtained through modelling the transmission model with Position-orthogonal multiple access (POMA) system, where the position of UEs are structured with different operators to use the same spectrum frequency and time simultaneously, which in-turn increase the spectrum sharing capability in LTE network. Comparative results shown an improved remarks with the surveyed methods and the proposed work forms the basis for Structured LTE Network deployments in future.
Corrected Gradient Methods for Distributed optimization

We propose a class of algorithms as Corrected Gradient Methods (CGMs) to solve the distributed optimization problem of minimizing an aggregate cost with consensus requirement on individual decision variables. Specifically, we design the algorithm by combining estimation and correction of the disagreement of individual gradients, and apply it respectively to gradient descent method and heavy ball method. With a doubly stochastic weight matrix over a strongly connected network, we show that in both cases CGM can achieve the convergence to a common optimum for L-smooth convex functions; if the function is also strongly convex, then the convergence is exponentially fast. Simulation is conducted to validate the efficacy of CGMs.
Hertz-to-terahertz dielectric response of nanoconfined water molecules

Broad-band dielectric spectroscopy, heat capacity measurements and molecular dynamic simulations are applied to study excitations of interacting electric dipoles spatially arranged in a network with an inter-dipole distance of 5-10 Å. The dipoles with magnitude of 1.85 Debye are represented by single H2O molecules located in voids (0.5 nm size) formed by ions of the crystal lattice of cordierite. We discover emergence of nontrivial disordered paraelectric phase of dipolar system with signs of phase transition below 3 K.
Uplink Grant-Free Access Solutions for URLLC services in 5G New Radio

Ultra-reliable low latency communication requires innovative resource management solutions that can guarantee high reliability at low latency. Grant-free random access, where channel resources are accessed without undergoing assignment through a handshake process, is proposed in 5G New Radio as an important latency reducing solution. However, this comes at an increased likelihood of collisions resulting from uncoordinated channel access. Novel reliability enhancement techniques are therefore needed. This article provides an overview of grant-free random access in 5G New Radio focusing on the ultra-reliable low latency communication service class, and presents two reliability-enhancing solutions. The first proposes retransmissions over shared resources, whereas the second proposal incorporates grant-free transmission with non-orthogonal multiple access where overlapping transmissions are resolved through the use of advanced receivers. Both proposed solutions result in significant performance gains, in terms of reliability as well as resource efficiency. For example, the proposed non-orthogonal multiple access scheme can support a normalized load of more than 1.5 users/slot at packet loss rates of ~ 10−5 a significant improvement over conventional grant-free schemes like slotted-ALOHA.
Human body communication: Channel characterization issues

Human Body Communication (HBC) is a promising wireless technology that uses the human body tissues as a signal propagation medium. In HBC, the information signal is coupled to the body through an electrostatic or magnetostatic field via electrodes and is captured in another part of the body using similar electrodes. HBC has lower power consumption than conventional radio frequency (RF) approaches, because it operates at lower frequencies, usually between 0.1 MHz and 100 MHz, avoiding the body shadowing effects, complex and power hungry RF circuits and antennas. In addition, the signal is mainly confined to the human body, guaranteeing high data communication security and high efficiency in the network utilization. Designs have already been shown to achieve energy efficiency of pJ/bit and power of micro-Watts, paving the way for autonomous, energy harvested powered devices [1]. With these characteristics, HBC helps to reduce the battery volume and consequently the size and the weight of wearable devices such as watches, earphones, glasses, shoes or clothes. Overall, HBC presents itself as an interesting alternative to implement Body Sensor Networks (BSN) or Body Area Networks (BAN), especially since it is supported by the IEEE standard 802.15. 6 for short-range, low-power and highly reliable wireless communication systems for use in close proximity to or within the human body [2].
An Extended GN Algorithm for Identifying Community Structure in Networks

Community structure is one of the most important property of networks. In the study of complex systems, the task of detecting this hidden community structure efficiently is a key concept. An approach is presented to analyse the information traveling or traffic flow in weighted networks, by employing the ensemble theory in statistical mechanism. Through this method, the grand canonical ensemble (GCE) betweenness is constructed. Then, an extended GN algorithm of detecting community structure with the GCE betweenness is developed. It is applied to uncover the hierarchical clustering on a set of computer-generated and real networks. Furthermore, the common modularity is generalized to the weighted modularity, in order to detect the best division in the weighted networks. Finally, the performance of the proposed algorithm has been tested on several network data sets. The experimental results shows that there is no loss of information in terms of edge strength when using this quantity to measure the fitness of a particular community structure in weighted networks.
An Infotaxis-based Odor Source Searching Strategy for a Mobile Robot Equipped with a TDLAS Gas Sensor

As a source searching method, infotaxis has been used in mobile olfactory robots equipped with contact-type MOS (metal oxide semiconductor) gas sensors. This method navigates a robot to explore more cues in the global space and therefore costs much time at the early stage. To solve this problem, this paper presents a line integral infotaxis (LII) algorithm which is realized by applying a remote-type TDLAS (tunable diode laser absorption spectroscopy) gas sensor to the infotaxis. In addition, the robot often gets stuck in the place of high concentration during the infotaxis searching process, so an odor source declaration procedure which is implemented by calculating the odor particle flux on a closed curve is added to the LII. Simulation results show that the LII algorithm can enormously save time of exploration and solve the local convergence problem.
An Efficient Real time Product Recommendation using Facial Sentiment Analysis

Image recognition based on deep learning has made successful attempts in solving Computer Vision problems. Deep learning algorithms have primarily contributed to face detection, facial expression recognition, age and gender determination tasks. The performances of the algorithms have been evaluated by the researchers and they are made available as cloud services. By accessing the cloud, application developers can incorporate the already tested algorithms in their work and pay for the utilized service. Amazon Rekognition is a web service which provides highly accurate facial analysis and more suitable for facial recognition. Amazon Rekognition includes a simple, easy-to-use API that can quickly analyze any image file that’s stored in Amazon S3. This paper explains the necessary steps to utilize the service to build an application for a retail store. The application captures the customer faces, performs facial analysis and recommends appropriate products by displaying the targeted advertisements. With the facial analysis responses of recognition algorithm, a decision block is devised to select the appropriate content to be displayed. The application is tested with anonymous images and performance is appreciable.
How do practitioners create inclusive environments in day care settings for children under the age of five years with chronic health conditions? An exploratory case study

The concept of inclusion within the English education system is often taken for granted. There are a number of factors that can impact on inclusive practice and this demands careful exploration. Chronic health conditions (CHCs), such as anaphylaxis, asthma, diabetes, epilepsy and eczema, pose particular challenges to inclusion for practitioners who teach, educate and care for children under the age of five years in day care settings. These conditions can have a significant effect on children’s health, especially in the minority world. However, there is a paucity of research about how the symptoms affect children’s early education. This mixed-methods study collected quantitative data by sending a postal survey to 60 settings in order to find out how many children are affected by these conditions in day care settings. Four of the surveyed settings went on to participate in the qualitative aspect of the study. Qualitative data were also collected from parents of children with CHCs and the study included observations of a child in his early childhood setting over the course of a year. The findings revealed that 11% of children attending the settings in this study had been diagnosed with one or more CHCs and that CHCs had a profound effect on children and their parents. Parents reported that knowledge of the specific conditions is important for practitioners to have in order to create inclusive relationships with them. Practitioners in this study demonstrated a collaborative approach to leadership when creating inclusive environments. However, the findings revealed tensions for practitioners regarding the inclusion of all children in the curriculum. The findings suggest that achieving inclusion may therefore be problematic for some children. However, the communication skills, knowledge of CHCs and willingness of practitioners were vital to the inclusion of children with CHCs in their early education.
Study of the interaction between light and nanoantennas in Tip-Enhanced Raman Spectroscopy

The interaction of light with nanometric metallic structures - nanoantennas - is of particular interest for material analysis techniques such as Tip Enhanced Near-Field Optical Microscopy (TENOM) and, specifically, Tip Enhanced Raman Spectroscopy (TERS). In these scenarios, the nanoantenna's response to an excitation provided by a tightly focused laser beam is essential in order to qualify the system's lateral resolution and signal-to-noise ratio. This work proposes an apparatus that enables the study of the nanoantenna's response in the focal region of light provided by a high numerical aperture optical system. The apparatus is validated by measuring the Raman scattering intensity distribution around the focal region under different conditions, enabling the precise characterization of the electromagnetic field distribution around the focal region, providing informations regarding alignment tolerance, optical signal enhancement and optical system's Point Spread Function (PSF).
Characterization at High Frequencies of Planar Transformers for Aeronautical Converters

This work presents a quick and simple method to characterize planar transformers at high frequencies. This method only requires two measurements which can be performed with a spectrum analyzer with tracking generator. By performing a circuit analysis of a high-frequency circuit model of the planar transformer regarded as a four-ports device we obtain analytical expressions of the transmission coefficients of the planar transformer excited in open-circuit, common mode and differential mode setups in a wide frequency range. This allows us to develop an efficient method to determine the high-frequency parameters of the circuit model from the result of these simple measurements. Different commercial planar transformers have been measured and the predicted performance of the model has been compared with measured responses. We have verified that in all the cases the measured transmission coefficient exhibits the behavior predicted by the theoretical analysis.