Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical Layer Performance Analysis of MIMO-WiMAX on Simulink

IEEE 802.16, popularly known as WiMAX, is the emerging new technology in the upcoming generation of wireless communication. The restrictions present in the existing wireless communication technologies like low data rate, short coverage area, and lack of security can be taken care of with the help of WiMAX technology. Wireless is not as secured like others networking technologies because of the evolution of other technologies. In order to increase the reliability and to prevent WiMAX network from different attacks, security is essential. The key target of the work is to analyze the performance of WiMAX physical layer and combat security issues using different modulation schemes and encoding algorithms. The WiMAX physical layer in different digital modulation schemes are made to pass through AWGN and several multipath fading channels and the bit error rate performance is analyzed at different cyclic prefix in the presence of single as well as multiple transceivers. Simulation of the models is carried out with the help of MATLAB-SIMULINK and the results are analyzed accordingly.
Comparative Analysis of IoT Communication Protocols

With the proliferation of machine-to-machine communication, there are many communication protocols standardized for IoT applications. Performances of these protocols may significantly deviate from each other even under the same operating conditions. In this paper, we quantitatively compare the performances of a set of well-known IoT communication protocols, namely CoAP (Constrained Application Protocol), MQTT (Message Queuing Telemetry Transport) and XMPP (Extendible Message Persistent Protocol) in a real-world testbed. CoAP employs UDP packets for transmission while others use TCP. For this purpose, we design as small testbed that collects real-time environmental data. By designing such a system, we aim to reveal the differences among protocols in terms of packet creation time and packet transmission time. The obtained results show that XMPP is worse than other protocols in both metrics and MQTT and CoAP perform almost equally.
Conversational Time Travel: Evidence of a Retrospective Bias in Real Life Conversations

We examined mental time travel reflected onto individuals’ utterances in real-life conversations using a naturalistic observation method: Electronically Activated Recorder (EAR, a portable audio recorder that periodically and unobtrusively records snippets of ambient sounds and speech). We introduced the term conversational time travel and examined, for the first time, how much individuals talked about their personal past versus personal future in real life. Study 1 included 9,010 sound files collected from 51 American adults who carried the EAR over 1 weekend and were recorded every 9 min for 50 s. Study 2 included 23,103 sound files from 33 young and 48 healthy older adults from Switzerland who carried the EAR for 4 days (2 weekdays and 1 weekend, counterbalanced). 30-s recordings occurred randomly throughout the day. We developed a new coding scheme for conversational time travel: We listened to all sound files and coded each file for whether the participant was talking or not. Those sound files that included participant speech were also coded in terms of their temporal focus (e.g., past, future, present, time-independent) and autobiographical nature (i.e., about the self, about others). We, first, validated our coding scheme using the text analysis tool, Linguistic Inquiry and Word Count. Next, we compared the percentages of past- and future-oriented utterances about the self (to tap onto conversational time travel). Results were consistent across all samples and showed that participants talked about their personal past two to three times as much as their personal future (i.e., retrospective bias). This is in contrast to research showing a prospective bias in thinking behavior, based on self-report and experience-sampling methods. Findings are discussed in relation to the social functions of recalling the personal past (e.g., sharing memories to bond with others, to update each other, to teach, to give advice) and to the directive functions of future-oriented thought (e.g., planning, decision making, goal setting that are more likely to happen privately in the mind). In sum, the retrospective bias in conversational time travel seems to be a functional and universal phenomenon across persons and across real-life situations.
How right hemisphere damage after stroke can impair speech comprehension

See Sheppard and Hillis (doi:10.1093/brain/awy291) for a scientific commentary on this article. When right hemisphere strokes cause language impairments, it is generally assumed that the patients originally had atypical language lateralisation. Gajardo-Vidal et al. show how impaired speech comprehension can also arise when damage impinges on right hemisphere regions that are required to support speech comprehension even in the absence of atypical lateralisation.
Improving Teacher-Student Communication During Lectures Using a Robot and an Online Messaging/Voting System

In recent decades and years classroom teaching has been influenced by the appearance of digital devices and the pervasiveness of the internet. In recent years robots started appearing in learning environments. Even though classroom presentations improved considerably, interaction between students and teachers has not improved greatly. We propose a new way of using a desktop robot and an online voting/messaging system to make the classroom experience more interactive. Concretely, in this paper we report on a lecture which was assisted by a robot. The students posted questions and opinions on an online messaging platform. The messages were then sent to the robot to be posed to the teacher, either directly or through a voting procedure. Students found this new way of interaction very efficient and useful for improving their communication with the teacher. At the same time the number of interactions should be controlled so it does not interfere with the teaching process.
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000

This paper presents a complete quasi-3-D analytical model combining the solution of Maxwell’s equations and the magnetic equivalent circuit for the determination of no-load magnetic field distributions of slotted axial flux permanent magnet machines (AFPMMs) considering the end effect without using 3-D finite element method (FEM). The effect of end effect on the air gap magnetic field distributions along the radial direction of AFPMMs is considered by introducing the end function. Moreover, based on the proposed analytical model, the method of adjusting the pole-arc ratio along the radial direction is adopted to solve the local saturation problem of the stator teeth at the inner radius when adopting the parallel slot structure. The relationship between the inner pole-arc ratio and the outer pole-arc ratio is obtained when the tooth flux densities are consistent along the radial direction. Finally, taking two AFPMM prototypes as examples, the analytical calculation results, 3-D FEM analyses and experiment results are compared to validate the accuracy of the analytical model. INDEX TERMS Axial flux permanent magnet machines, complete quasi-3D analytical model, slotting effect, end effect
Would You Obey an Aggressive Robot: A Human-Robot Interaction Field Study

Social Robots have the potential to be of tremendous utility in healthcare, search and rescue, surveillance, transport, and military applications. In many of these applications, social robots need to advise and direct humans to follow important instructions. In this paper, we present the results of a Human-Robot Interaction field experiment conducted using a PR2 robot to explore key factors involved in obedience of humans to social robots. This paper focuses on studying how the human degree of obedience to a robot's instructions is related to the perceived aggression and authority of the robot's behavior. We implemented several social cues to exhibit and convey both authority and aggressiveness in the robot's behavior. In addition to this, we also analyzed the impact of other factors such as perceived anthropomorphism, safety, intelligence and responsibility of the robot's behavior on participants' compliance with the robot's instructions. The results suggest that the degree of perceived aggression in the robot's behavior by different participants did not have a significant impact on their decision to follow the robot's instruction. We have provided possible explanations for our findings and identified new research questions that will help to understand the role of robot authority in human-robot interaction, and that can help to guide the design of robots that are required to provide advice and instructions.
Study of Visual Quality Assessment on Pattern Images: Subjective Evaluation and Visual Saliency Effects

This paper presents a comparison study on visual quality scores obtained from single-stimulus and double-stimulus approaches for pattern images, respectively. We also conduct the comparison on general (non-pattern) images, which serve as a control group. The pattern and non-pattern (PNP) images are collected and built by the Perceptual Data Analysis and Processing Laboratory, National Chung Hsing University, and evaluated by a group of people with both single-stimulus and double-stimulus approaches. Then, we examine the difference of mean opinion scores obtained by both the methods with respect to the image types, image contents (scenes), and distortion types. The hypothesis tests are used to determine whether there is a significant difference between both the sets of scores. The test results suggest that the differences exist and are significant for some specific distortion types and image contents. For example, the pattern images with simple colors should be viewed by the double-stimulus method in order to reduce the variance of subjective scores. In the end, we test three well-known full-reference image quality metrics (IQMs) on both the PNP images. In addition, we investigate whether the visual saliency (VS) plays a more important role for pattern images compared with non-pattern images. We discover that the most salient regions are more influential on pattern images than non-pattern images. Also, by introducing the VS information into the IQMs with a newly proposed Otsu’s weighted VS mask, the correlation performance between objective quality scores obtained from IQMs and subjective scores from human’s visual perception can be boosted further.
State of the Software Development Life-Cycle for the Internet-of-Things

Software has a longstanding association with a state of crisis considering its success rate. The explosion of Internet-connected devices, Internet-of-Things, adds to the complexity of software systems. The particular characteristics of these systems, such as being large-scale and its heterogeneity, pose increasingly new challenges. In this paper, we first briefly introduce the IoT paradigm and the current state of art of software development. Then, we delve into the particularities of developing software for IoT systems and systems of systems, given an overview of what are the current methodologies and tools for design, develop and test such systems. The findings are discussed, revealing open issues and research directions, and reveal that the nowadays IoT software development practices are still lagging behind of what are the current best practices.
PruneJuice: Pruning Trillion-edge Graphs to a Precise Pattern-Matching Solution

Pattern matching is a powerful graph analysis tool. Unfortunately, existing solutions have limited scalability, support only a limited set of search patterns, and/or focus on only a subset of the real-world problems associated with pattern matching. This paper presents a new algorithmic pipeline that: (i) enables highly scalable pattern matching on labeled graphs, (ii) supports arbitrary patterns, (iii) enables trade-offs between precision and time-to-solution (while always selecting all vertices and edges that participate in matches, thus offering 100% recall), and (iv) supports a set of popular data analytics scenarios. We implement our approach on top of HavoqGT and demonstrate its advantages through strong and weak scaling experiments on massive-scale real-world (up to 257 billion edges) and synthetic (up to 4.4 trillion edges) graphs, respectively, and at scales (1,024 nodes / 36,864 cores) orders of magnitude larger than used in the past for similar problems.
A Computational Framework for Integrating Task Planning and Norm Aware Reasoning for Social Robots

Autonomous robots are envisioned to increasingly become part of our lives in the house, restaurants, hospitals and offices. Additionally, self-driving cars will be soon appearing in city streets and highways and they will have to interact with cars driven by humans as well as other self-driving cars. In these settings the robots not only need to efficiently perform their tasks but also be able to interact with humans in socially appropriate ways. To accomplish this, robots must be able to reason not only on how to perform their tasks, but also incorporate societal values, social norms and legal rules so they can gain human acceptability and trust. Moreover, interactions with these robots will be long term. Long-term human interaction with robots as well as robot combined reasoning about both tasks and social norms generate multiple modeling and computational challenges. In this paper, we address one of the most important of these challenges, namely what is an appropriate and scalable computational framework that enables simultaneous task and normative reasoning. In particular, we report on our work on a novel computational framework, Modular Normative Markov Decision Processes (MNMDP) that integrates reasoning for domain tasks and normative reasoning for long-term autonomy. The MNMDP framework applies normative reasoning considering only the norms that are activated in appropriate contexts, rather than considering the full set of norms, thus significantly reducing computational complexity. The model modularity is also advantageous for long-term human-robot interaction. We present computational experiments that show significant computational improvements as compared with a base Normative Markov Decision Process (MDP) framework that includes the full set of norms.
Impella CP or VA-ECMO in profound cardiogenic shock: left ventricular unloading and organ perfusion in a large animal model.

AIMS The aim of this study was to evaluate the Impella CP over veno-arterial extracorporeal membrane oxygenation (VA-ECMO) and their impact on left ventricular unloading and end-organ perfusion. METHODS AND RESULTS Cardiogenic shock (CS) was induced by injecting microspheres into the left coronary artery in fourteen adult female swine. Impella CP or VA-ECMO was initiated in the presence of CS and evaluated after 60 minutes. Left ventricular pressure-volume area (PVA, total mechanical work) was obtained from a conductance catheter. Results are presented as mean (95% confidence interval) and the rank-sum test was used to assess differences between devices. Compared to the CS state, PVA was unaffected by Impella CP and increased on VA-ECMO (from 2,548 [2,193; 2,904] mmHg x mL during CS to 5,775 [4,451; 7,099], between device p-value=0.02). Arterial lactate increased during CS and decreased on support with no difference between devices. Renal venous oxygen saturation decreased during CS and increased on support with no difference between devices. Cerebral venous oxygen saturation increased to 33% [25, 40] on Impella CP and to 69% [49, 89] on VA-ECMO, p=0.04. CONCLUSIONS In this porcine model of profound CS, Impella CP unloaded the left ventricle compared to VA-ECMO. Both devices improved end-organ perfusion, with a tendency towards higher venous oxygen saturations on VA-ECMO.
Bacterial Community Composition of Throughfall and Stemflow

Aboveground plant surfaces (the phyllosphere) arguably represent the largest terrestrial habitat at ∼6.4 × 108 km2 (Morris and Kinkel, 2002), which is double the global land surface (Vorholt, 2012). Microbes flourish in the phyllosphere, including bacteria (Redford et al., 2010), fungi (Kembel andMueller, 2014), and archaea (Finkel et al., 2011), although bacteria generally dominate (Andrews and Harris, 2000). The phyllosphere bacterial community plays major roles in shaping plant ecophysiology, including: phytopathogeny (Saleem et al., 2017), leaf litter decomposition (Voríškova and Baldrian, 2013), and trace gas emissions (Bringel and Couée, 2015). While much attention has focused on how phyllosphere bacterial communities affect plant-atmosphere interactions (Remus-Emsermann and Schlechter, 2018), a current knowledge gap exists regarding how bacterial community composition in precipitation is altered by contact with the phyllosphere. Vegetation significantly alters the amount and pattern of mass and energy inputs to the land surface. The phyllosphere is washed with tens to hundreds of liters over a single storm (Levia et al., 2011), sometimes in <0.25 h (Keim and Skaugset, 2004). When rainfall contacts forest canopies, it reaches the surface via two hydrologic fluxes: throughfall (TF) and stemflow (SF). TF is the portion of rain that drips from the canopy or passes through gaps, while SF is that which contacts and subsequently flows down stems, branches, and trunks. Recent work has shown that TF and SF contain up to 2 orders of magnitude higher concentrations of bacterial cells than open rainfall, resulting in >1016 cells ha−1 being delivered to the forest floor annually (Bittar et al., 2018). No work has assessed the taxonomic identities of bacteria transported by TF and SF, leaving open questions with strong ecohydrological implications: (1) Which bacterial taxa are TF and SF transporting to the surface? and, (2) Does the bacterial composition of TF, SF, and rainfall differ? Thus, this report examines how rainfall bacterial community composition is altered by interaction with Quercus virginiana (Mill., southern live oak) hosting the epiphyte, Tillandsia usneoides (L., Spanish moss) for 10 storms between March-September 2016 (Table S1). Sampled storms were evenly spread throughout the season (Figure S1). Bacterial community composition between rainfall, TF, and SF is compared, the potential sources of bacterial taxa are discussed, and additional questions generated by these measurements are raised.
Indirect Space Vector Modulation Based Three Phase Matrix Converter

Three phase rectifier and Voltage Source Inverter (VSI) are replaced by Matrix Converters (MC) in AC-DC-AC conversion in recent advancements. MC is an AC- AC converter having sinusoidal output voltage and current. This drive is superior to a conventional VSI. Usually a 3X3 matrix converter is used because of our load and supply is three phase. The main objective of the paper is to generate an output voltage having a different voltage level as compared to input voltage level with sinusoidal shape. It is done by using indirect space vector modulation technique in matrix converter. By this approach, we can reduce the complexity of Space vector modulated multilevel inverters. In the Indirect Space Vector Modulation (ISVM) technique, simultaneous O/P voltage and I/P current can be obtained by employing the inverter stage Space Vector Modulation (SVM) with rectifier stage SVM.
Silencing the FOLR2 Gene Inhibits Cell Proliferation and Increases Apoptosis in the NCI-H1650 Non-Small Cell Lung Cancer Cell Line via Inhibition of AKT/Mammalian Target of Rapamycin (mTOR)/Ribosomal Protein S6 Kinase 1 (S6K1) Signaling

Background The FOLR2 gene encodes folate receptor-beta (FR-beta), which is expressed by tumor-associated macrophages. The effects of FOLR2 gene expression in non-small cell lung cancer (NSCLC) remains unknown. This study aimed to investigate the effects of FOLR2 gene expression and gene silencing in human NSCLC cell lines and normal human bronchial epithelial (HBE) cells in vitro. Material/Methods Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot were performed to detect the expression of the FOLR2 gene, cell cycle and apoptosis-associated genes in normal HBE cells and the NSCLC cell lines, A549, NCI-H1299, NCI-H1650, and NCI-H460. Using small interfering RNA (siRNA), or silencing RNA, FOLR2 gene silencing was performed for NCI-H1650 cells. Cell counting kit-8 (CCK-8) was used to measure cell viability. Cell cycle and apoptosis were determined using flow cytometry. Western blot evaluated the expression of Akt, mTOR, and S6K1 signaling. Results Expression of the FOLR2 gene was increased in NSCLC cells compared with normal HBE cells. Silencing of the expression of the FOLR2 gene in NCI-H1650 cells reduced cell viability, increased cell apoptosis, and arrested cells in the G1 phase of the cell cycle, decreased the expression of cyclin D1, upregulated expression of cell cycle inhibitors, p21 and p27, upregulated the expression of Bax/Bcl-2, and inhibited phosphorylation of AKT, mTOR, and S6K1. Conclusions Silencing of the FOLR2 gene inhibited phosphorylation of AKT, mTOR, and S6K1, inhibited cell proliferation and increased apoptosis in the NCI-H1650 human NSCLC cell line.
A new stereo matching algorithm based on probabilistic diffusion

In this paper, the general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The probabilistic models are independence and similarity among the neighboring disparities in the configuration. The formula can be implemented into the some different forms corresponding to the probabilistic models in the disparity neighborhood or configuration. And, we propose new probabilistic models in order to simplify the joint probability distribution of disparities in the configuration. According to the experimental results, the proposed algorithm outperformed the other ones, such as sum of squared difference(SSD) based algorithm and Scharstein's method, and the proposed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to 0.01% of the generalized formula.
Statistical Investigation of Image Processing Algorithms Characteristics Automation

Existing image processing algorithms provides different results depending on input data. More often evaluation of algorithm results provides by expert method based on limited set of initial data. Such evaluation obtaining requires experts involving and has tremendous time consumption. Estimation obtained is relevant to that data set the investigation based on, and could have reverse result when have been used the other data set. The solution is in automation of algorithms statistical analysis in different scenes using partial and general quality estimations. This paper describes different image enhancing methods, qualitative and quantitative image parameters are given, generalized image quality and image processing algorithm parameter is presented, methods of algorithms analyzing, comparing and selecting are suggested.
MQTT Based Air Quality Monitoring System using Node MCU and Node-RED

This paper present an implementation of MQTT based air quality monitoring system. The air quality measurement device is a hardware using ESP8266 NodeMCU that connects to sensors for measuring temperature, humidity, concentration of carbon monoxide (CO), ozonegas (O3), and PM2.5. The firmware of device makes the device act as a publisher that reads the sensor data and sends them to MQTT Broker. Node-RED is used to be a subscriber that subscribes to receive data from MQTT Broker. With Node-RED, we can easily make a flowto manage and handle received data. Then, Node-RED will send data to the air quality monitoring dashboard which is a responsive web application to display data in gauge, text and chart user interface. In addition, when the value of some data exceed the configured range, Node-RED will send an alarm message via LINE Notify to notify users.