Miracle-dz/UniFAR-FTUs huggingface dataset:
JamLab-NG: Benchmarking Low-Power Wireless Protocols under Controllable and Repeatable Wi-Fi Interference

Evaluating the performance of low-power wireless protocols in noisy environments in a repeatable and fullyautomated way is still an open problem in our community. On the one hand, there is a lack of tools enabling the controllable and repeatable generation of interference using Wi-Fi devices. On the other hand, existing testbeds do not offer the automated generation of Wi-Fi interference on a large-scale. In this work, we present JamLab-NG, an open-source framework allowing the generation of controllable Wi-Fi interference using off-the-shelf devices such as the Raspberry Pi 3. JamLab-NG enables the fine-grained control of individual link-layer transmissions, avoiding the uncontrollable delays introduced by the network stack, the operating system, and the clear channel assessment procedure. Furthermore, JamLab-NG allows to generate repeatable Wi-Fi interference patterns by controlling radio settings such as the transmission speed and the packet length, which would otherwise be automatically adapted by the radio firmware at run-time. We use JamLab-NG to augment an existing testbed and embed the generation of Wi-Fi interference into its automated execution of experiments. Among others, we allow remote configuration of the interference generated by individual Wi-Fi devices, and show that they can operate in a synchronized fashion without additional infrastructure. Finally, we use the augmented testbed to benchmark the performance of state-of-the-art IoT protocols under Wi-Fi interference in a repeatable and fully-automated way.
Development of wearable gait analysis device based on vertical ground reaction forces

Gait analysis system is widely used in the field of biomechanics and sports sciences. It is a sophisticated system that requires high costs to develop and run. This system also requires trained people to handle the system. Besides, the current wearable gait analysis device faced limitations such as low durability and repeatability, big size and heavy weight, and limited data storage and usage time. Based on these conditions, this project was created to overcome the stated problems by initiating a pilot work on developing a reliable and cost-effective device. This initial gait analysis device only focused on the vertical ground reaction force (vGRF) and analyzed the human gait pattern during normal walking activity. This project was designed with wireless capability that makes handling and monitoring the device easier. This project is a prototype-based gait analysis system with the idea to develop a low-cost system that can be integrated and enhance the existing system. Resistive force sensors were placed at three locations representing the most pressurized on foot based on a previous study to measure the vGRFs. The device was tested by conducting experiments to analyzethe gait pattern of ten healthy subjects during normal walking activity. The results were verified according to the standard gait data obtained from the previous study. The results showed that the developed wearable gait analysis device able to produce similar curve patterns as the standard vGRF with 94.5 % accuracy. With the high repeatability and accuracy, this device can be further expanded by including the lateral and longitudinal ground reaction forces, performing more activities, and conducting an experimental studyfrom different subjects’ backgrounds.
A mini-screen to identify the role of microRNAs in the Drosophila Insulin-Producing cells regulating lifespan

Various genetic, molecular and environmental factors influence the lifespan of an organism, which includes the highly conserved insulin signaling pathway. In Drosophila , Insulin-Producing cells (IPCs) present in the fly brain, analogous to vertebrate pancreas, control growth, metabolism and lifespan. Regulation of gene expression by microRNAs is strongly believed to be crucial in determining adult lifespan, however, no systematic approach has been conducted so far to study the role of microRNAs in the IPCs in the aging process. Most of the current work has been focused on deciphering the roles of microRNAs in the IPCs during developmental stages. Here, we report the results of a mini-screen performed to identify microRNAs that function in the adult IPCs in regulating lifespan and neuronal integrity.
Moving Target Defense-Based Denial-of-Service Mitigation in Cloud Environments: A Survey

With the increased frequency and intensity of denial-of-service (DoS) attacks on critical cloud-hosted services, resource adaptation schemes adopted by the cloud service providers (CSPs) need to be intelligent. Specifically, they need to be adaptable to attack behavior and be dynamic to curb resource over-utilization. The concept of moving target defense (MTD) has recently emerged as an effective and agile defense mechanism against DoS attacks that particularly target cloud-hosted applications. However, the existing surveys that seek to explore this space either focus more on MTD for generic cyberattack mitigation or on DoS attack defense on cloud systems. In this survey, we particularly provide an in-depth analysis on how MTD can help recover critical cloud assets in the face of DoS attacks and how emerging programmable technologies such as software-defined networking (SDN) can be leveraged to achieve that goal. Unlike existing surveys, we categorize DoS attacks on cloud platforms based on their working mechanism. We also discuss the non-MTD-based DoS defense strategies for both cloud and non-cloud infrastructures in order to highlight the pros and cons of MTD-based strategies. We introduce MTD working mechanisms and present how existing research is envisioning MTD’s application in mitigating DoS attacks, both with and without SDN. We also take an in-depth look at the testbed implementations and resilience and performance evaluations of MTD approaches. Finally, we articulate the existing challenges in MTD for DoS mitigation in cloud systems and how these challenges are shaping the future research in this domain.
Improving Sensor Network Convergecast Performance with Directional Antennas

The use of directional antennas for wireless communications brings several benefits like increased communication range and reduced interference. One example of directional antennas are electronically-switched directional (ESD) antennas that can easily be integrated into Wireless Sensor Networks (WSNs) due to their small size and low cost. However, current literature questions the benefits of using ESD antennas in WSN due to the increased likelihood of hidden terminals and increased power consumption. This is mainly because earlier studies have used directionality for transmissions but not for reception. In this paper we introduce novel full stack optimizations in order to fully utilize the benefits of using directional antennas. We modify the MAC, routing and neighbor discovery mechanisms to support directional communication. We focus on convergecast investigating a large number of different network topologies. Our simulation results show that in networks with dense traffic, directional antennas achieve up to 24% higher packet delivery rates, a 55% decrease in energy consumption, and a 46% decrease in the energy per received packet.
Energy consumption and environmental quality nexus: empirical evidence from China

This study examined the nexus between energy consumption and environmental quality in light of China’s 2060 carbon-neutrality agenda utilizing annual frequency data from 1971 to 2018. In order to obtain valid and reliable outcomes, more robust econometric techniques were employed for the analysis. From the results, all the variables were first differenced stationary and cointegrated in the long-run. The elastic effects of the predictors on the explained variable were explored through the ARDL, FMOLS, and the DOLS techniques, and from the discoveries, energy utilization worsened environmental quality in the country via more CO2 emissions. Also, industrialization and urbanization deteriorated the country’s environmental quality; however, technological innovations improved ecological quality in the nation. On the causal connections between the variables, a unidirectional causality from energy consumption to CO2 effluents was discovered. Also, feedback causalities between industrialization and CO2 secretions, and between urbanization and CO2 exudates were disclosed. However, there was no causality between technological innovations and CO2 emanations. Based on the findings, the study recommended among others that, since energy consumption pollutes the environment, the country should transition to the utilization of renewable energies. Also, the government should allocate more resources to the renewable energy sector. This will help increase the portion of clean energy in the country’s total energy mix. Furthermore, research and development that are linked to the utilization of green energies should be supported by the government. Data constraints were the main limitation of this exploration. Therefore, in the future, if more data become available, similar explorations could be conducted to check the robustness of our study’s outcomes.
The need for dried plasma – a national issue

Recent studies have demonstrated that early transfusion of plasma or RBCs improves survival in patients with severe trauma and hemorrhagic shock. Time to initiate transfusion is the critical factor. It is essential that transfusion begin in the prehospital environment when transport times are longer than approximately 15 to 20 minutes. Unfortunately, logistic constraints severely limit the use of blood products in the prehospital setting, especially in military, remote civilian, and mass disaster circumstances, where the need can be most acute. US military requirements for logistically supportable blood products are projected to increase dramatically in future conflicts. Although dried plasma products have been available and safely used in a number of countries for over 20 years, there is no dried plasma product commercially available in the United States. A US Food and Drug Administration–approved dried plasma is urgently needed. Considering the US military, disaster preparedness, and remote civilian trauma perspectives, this is an urgent national health care issue.
L INGUIST : Language Model Instruction Tuning to Generate Utterances for Intent Classiﬁcation and Slot Tagging

We present L INGUIST , a method for generat-001 ing synthetic data for Intent Classiﬁcation and 002 Slot Tagging (IC+ST) based on a 5B multilin-003 gual seq2seq model ﬁne-tuned on a ﬂexible 004 instruction prompt. On a 10-shot setting for 005 learning a new SNIPS intent, we show abso-006 lute improvement of +2.5 points (IC) and +2.8 007 points (ST) over data upsampling, and com-008 bined gains of +4.7 points (IC) and +3.2 points 009 (ST) when combined with Back-Translation. 010 On an internal production dataset for Conver-011 sational Agent IC+ST, we show between 7.9% 012 and 25.2% relative improvement compared to 013 an internal baseline across four languages. To 014 the best of our knowledge, we are the ﬁrst to use 015 instruction ﬁne-tuning of a large scale seq2seq 016 model to generate slot-labeled data. 017
Arbuscular mycorrhizal fungi serve as keystone taxa for revegetation on the Tibetan Plateau

Revegetation is widely used to enhance degraded topsoil recovery with the enhancements of soil nutrient accumulation and soil structure stabilization. Arbuscular mycorrhizal fungi (AMF) are important for the allocation of carbon into the soil and the formation of soil aggregates. Thus, we hypothesized that AMF could construct more niches for other microbes during revegetation, making AMF keystone taxa of soil. Soil fungal and bacterial communities were investigated under a revegetation experiment and correlation networks between soil fungi and bacteria were constructed. Simultaneously, the plant growth level, soil properties and structure, and soil microbial carbon decomposition abilities were measured. The results revealed that AMF were the most central fungi at the phylum (degree = 3), class (degree = 11), and family (degree = 15) levels. The reads number of AMF were positively correlated with both fungal (R2 = 0.431, P < 0.001) and bacterial (R2 = 0.106, P = 0.044) richness. Higher colonization of AMF in roots and/or more AMF extraradical mycelium and spores in soil indicated a better plant growth, more stable soil aggregates, and a higher carbon decomposition ratio. Our results highlight that AMF are keystone taxa in revegetation, as they play significant roles in enhancing the recovery of the belowground microbiome diversity, soil structure stability, and nutrients cycling. The positive roles of AMF in revegetation support the application of AMF in ecosystem recovery.
Efficacy and Tolerability of Weekly Chemotherapy for Locally Advanced or Metastatic Gastric Cancers

Gastric cancer is the fourth most common cancer in the world and the second most common cause of cancerrelated death. At diagnosis, nearly two third of gastric cancer patients are in metastatic stage (Kamangar et al., 2006; Jemal et al., 2011). Systemic chemotherapy is the major treatment option at locally advanced and metastatic gastric cancer. The aim of treatment in advanced stage is palliation and improving survival. The advantage of combination treatments against single agent treatments in advanced stage gastric cancer patients is recently popular (Wagner et al., 2006). Docetaxel, cisplatin and 5-fluorourasil (DCF) which is administered every three weeks is an effective treatment than other treatments, but it carries high toxicity risk especially in patients with poor performance status. It is
Immunologic risks of whole blood: ABO compatibility, D alloimmunization, and transfusion‐related acute lung injury

W hen it comes to deciding whether or not to use whole blood in the initial resuscitation of traumatically injured patients, one must weigh potential risks against potential benefits. Potential benefits of whole blood include increased in vitro and in vivo hemostatic properties of cold-stored platelets, simplified logistics of resuscitation, and less dilutional coagulopathy resulting from both introducing plasma earlier in the resuscitation and transfusing a less dilute product compared to traditional component therapy (processing related dilution includes 63 mL of anticoagulant used for whole blood collection and 100 mL of additive solution to prolong red blood cell [RBC] shelf life). Wholeblood transfusion, however, poses a risk for hemolysis when the product is non-ABO identical to the transfusion recipient, as well as other immunologic risks, including possible D alloimmunization, if D+ whole blood is used, and transfusion-related acute lung injury (TRALI). This article provides an overview of the immunologic risks associated with whole blood transfusion, including ABO incompatibility (in terms of both red cell and plasma incompatibility), D alloimmunization, and TRALI, and describes how centers that currently use whole blood are mitigating these risks.
An ancient age of open-canopy landscapes in northern Madagascar? Evidence from the population genetic structure of a forest tree genetic analyses of two endemic rodent species.

ancient forest fragmentation and connectivity generate high a The Manuscript „How ancient forest fragmentation and riparian connectivity generate high levels of genetic diversity in a micro-endemic Mala gasy tree“ written by Salmona et al. deals with an interesting topic in a very interesting and understudied ecosystem. The authors used chloroplast SSRs and RADseq loci from the chloroplast, mitochondrial and nuclear genomes to study the genetic diversity and genetic structure of a narrow endemic tree species, Noronhia spinifolia in Madagascar. The study presents interesting data and results and is well written, nevertheless I have some concerns that I will lay out in detail below and that I hope will be helpful for the authors to improve the manuscript. and
Unsupervised Hybrid Fuzzy Clustering Algorithm for Anomaly Detection

Now a days every industry no matter what domain has customer service associates. Organizations with large number of customers have lot of data and events with respect to customer services. There are some unwanted events which causes customer dissatisfaction or customer attrition. These may be the rare events which may be unfavorable to the business and remain unidentified in huge data. For example, customer calls to the customer service associate and waiting in a long waiting queue, or transferred to multiple departments etc. This manuscript provides the real time algorithm to prioritize the inbound calls (inbound calls are from cus-tomer-to-customer service associate) in order to improve customer satisfaction and engagement experience by learning their behavior and detecting anomalous inbound calls. While working on fast increasing volumetric real-life data, it is impossible to tag each observation for modelling. This manuscript proposes an unsupervised machine learning algorithm for real time behavior learning and anomaly detection in inbound call data records. This is an unsupervised hybrid fuzzy algorithm which supports the real time untagged data.
On improved bounds and conditions for the convergence of Markov chains

We continue the work of improving the rate of convergence of ergodic homogeneous Markov chains. The setting is more general than in previous papers: we are able to get rid of the assumption about a common dominating measure and consider the case of inhomogeneous Markov chains as well as more general state spaces. We give examples where the new bound for the rate of convergence is the same as (resp. better than) the classical Markov–Dobrushin inequality.
Interlock-Free Multi-Aspect Rationalization for Text Classification

Explanation is important for text classification tasks. One prevalent type of explanation is rationales, which are text snippets of input text that suffice to yield the prediction and are meaningful to humans. A lot of research on rationalization has been based on the selective rationalization framework, which has recently been shown to be problematic due to the interlocking dynamics. In this paper, we show that we address the interlocking problem in the multi-aspect setting, where we aim to generate multiple rationales for multiple outputs. More specifically, we propose a multi-stage training method incorporating an additional self-supervised contrastive loss that helps to generate more semantically diverse rationales. Empirical results on the beer review dataset show that our method improves significantly the rationalization performance.
High Methicillin Resistance of Staphylococcus aureus and Coagulase-Negative Staphylococci in Imam Khomeini

infection is increasingly found in patients with cystic fibrosis (CF). a study to determine whether MRSA infec tion has a deleterious effect on the clinical status of children with CF. Children with MRSA in respiratory cultures during a 7- year period were identified and compared with controls matched for age, gender, and respiratory function. Respiratory function tests, anthropometric data, Shwachman- Kulczycki score, Northern chest radi ograph score, intravenous and nebulized
Industrial Automation Software: Using the Web as a Design Guide

—When looking the World Wide Web (and the Internet at large) as one giant application, we observe certain desirable properties that would also be welcome, but cannot be taken for granted, in industrial automation systems. In particular, subtasks that are unrelated from a functional point of view (such as Web sites) are usually well-separated from each other. Web services can play a key role in bringing separation of concerns to automation systems. The paper introduces a relevant standard (OPC UA). It also presents the Normalized Systems theory as a structured way of ensuring separation of concerns which is applicable to a wide range of application domains, from the Web to programs within automation controllers.
Gaining Insight into Effective Teaching of AI Problem-Solving Through CSEDM: A Case Study (Work in Progress)

CS education for all has been established as an important area of focus for both researchers and practitioners in recent years. At the same time, due to the increasing prevalence of AI technology in humans’ everyday lives, artiﬁcial intelligence (AI) education for K-12 is gaining special attention among CS educators. AI literacy, even more than general CS competencies, requires evidence-based research to be eﬀectively integrated in our schools. The common learning environments utilized for CS education enable us to go beyond conventional educational research approaches by providing a platform where detailed data can be collected from students’ interaction with CS-education-related activities. Thus, conventional educational research approaches coupled with insights gained from pattern recognition and student modeling approaches enable us to eﬀectively improve our instruction and to provide students with adaptive scaﬀolding. In this work, we present our ﬁrst AI curriculum module that is designed to teach a fundamental AI search algorithm, Breadth-First Search (BFS), through a series of progressively scaﬀolded activities. Data is collected from a preliminary pilot of this activity with a high-school student in the form of a think-aloud protocol, screen capture, submitted block-based programming artifacts, and interview questions. Our results demonstrate that our activities have been successful in increasing the student ’s knowledge about the BFS algorithm and more importantly, how this particular AI algorithm can be utilized to solve real world problems. Based on the results of this pilot study, we propose designing a comprehensive AI curriculum contextualized within a learning environment that collects detailed data from students’ progress to inform instructional design and facilitate adaptive scaﬀolding for students.
Experimental Study on the Noise Reduction Performance of the Ladder Sleeper Track on the Elevated Bridges of Urban Rail Transit

Field measurement was conducted at the test section of the ladder sleeper track on the elevated bridge of Beijing Metro Line 5.The site noise near the bridge was measured and analyzed when the train running on the ladder sleeper track and the common slab track.The spectral characteristics of the noise and the variation laws of noise with respect to train speed were studied.The test results indicate that the ladder sleeper track has better noise-reduction performance than the common slab track.In the time domain,the noise level increases with train speed,the noise of the ladder sleeper track is lower than that of the common slab track. In the frequency domain,the energy of the noise focuses between 60 and 4 000 Hz.The main frequency component of the noise near the track and at the 25 m measurement point is the wheel-track noise above 1 000 Hz,while that near the beam bottom emerges predominant frequencies between 100 and 150 Hz.The noise of the ladder sleeper track between 1 and 4 Hz and between 30 and 1 000 Hz is about 6 dB lower than that of the common slab track.The two types of track have equivalent noise level between 4 and 30 Hz and above 1 000 Hz.
Let’s Call! Using the Phone to Increase Acceptance of COVID-19 Vaccines ∗

Vaccinating against COVID-19 is the main hope to end the current pandemic. We develop and test experimentally three phone-based cumulative interventions to increase COVID-19 vaccine acceptance in Mozambique. First, the provision of a simple positive message informing about these vaccines. Second, the activation of social memory on the country’s success in eradicating wild polio. Finally, the inoculation against fake news by developing among participants a critical view towards misleading information. We ﬁnd that the combination of the three interventions increases COVID-19 vaccine acceptance and trust in institutions.