Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Error Resiliency of Sparse Code Multiple Access Using Precoding and Non-orthogonal Signaling Techniques

The high data rate and seamless connectivity requirements in cellular networks make it necessary to explore key-enabling technologies for next generation (5G) wireless communication networks. Recent studies in 5G physical layer technologies consider the use of non-orthogonal multiple access and non-orthogonal signaling techniques as means to achieve better spectral efficiency at an acceptable link level performance. In this paper, a new multi-user transmission scheme is presented which combines Sparse Code Multiple Access (SCMA), a non-orthogonal multiple access technique that exploits codeword sparsity, with a non-orthogonal multicarrier signal that uses precoding and tight subcarrier packing schemes. The study assesses the impact of bandwidth compression and iterative decoding complexity on the bit error rate (BER) performance of the system. Modeling is done in simple Gaussian noise channels and channels experiencing independent frequency-flat Rayleigh fading per sample. Simulation results are provided showing that proposed transmission scheme can achieve better error resiliency while maintaining the spectral efficiency of conventional SCMA.
Master of Web Puppets: Abusing Web Browsers for Persistent and Stealthy Computation

The proliferation of web applications has essentially transformed modern browsers into small but powerful operating systems. Upon visiting a website, user devices run implicitly trusted script code, the execution of which is confined within the browser to prevent any interference with the user's system. Recent JavaScript APIs, however, provide advanced capabilities that not only enable feature-rich web applications, but also allow attackers to perform malicious operations despite the confined nature of JavaScript code execution. In this paper, we demonstrate the powerful capabilities that modern browser APIs provide to attackers by presenting MarioNet: a framework that allows a remote malicious entity to control a visitor's browser and abuse its resources for unwanted computation or harmful operations, such as cryptocurrency mining, password-cracking, and DDoS. MarioNet relies solely on already available HTML5 APIs, without requiring the installation of any additional software. In contrast to previous browser-based botnets, the persistence and stealthiness characteristics of MarioNet allow the malicious computations to continue in the background of the browser even after the user closes the window or tab of the initial malicious website. We present the design, implementation, and evaluation of a prototype system, MarioNet, that is compatible with all major browsers, and discuss potential defense strategies to counter the threat of such persistent in-browser attacks. Our main goal is to raise awareness regarding this new class of attacks, and inform the design of future browser APIs so that they provide a more secure client-side environment for web applications.
Correlation Based Evaluation for Search Tools

An important task for information seeker is to find a search engine that requires minimum users efforts and delivers relevant and desired results in minimum span of time. It is likely the results to be organized in such a manner that relevancy of results decreases gradually as search length increases. In this paper, we propose a correlation based algorithm that accepts explicit users’ judgments about the relevancy of web documents in Ranked Precision Metric form. To remove the possibility of biased judgments, the algorithm incorporates implicit users feedback in the form of Session duration, Dwell time and click hits. We have conducted an extensive evaluation and comparison of three popular search engines with the help of 25 TREC queries. The paper presents the initial results derived with the help of Implicit users feedback and Ranked Precision metric.
Huge information extraction techniques of Data Security

In an Organization which can be prosperity there is a huge volume of data to be destitute down. The information lands in examination at fast and the significant measure of data variety. Like shrewd titanic information is named as the “Epic Data”. This Huge information when investigated really can expect a fundamental part looking ahead with business information. Breaking down for lot of information for a while later diminishing it, seeing two or three cases out of it can enable the data security to supervisor screen even the scarcest points of interest of the exercises continuing in the endeavour. Making utilization of information mining and applying fit figuring's of machine taking in the security of an undertaking can be particularly well dealt with. We correspondingly thought to execute the examination and also design procedures.
Converting Galactose into the Rare Sugar Talose with Cellobiose 2-Epimerase as Biocatalyst

Cellobiose 2-epimerase from Rhodothermus marinus (RmCE) reversibly converts a glucose residue to a mannose residue at the reducing end of β-1,4-linked oligosaccharides. In this study, the monosaccharide specificity of RmCE has been mapped and the synthesis of d-talose from d-galactose was discovered, a reaction not yet known to occur in nature. Moreover, the conversion is industrially relevant, as talose and its derivatives have been reported to possess important antimicrobial and anti-inflammatory properties. As the enzyme also catalyzes the keto-aldo isomerization of galactose to tagatose as a minor side reaction, the purity of talose was found to decrease over time. After process optimization, 23 g/L of talose could be obtained with a product purity of 86% and a yield of 8.5% (starting from 4 g (24 mmol) of galactose). However, higher purities and concentrations can be reached by decreasing and increasing the reaction time, respectively. In addition, two engineering attempts have also been performed. First, a mutant library of RmCE was created to try and increase the activity on monosaccharide substrates. Next, two residues from RmCE were introduced in the cellobiose 2-epimerase from Caldicellulosiruptor saccharolyticus (CsCE) (S99M/Q371F), increasing the kcat twofold.
Defects in The Next Release; Software Defect Prediction Based on Source Code Versions

Nowadays, automatic defect prediction has attracted wide attention, but many researchers neglected the availability of huge amount of data in version control systems. Deploying the next release of the project on time and on schedule urges software project managers to optimize efforts based on their limited human resources. Usually software projects have longterm version history from which we can build a repository of rnetrics, defects, fixes and causes. Having an accurate estimate of the distribution of bugs across components helps project managers to optimize the available resources by focusing on the problematic system parts. We have introduced a benchmark for interested readers in this field which provides all the data needed to apply a large array of prediction techniques proposed in the literature. Our dataset allows the reproduction of the experiments reported in this paper and their comparison with novel defect prediction approaches. We evaluated a selection of representative classification approaches and compared the performance of major classifiers using various evaluation measures such as Precision, Recall, F-measure, G-mean and AUC. The experimental results show that Random Forest is a powerful method and as a good candidate for software defect prediction can be used for further improvements.
Rapid communication of efforts to resolve differences or update variant interpretations in ClinVar through case-level data sharing

In this issue of Cold Spring Harbor Molecular Case Studies, we have included the first publication (Grant et al. 2018) of a new article type, the “Variant Discrepancy Resolution. ” This format has been created in response to the growth and utility of the ClinVar database as a repository of variant-level data (Landrum et al. 2018). The largest number of ClinVar submissions, >85%, are now coming from clinical laboratories that have voluntarily shared their interpretations of variants, as mechanisms to both crowdsource the enormous challenge of variant interpretation and ensure the most accurate interpretations are returned to patients. Behind each submission is often a rich body of evidence, some of which is published, but often valuable unpublished case data cannot be easily shared because of the lack of patient consent in the clinical testing process. Instead, laboratories are alerted to the potential for a different evidence base when their submitted variant interpretations show up as conflicting in ClinVar. These alerts prompt submitters to reach out and exchange this detailed case-level data, which can then be used to resolve the differences in interpretation or, in some cases, move a variant that is consistently classified as a variant of uncertain significance (VUS) up or down toward a pathogenic or benign interpretation. In this issue, two clinical laboratories had both interpreted a variant as a VUS, but, upon contact by a patient whose fetus harbored the variant, they exchanged detailed case-level evidence that enabled the variant to move to a Likely Benign interpretation. This rapid exchange also led the family to continue the pregnancy without concern for an adverse outcome. Recent publications from ClinGen have also demonstrated the benefit of sharing unpublished data from clinical laboratories to resolve discrepancies or reclassify VUSs (Gelb et al. 2018; Kelly et al. 2018). The Editors of Cold Spring Harbor Molecular Case Studies encourage these interlaboratory exchanges and have generated an article type to allow this work to be recognized and more rapidly captured in a documented form that the community can access. Clinical laboratories can obtain reports of their variant interpretation differences from the ClinVar Miner website (Henrie et al. 2018; https://clinvarminer.genetics.utah.edu/) or request a custom report from ClinGen (clingen@clinicalgenome.org), which will also highlight variants in which the submitter has an outlier interpretation compared to other submitters (Harrison et al. 2018). Studies have shown that most of the differences in interpretation can be resolved, with a large percentage due to the exchange of unpublished data (Garber et al. 2016; Harrison et al. 2017). We encourage the community to engage in Corresponding author: hrehm@partners.org
Developing Lignosulfonate-Based Activated Carbon Fibers

In this study, electrospinning technology, physical activation, and carbonization processing were applied to produce lignosulfonate-based activated carbon fibers. The porous structure of the produced lignosulfonate-based activated carbon fibers primarily contained mesopores and a relatively small amount of micropores. Moreover, insufficient carbonization caused fiber damage during CO2 activation. The weight loss rate and specific surface area increased with increase in carbonization time, and products with carbonization temperatures of 700 °C were of higher quality than those with other temperatures. Moreover, the two-step carbonization process provided fibers with improved quality because of a low weight loss rate, improved processing, and high surface area. Lignosulfonate-based activated carbon fibers can be used as a highly efficient adsorption and filtration material, and further development of its applications would be valuable.
Optical Fronthaul Options for Meeting 5G Requirements

New functional splits for the 5G Radio Access Network have been identified so that fronthaul will no longer need to transport sampled time-domain waveforms. However, the different functional split points place differing demands on the fronthaul transport, while also posing different constraints to 5G techniques, such as massive MIMO. According to these conflicting demands, it is likely that in many cases, more than one split point may be needed in the same radio access network.
M-ary ASK Modulation in FSO System with SIMO over Log-normal Atmospheric Turbulence with Pointing Errors

In this paper, we analyze the average bit error rate (BER) and outage probability performances of a free-space optical (FSO) system. We used M-ary amplitude shift keying modulation (M-ary ASK) scheme. Lognormal atmospheric turbulence with the effect of pointing errors (PE) and, path loss is employed to model the FSO channel. However, to overcome the degradation in BER performance due to pointing errors, the single-input-multiple-output technique with maximal ratio combining diversity is used. For instance, the average BER for M=2 without using SIMO is 1.944×10−6 at 0 dBm transmitted power, while the average BER for M=2 with using SIMO technique with N=2 become 1.512×10−11 at 0 dBm. The asymptotic expression for average BER and the closed-form expression for outage probability are both driven and tested with and without SIMO.
Unleashing Parallelism With Minimal Test Inflation in Multi-Threaded Test Pattern Generation

Shared-memory systems enable parallel computing for the automatic test pattern generation (ATPG). Although the existing techniques for parallel ATPG reach near-linear speedup, test inflation becomes a common problem in its practicality. Therefore, this paper proposes a multi-threaded test pattern generation called <italic>MT-TPG</italic> that can suppress test inflation and accelerate fault processing, simultaneously, to retain high parallelism. For suppressing test inflation, <italic>hard-fault shuffling</italic> (<italic>HFS</italic>) and <italic>concurrent-fault interruption</italic> (<italic>CFI</italic>) are involved to avoid repeated detection of the same fault among different threads. For accelerating fault processing, the <italic>potentially-droppable-fault removal</italic> (<italic>PDFR</italic>) and <italic>single-pattern parallel-fault simulation</italic> (<italic>SPPFSim</italic>) collectively drop not-yet-detected faults as early as possible for shortening the overall execution time of ATPG. According to our experimental results, the <italic>HFS</italic> and <italic>CFI</italic> can successfully suppress test inflation to < 4% on 17 benchmark circuits; <italic>PDFR</italic> and <italic>SPPFSim</italic> can achieve 13.7X speedup using 16 threads on average. As a result, <italic>MT-TPG</italic> is proven effective at unleashing parallelism with minimal test inflation on shared-memory systems.
Telomeric Repeat-Binding Factor Homologs in Entamoeba histolytica: New Clues for Telomeric Research

Telomeric Repeat Binding Factors (TRFs) are architectural nuclear proteins with critical roles in telomere-length regulation, chromosome end protection and, fusion prevention, DNA damage detection, and senescence regulation. Entamoeba histolytica, the parasite responsible of human amoebiasis, harbors three homologs of human TRFs, based on sequence similarities to their Myb DNA binding domain. These proteins were dubbed EhTRF-like I, II and III. In this work, we revealed that EhTRF-like I and II share similarity with human TRF1, while EhTRF-like III shares similarity with human TRF2 by in silico approach. The analysis of ehtrf-like genes showed they are expressed differentially under basal culture conditions. We also studied the cellular localization of EhTRF-like I and III proteins using subcellular fractionation and western blot assays. EhTRF-like I and III proteins were enriched in the nuclear fraction, but they were also present in the cytoplasm. Indirect immunofluorescence showed that these proteins were located at the nuclear periphery co-localizing with Lamin B1 and trimethylated H4K20, which is a characteristic mark of heterochromatic regions and telomeres. We found by transmission electron microscopy that EhTRF-like III was located in regions of more condensed chromatin. Finally, EMSA assays showed that EhTRF-like III forms specific DNA-protein complexes with telomeric related sequences. Our data suggested that EhTRF-like proteins play a role in the maintenance of the chromosome ends in this parasite.
Enhancing the Isolation and Performance of Control Planes for Fog Computing

Fog computing, which places computing resources close to IoT devices, can offer low latency data processing for IoT applications. With software-defined networking (SDN), fog computing can enable network control logics to become programmable and run on a decoupled control plane, rather than on a physical switch. Therefore, network switches are controlled via the control plane. However, existing control planes have limitations in providing isolation and high performance, which are crucial to support multi-tenancy and scalability in fog computing. In this paper, we present optimization techniques for Linux to provide isolation and high performance for the control plane of SDN. The new techniques are (1) separate execution environment (SE2), which separates the execution environments between multiple control planes, and (2) separate packet processing (SP2), which reduces the complexity of the existing network stack in Linux. We evaluate the proposed techniques on commodity hardware and show that the maximum performance of a control plane increases by four times compared to the native Linux while providing strong isolation.
MQTT+: Enhanced Syntax and Broker Functionalities for Data Filtering, Processing and Aggregation

In the last few years, the Message Queueing Telemetry Transport (MQTT) publish/subscribe protocol emerged as the de facto standard communication protocol for IoT, M2M and wireless sensor networks applications. Such popularity is mainly due to the extreme simplicity of the protocol at the client side, appropriate for low-cost and resource-constrained edge devices. Other nice features include a very low protocol overhead, ideal for limited bandwidth scenarios, the support of different Quality of Services (QoS) and many others. However, when an edge device is interested in performing processing operations over the data published by multiple clients, the use of MQTT may result in high network bandwidth usage and high energy consumption for the end devices, which is unacceptable in resource constrained scenarios. To overcome these issues, we propose in this paper MQTT+, which provides an enhanced protocol syntax and enrich the pub/sub broker with data filtering, processing and aggregation functionalities. MQTT+ is implemented starting from an open source MQTT broker and evaluated in different application scenarios.
Using Graph-Pattern Association Rules On Yago Knowledge Base

We propose the use of Graph-Pattern Association Rules (GPARs) on the Yago knowledge base. Extending association rules for itemsets, GPARS can help to discover regularities between entities in knowledge bases. A rule-generated graph pattern (RGGP) algorithm was used for extracting rules from the Yago knowledge base and a graph-pattern association rules algorithm for creating association rules. Our research resulted in 1114 association rules, where the value of standard confidence at 50.18% was better than partial completeness assumption (PCA) confidence at 49.82%. Besides that the computation time for standard confidence was also better than for PCA confidence
Hardware/Software Implementation Factors Influencing Ethernet Latency

Minimum Cycle Time is a common performance indicator adopted to compare Real-Time Ethernet protocols. Though serving its purpose, Minimum Cycle Time excludes the delays inside the sending and receiving nodes, so it is insufficient to estimate the end-to-end latency. In this work, we describe some implementation possibilities of an Ethernet node in a System-on-Chip and present measurements of the delay to send/receive packets from/to the application layer. We chose different points in the software to make the measurement, so the results cover more use-cases. We found the Ethernet Lite Media Access Controller (MAC) to be faster than the hard MAC (GEM) and the Lightweight IP stack to add less than 2.2 μs. Finally, we show how a hardware accelerator can reduce the delay of high-priority packets by 1.4 μs.
Deep Residual Convolutional Network for Natural Image Denoising and Brightness Enhancement

Because of the low-light shooting environment, the camera sensor will loss huge details and fuzzy edge. A deep low-light residual convolutional network (LRCNN) is proposed in this paper, which utilizes the sparse coding feature to get the true signal and adaptively adjusts the image exposure in the low-light state. The residual connections in LRCNN help us preserve more potential detail information in the original picture and accelerate the training speed of the network. Many existing image enhancement algorithms only are able to address one aspect of image problems. We designed a neural network system which could deal with many image processing problems at the same time. The experimental results show that our neural network system well optimizes the images that affected by darkness and noise. It also avoids an artificial appearance in generating the image patches.
Are fintechs really a hype? A machine learning-based polarity analysis of Brazilian posts on social media

Fintechs are technology companies that, in contrast to traditional banks, are engaged in digital solutions for payment, money transfers, and real-time notifications. Taking advantage of digital means of communication, most of the service interactions between fintechs and customers occurs via chats or posts in social media. In this work, our goal is to use machine learning to analyze these posts and identify what are the terms used by customers to express positive, neutral and negative customer experiences. During this analysis, we assess the following questions using data from the 3 biggest fintechs in Brazil: (i) what are the most commented topics on social media regarding fintechs, (ii) what are the words more often used by customers to express positive, negative and neutral reactions to the customer service obtained; and (iii) what kind of machine learning model should a fintech use to automatically identify whether a post is positive, negative or neutral.
Insights into photovoltaic properties of ternary organic solar cells from phase diagrams

ABSTRACT The efficiency of ternary organic solar cells relies on the spontaneous establishment of a nanostructured network of donor and acceptor phases during film formation. A fundamental understanding of phase composition and arrangement and correlations to photovoltaic device parameters is of utmost relevance for both science and technology. We demonstrate a general approach to understanding solar cell behavior from simple thermodynamic principles. For two ternary blend systems we construct and model phase diagrams. Details of EQE and solar cell parameters can be understood from the phase behavior. Our blend system is composed of PC70BM, PBDTTT-C and a near-infrared absorbing cyanine dye. Cyanine dyes are accompanied by counterions, which, in a first approximation, do not change the photophysical properties of the dye, but strongly influence the morphology of the ternary blend. We argue that counterion dissociation is responsible for different mixing behavior. For the dye with a hexafluorophosphate counterion a hierarchical morphology develops, the dye phase separates on a large scale from PC70BM and cannot contribute to photocurrent. Differently, a cyanine dye with a TRISPHAT counterion shows partial miscibility with PC70BM. A large two-phase region dictated by the PC70BM: PBDTTT-C mixture is present and the dye greatly contributes to the short-circuit current. Graphical Abstract