Miracle-dz/UniFAR-FTUs huggingface dataset:
Glia-derived exosomal miR-274 targets Sprouty in trachea and synaptic boutons to modulate growth and responses to hypoxia

Significance Our study provides significant advances in the understanding of circulating exosomal microRNA (miRNA) in animals. Circulating exosomal miRNAs mediate communication among tissues and organs. In glia, mature miR-274 is produced and secreted to the circulating hemolymph to target the recipient cells, neurons, and tracheal cells. We also identified the target gene sty whose expression is down-regulated by miR-274 in the target/recipient cells. Downregulation of Sty leads to upregulation of MAPK signaling, thereby promoting the growth of synaptic boutons and tracheal branches. Thus, glia-derived miR-274 might be deemed a “gliotransmitter” to mediate communication with neurons and tracheal cells. The modulation of tracheal branches by glial-derived miR-274 is also crucial for fine-tuning larval behavior in response to hypoxia. Secreted exosomal microRNAs (miRNAs) mediate interorgan/tissue communications by modulating target gene expression, thereby regulating developmental and physiological functions. However, the source, route, and function in target cells have not been formally established for specific miRNAs. Here, we show that glial miR-274 non-cell-autonomously modulates the growth of synaptic boutons and tracheal branches. Whereas the precursor form of miR-274 is expressed in glia, the mature form of miR-274 distributes broadly, including in synaptic boutons, muscle cells, and tracheal cells. Mature miR-274 is secreted from glia to the circulating hemolymph as an exosomal cargo, a process requiring ESCRT components in exosome biogenesis and Rab11 and Syx1A in exosome release. We further show that miR-274 can function in the neurons or tracheal cells to modulate the growth of synaptic boutons and tracheal branches, respectively. Also, miR-274 uptake into the target cells by AP-2–dependent mechanisms modulates target cell growth. In the target cells, miR-274 down-regulates Sprouty (Sty) through a targeting sequence at the sty 3′ untranslated region, thereby enhancing MAPK signaling and promoting cell growth. miR-274 expressed in glia of an mir-274 null mutant is released as an exosomal cargo in the circulating hemolymph, and such glial-specific expression resets normal levels of Sty and MAPK signaling and modulates target cell growth. mir-274 mutant larvae are hypersensitive to hypoxia, which is suppressed by miR-274 expression in glia or by increasing tracheal branches. Thus, glia-derived miR-274 coordinates growth of synaptic boutons and tracheal branches to modulate larval hypoxia responses.
Implementation of Borda Clustering to Form Zakat Receiving Villages Clusters Based on Region Potency in Indonesia

Indonesia has a huge potential of zakat funds up to IDR 286 trillion. This is a positive impact of the large moslem population that reached 216.66 million in 2016. However, the absorption and distribution of zakat funds have not been maximized, for example in 2015 the absorption of zakat was IDR 3.70 trillion and the distributed fund were only 61.61 percent. The utilization of region’s potential data can be an alternative approach to expand zakat distribution. This research implements Borda Clustering algorithm on the 2011 Potential Village Skyline Data from the Central Statistics Agency to produce priority area clusters of zakat receiver in Indonesia. Evaluations using the Silhouette Score against the cluster resulted 0.72 in range of −1 to +1. The regional clusters prioritized in obtaining zakat funds are mostly in Papua, Nangro Aceh Darrusalam, and West Papua Province. The results of the clusters can be used as suggestion for the zakat management organization to maximize the distribution of zakat funds.
Hotspot Analysis of Short-term Load Forecasting Based on Knowledge Graph

Short-term load forecasting is instructive to the daily work of the electrical industry, and the analysis of its evolutionary trends and changes of hotspot will promote the development of this field. Based on 5,984 literatures in the field of short-term load forecasting from 2012 to 2020 in the WOS database, Citespace was used to generate keyword cooccurrence maps and co-cited maps, and in-depth analysis of hotspot in the field of power load prediction in the form of visual analysis. The results show that load forecasting technology is dominated by deep learning, and load forecasting is applied to user energy management.
Grade10: A Framework for Performance Characterization of Distributed Graph Processing

Graph processing is one of the most important and ubiquitous classes of analytical workloads. To process large graph datasets with diverse algorithms, tens of distributed graph processing frameworks emerged. Their users are increasingly expecting high performance for diversifying workloads. Meeting this expectation depends on understanding the performance of each framework. However, performance analysis and characterization of a distributed graph processing framework is challenging. Contributing factors are the irregular nature of graph computation across datasets and algorithms, the semantic gap between workload-level and system-level monitoring, and the lack of lightweight mechanisms for collecting fine-grained performance data. Addressing the challenge, in this work we present Grade10, an experimental framework for fine-grained performance characterization of distributed graph processing workloads. Grade10 captures the graph workload execution as a performance graph from logs and application traces, and builds a fine-grained, unified workload-level and system-level view of performance. Grade10 samples sparsely for lightweight monitoring and addresses the problem of accuracy through a novel approach for resource attribution. Last, it can identify automatically resource bottlenecks and common classes of performance issues. Our real-world experimental evaluation with Giraph and PowerGraph, two state-of-the-art distributed graph processing systems, shows that Grade10 can reveal large differences in the nature and severity of bottlenecks across systems and workloads. We also show that Grade10 can be used in debugging processes, by exemplifying how we find with it a synchronization bug in PowerGraph that slows down affected phases by $1.10-2.50\times$. Grade10 is an open-source project available at https://github.com/atlarge-research/grade10.
A Novel Approach based on Microservices Architectures and Computer Vision to improve access to Culture Heritage

The combination of technologies enabling the Internet of Things and advanced computer vision techniques find application in several real contexts. Among these, the tourism sector and in particular the access to artistic and cultural heritage represents a context where the interest in the use of innovative technologies is very high. In these contexts, the interest is mainly focused on improving the accessibility of artistic and cultural places and guarantee personalized contents in order to make the tourist experience unique during a cultural visit. The use of innovative technologies such as mobile devices, BLE beacons, and Cloud services is essential for achieving these objectives. Among the computer vision techniques, a particularly useful and interesting technique in the aforementioned contexts is based on the extraction of keypoints. This work summarizes the main scientific results obtained within a research project called INSTANT. The description of the system architecture has been reported and the first functional validation of the entire proposed system has been discussed through some demo labs.
An adaptive data compression mechanism for Wireless Sensor Networks in the Smart Grid Scenarios

The smart grids (SG) present a new architecture for the generation, transmission, and distribution of electrical energy, combined with new information and communication technologies aimed at guaranteeing the required demand, quality, availability and reliability of energy supply to its consumers. The Smart Meter (SM) is installed in each residence whose functionality is to record information related to consumption and send it to the Electric Power Company (EPC) through a two-way communication infrastructure. However, a large amount of data transmitted by the SG network can cause significant congestion in the communication infrastructure, leading to packet loss, increased latency and other issues affecting decision-making, which must be done in real-time. Thus, this work presents a performance analysis of the two-way communication infrastructure of SG in two experimental scenarios: with and without the application of an adaptive data compression mechanism. The communication infrastructure is presented logically and in practice, where a testbed is deployed using IEEE 802.11 communication technologies on the Message Queuing Telemetry Transport (MQTT) protocol to represent the communication infrastructure and analyze network performance using the Wireshark tool in a real scenario.
A Study of QoE-Aware Adaptation Mechanism for DASH Video Streaming based on Objective Visual Quality Assessment

Dynamic Adaptive Streaming over HTTP (DASH) is a largely used video streaming technique. One key point is its adaptation mechanism which resides at the client’s side. This allows various context-aware adaptation strategies in order to optimize the overall Quality of Experience (QoE) of the video streaming. In this paper, we present a study on an adaptation mechanism which uses an objective visual quality assessment, namely the Structural Similarity Index Measurement (SSIM) metric, as a key criterion for adaptation. More specifically, the SSIM helps to maximize the effective use of the available bandwidth, in the sense that we adopt a higher bitrate not only because it is allowed by network conditions, but also because it does bring a significant visual quality improvement (measured through SSIM metric). In this way, an upgrade in bandwidth consumption will be allowed only if there is a real contribution to visual quality. This study has been tested through a series of experimental results obtained with several strategies for the choice of the threshold value. Our tests are all based on real mobile-network traffic traces and real video sequences.
Design of microstrip patch sensor for non-invasive body electrolyte monitoring

Diagnostic of Human sweat is becoming a gold standard for non-invasive procedure to identify electrolyte levels. In this paper a low cost, non-invasive microstrip patch antenna-based sensor is proposed to detect human electrolyte level from sweat. The designed sensor is a Copper patch antenna with operating frequency of 0.5 GHz to 3.5 GHz. The antenna is designed on 1.5 mm thick paper-based substrate. For sensitivity comparison, three different dielectric constants ranging from 1.0 to 2.0 have been tested in designed antenna structure having resonant frequency of 1.575 GHz. The dielectric properties of substrate are transmuted when liquid is absorbed by it. The variation of magnitude of reflection coefficient and shift in resonant frequency is observed for various concentrations of sodium chloride (NaCl) ranging from 50 mmol/L to 300 mmol/L. First resonance is considered for data analyzing, as there is significant change in both resonance frequency and magnitude of reflection coefficients for different level of electrolytes. Paper substrate with higher dielectric constant shows better sensitivity compared to lower one.
Design and Analysis of a Slotted Micro-cantilever based Biosensor for Detection of P24 Antigen in HIV Diagnoses

This paper presents the design and simulation of a slotted micro-cantilever based biosensor for HIV (Human Immune Deficiency Virus) detection with P-24 antigen. In this work, dynamic mode analysis is performed to study the frequency and the deflection of the micro-cantilever. The design procedure and finite element analysis of the model are conducted by COMSOL Multiphysics 5.4 software. The model of the micro-cantilever is multilayered which contains seven slots. The antibody coating on the cantilever surface interacts the present antigen of P-24 in the blood sample which deflects the body of the micro-cantilever. The simulated result shows a significant change in the displacement of the cantilever which results in a better sensitivity. It is observed that the displacement of the proposed model is as 3.47×106 µm.
Vibrations Signal Analysis for Generator Machine Fault Detection

Generators are essential whenever there are power interruptions. They provide emergency electrical power in establishments and even in remote areas. One fault in generator machines is the fluctuations of rotational speed in crankshaft of engine due to prolonged operation. When this condition is neglected, this will lead to generator shutdown, to increase in vibrations, and eventually cause damage to the connected load. This study proposes a prototype that can be utilized to diagnose the operating frequency of a generator machine. The system is comprised of accelerometer sensors and microcomputer with signal analysis and pattern recognition. A total of 607 data sets of vibration signals for training and validation were gathered under different rotational speeds of generators with different load conditions. Based on 120 trials, the system demonstrated a 93.33 % accuracy.
Motion Sensing for Wireless Body Area Networks Based on Android Using Wi-Fi Direct Transmission

Wireless sensor networks (WSN) applications were developed with a wide variety of applications. Wireless body area network (WBAN) was one of the implementations of WSN. The implementation used a sensor that was attached to the body to monitor body motion. In this article, we present our research about motion sensing for wireless body area networks (WBAN) that used Wi-Fi direct transmission. This article proposed a motion-sensing for wireless body area networks (MOSBAN) with Wi-Fi direct transmission based on android to send the value of accelerometer and gyroscope from smartphone to smartphone. MOSBAN is expected can send the value of sensors accurately with Wi-Fi direct transmission. This article presented a comprehensive result of 50 meters distance from smartphone1 to smartphone 2. The validity of MOSBAN is proven by exacted 100% of data transmission using Wi-Fi Direct.
Versatile Externally Modulated Lasers Technology for Multiple Telecommunication Applications

This paper presents our technological approach for Externally Modulated Lasers (EMLs), based on Semi-Insulating Buried Heterostructure (SIBH) waveguide. We use Gas Source Molecular Beam Epitaxy (GSMBE) to grow the Phosphorus-based multi-quantum wells for both laser and modulator sections with butt-joint integration. The same GSMBE grows the p-doped InP claddings with low-diffusion Be dopant, leading to an accurate control of doping profiles, ensuring very steep modulator extinction curves. We present the main EML design rules and compromises, then apply them to different EMLs aiming at major telecom and datacom applications. After presenting characteristics of the standard 10 Gb/s C-band EML, we propose a 10 Gb/s EML at 1577 nm for next generation access networks, with a record high 10.5 dBm facet modulated power. Then we present high-speed EMLs up to 56 GBaud for datacenter interconnections, both in O- and C-band, aiming at very low peak-to-peak modulation voltage (< 1.2 V), high facet modulated power (> 4.4 dBm), and compatible with uncooled operation (20 to 70 °C). These results confirm the efficiency and versatility of this technological platform for EMLs in a broad range of applications.
Validity and internal consistency of EQ-5D-3L quality of life tool among pre-dialysis patients with chronic kidney disease in Sri Lanka, a lower middle-income country

Objective EQ-5D-3L is a generic QOL tool used mainly in economic evaluations. Burden of Chronic Kidney Disease (CKD) is rising in Sri Lanka. Assessing the validity of generic QOL tools creates new opportunities of their utilization among patients with CKD. Methods A cross-sectional study was conducted among 1036 CKD patients, selected using the simple random sampling technique. The validity was tested with six a-priori hypotheses. These included construct validity assessments, evaluating convergent validity and performing known group comparisons. EQ-5D-3L, Short Form-36 (SF-36), Center for Epidemiological Studies Depression Scale (CES-D-20) and General Health Questionnaire-12 (GHQ-12) were used to assess QOL, presence of depression and psychological distress respectively. Internal consistency of the whole tool and when each item is removed was assessed by Cronbach alpha. Results The response rate was 99.2%. Majority of participants were males (n=646,62.4%) in the age category of 41-60 (n=530; 51.2%). Most were in either stage 4 or 5 of CKD (n=646,75.1%). The summary measures of SF-36, positively and significantly correlated with the EQ-5D-3L index and VAS scores (p<0.001). EQ-5D-3L QOL scores were significantly different between the group with depression and without as measured by CES-D-20 (p<0.001). Assessed using GHQ-12, similar significance was detected between the group with psychological distress and without (p<0.001). The Cronbach alpha was 0.834 and when each item was removed, ranged from 0.782 to 0.832. Conclusion EQ-5D-3L is a valid generic QOL tool with satisfactory internal consistency to be used among CKD patients in the pre-dialysis stage.
Determination of Low-Level Audio Descriptors of a Musical Instrument Sound Using Neural Network

Audio files and the audio channel of video files can be described with temporal, spectral, cepstral, and perceptual audio descriptors. The so-called low-level descriptors are closely related to the signal characteristics. One can discern at least three levels of extraction granularity from the signal: at any point in the signal, in small arbitrary regions (i.e., frames) and longer pre-segmented regions. Even though there are tools (e.g., MIRToolbox, Python/libROSA) available for computing these descriptors, the resulting feature vector is always redundant as it contains many high-correlated descriptors and there are some limitations connected to the performance of these tools. That is why, in this study, a method for obtaining those descriptors using Artificial Neural Network (ANN) with a deep structure (i.e., DNN) is proposed. In such a scheme, the raw audio signal representing a given musical instrument is fed to the DNN input. Such a network can be used as a standalone module or as a pre-trained part of the bigger architecture. The results of deep network performance in the context of MPEG-7 descriptor derivation are shown along with the loss function convergence and behavior.
On the Performance and Adoption of Search-Based Microservice Identification with toMicroservices

The expensive maintenance of legacy systems leads companies to migrate such systems to microservice architectures. This migration requires the identification of system’s legacy parts to become microservices. However, the successful identification of microservices, which are promising to be adoptable in practice, requires the simultaneous satisfaction of many criteria, such as coupling, cohesion, reuse and communication overhead. Search-based microservice identification has been recently investigated to address this problem. However, state-of-the-art search-based approaches are limited as they only consider one or two criteria (namely cohesion and coupling), possibly not fulfilling the practical needs of developers. To overcome these limitations, we propose toMicroservices, a many-objective search-based approach that considers five criteria, the most cited by practitioners in recent studies. Our approach was evaluated in a real-life industrial legacy system undergoing a microservice migration process. The performance of toMicroservices was quantitatively compared to a baseline. We also gathered qualitative evidence based on developers’ perceptions, who judged the adoptability of the recommended microservices. The results show that our approach is both: (i) very similar to the most recent proposed approach on optimizing the traditional criteria of coupling and cohesion, but (ii) much better when taking into account all the five criteria. Finally, most of the microservice candidates were considered adoptable by practitioners.
Impact of Nonlinear Distortion with the Rapp Model on the GFDM System

The fifth-generation (5G) mobile communication system has various types of applications, with each application requiring different requirements. The characteristic of this system is flexible architecture. Generalized Frequency Division Multiplexing (GFDM) fulfills these requirements. However, as with other multicarrier systems, GFDM is also susceptible to nonlinear distortion due to the High Power Amplifier (HPA). In this paper, we examine the nonlinear distortion effect of the Rapp model on the GFDM system. We investigate 3 parameters namely Bit Error Rate (BER), the signal spectrum, and the Peak Average Power Ratio (PAPR). Simulation results show that PAPR on GFDM is more resistant to nonlinear distortion due to pulse shaping. The PAPR GFDM value with the Rapp Model is around 3dB, better than the Saleh Model. PSD on GFDM has not changed much due to nonlinear distortion, out-of-band (OOB) values from GFDM are still good around -90dB. But, BER performance on GFDM is very bad if given nonlinear distortion, At the time of Eb/No20dB, the BER achieved was less than 10-1.
Improving Fuzzing through Controlled Compilation

We observe that operations performed by standard compilers harm fuzzing because the optimizations and the Intermediate Representation (IR) lead to transformations that improve execution speed at the expense of fuzzing. To remedy this problem, we propose ‘controlled compilation’, a set of techniques to automatically re-factor a program's source code and cherry pick beneficial compiler optimizations to improve fuzzing. We design, implement and evaluate controlled compilation by building a new toolchain with Clang/LLVM. We perform an evaluation on 10 open source projects and compare the results of AFL to state-of-the-art grey-box fuzzers and concolic fuzzers. We show that when programs are compiled with this new toolchain, AFL covers 30 % new code on average and finds 21 additional bugs in real world programs. Our study reveals that controlled compilation often covers more code and finds more bugs than state-of-the-art fuzzing techniques, without the need to write a fuzzer from scratch or resort to advanced techniques. We identify two main reasons to explain why. First, it has proven difficult for researchers to appropriately configure existing fuzzers such as AFL. To address this problem, we provide guidelines and new LLVM passes to help automate AFL's configuration. This will enable researchers to perform a fairer comparison with AFL. Second, we find that current coverage-based evaluation measures (e.g. the total number of visited lines, edges or BBs) are inadequate because they lose valuable information such as which parts of a program a fuzzer actually visits and how consistently it does so. Coverage is considered a useful metric to evaluate a fuzzer's performance and devise a fuzzing strategy. However, the lack of a standard methodology for evaluating coverage remains a problem. To address this, we propose a rigorous evaluation methodology based on ‘qualitative coverage’. Qualitative coverage uniquely identifies each program line to help understand which lines are commonly visited by different fuzzers vs. which lines are visited only by a particular fuzzer. Throughout our study, we show the benefits of this new evaluation methodology. For example we provide valuable insights into the consistency of fuzzers, i.e. their ability to cover the same code or find the same bug across multiple independent runs. Overall, our evaluation methodology based on qualitative coverage helps to understand if a fuzzer performs better, worse, or is complementary to another fuzzer. This helps security practitioners adjust their fuzzing strategies.