Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of intrapopulation variability of bald cypress (Taxodium distichum L. Rich.) in a seed stand near Bačka Palanka using morphometric markers

In this paper are presented the results of an analysis of intrapopulation variability using morphometric markers of 20 Bald cypress test trees originating from seed stand S 01.10. 01.01 near Backa Palanka. The morphometric characters of cones (length, width and number of grains) and seedlings (root collar diameter and height of seedling) that were produced by this seed were analyzed. A mutual biological similarity or distance of mother trees depending on the analyzed charac- ters was tested. The determined values of the dimensions of cones and seedlings indicate the good genetic and adaptive potential of this species, which can be a starting point for the mass production of Bald cypress seed and planting materials in Serbia.
Constraining delay time distribution of binary neutron star mergers from host galaxy properties

Gravitational wave (GW) observatories are discovering binary neutron star mergers (BNSMs), and in at least one event we were able to track it down in multiple wavelengths of light, which allowed us to identify the host galaxy. Using a catalogue of local galaxies with inferred star formation histories and adopting a BNSM delay time distribution (DTD) model, we investigate the dependence of BNSM rate on an array of galaxy properties. Compared to the intrinsic property distribution of galaxies, that of BNSM host galaxies is skewed toward galaxies with redder colour, lower specific star formation rate, higher luminosity, and higher stellar mass, reflecting the tendency of higher BNSM rates in more massive galaxies. We introduce a formalism to efficiently make forecast on using host galaxy properties to constrain DTD models. We find comparable constraints from the dependence of BNSM occurrence distribution on galaxy colour, specific star formation rate, and stellar mass, all better than those from dependence on $r$-band luminosity. The tightest constraints come from using individual star formation histories of host galaxies, which reduces the uncertainties on DTD parameters by a factor of three or more. Substantially different DTD models can be differentiated with about 10 BNSM detections. To constrain DTD parameters at 10\% precision level requires about one hundred detections, achievable with GW observations on a decade time scale.
Adaptive Carrier Frequency Offset and Channel Estimation for MIMO-OFDM System

— The issue of Carrier Frequency Offset (CFO) and channel estimation are very important for Multi Input Multi Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM). Previously, the problem of channel estimation and CFO are considered and for that adaptive algorithm is proposed by using this algorithm CFO and channel parameter are estimated at the same time. A multiple model is used in which many sub channel models is constructed. The sub channel and CFO separately estimated so by using this method computational complexity can be reduced. This paper uses the kalman filter and EKF with sinc function for proper synchronization and to achieve a minimized mean square error (MSE) compared to first order and second order method. Finally, Different simulations are shown to prove the performance with compare to other methods.
Let’s Not Make Conclusions About Mandatory Vaccination From Imperfect Regression Analysis

Vaz and colleagues1 used regression analysis to conclude that mandatory vaccination led to increased vaccination coverage and reduced measles incidence in Europe. They compared 21 countries classified without mandatory vaccination and 7 countries classified with: Bulgaria, Czech Republic, Hungary, Latvia, Poland, Slovenia, and Slovakia. The authors found positive association between vaccination coverage and size of monetary fines imposed for not taking children for vaccination. We have several reservations on the methodology and analysis. While the 7 countries have indeed reported high coverage rates for several decades, we do not believe that mandatory vaccination is the primary reason. Instead, strong immunization programs is the major … E-mail: rnandy{at}unicef.org
Unsupervised non-parametric change point detection in electrocardiography

We propose a new unsupervised and non-parametric method to detect change points in electrocardiography. The detection relies on optimal transport theory combined with topological analysis and the bootstrap procedure. The algorithm is designed to detect changes in virtually any harmonic or a partially harmonic signal and is verified on ECG data streams. We successfully find abnormal or irregular cardiac cycles in the waveforms for the six of the most frequent types of clinical arrhythmias using a single algorithm. Our unsupervised approach reaches the level of performance of the supervised state-of-the-art techniques. We provide conceptual justification for the efficiency of the method.
WiTAG: Seamless WiFi Backscatter Communication

WiFi backscatter communication has the potential to enable battery-free sensors which can transmit data using a WiFi network. In order for WiFi backscatter systems to be practical they should be compatible with existing WiFi networks without any hardware or software modifications. Moreover, they should work with networks that use encryption. In this paper, we present WiTAG which achieves these requirements, making the implementation and deployment of WiFi backscatter communication more practical. In contrast with existing systems which utilize the physical layer for backscatter communication, we take a different approach by leveraging features of the MAC layer to communicate. WiTAG is designed to send data by selectively interfering with subframes (MPDUs) in an aggregated frame (A-MPDU). This enables standard compliant communication using modern, open or encrypted 802.11n and 802.11ac networks without requiring hardware or software modifications to any devices. We implement WiTAG using off-the-shelf components and evaluate its performance in line-of-sight and non-line-of-sight scenarios. We show that WiTAG achieves a throughput of up to 4 Kbps without impacting other devices in the network.
Using social media to geo-target emergency management efforts

The ubiquity of social media data has increased their use during emergency management (EM) in near real-time. For instance, during Haiti Earthquake (2010) and Hurricane Harvey (2017), Ushahidi and Twitter were used respectively for emergency response and rescue operations. Nonetheless, social media data tend to contain [ir]relevant information to be useful for disaster analytics for EM efforts. In this study, geo-tagged tweets obtained for 2013 Colorado flooding were analyzed to determine (i) what kind of situational awareness (SA) information could be extracted from tweets for emergency response and (ii) what is the spatio-temporal distribution of such information. The results indicate that tweets generated before September 12th (day of heavy precipitation) were non-relevant, but tweets generated on and following September 12th contained crisis information (about the event and its impacts), warnings, preparatory information. Next phase of this study will focus on developing a framework to integrate SA information with physical risk and social vulnerability to geo-target EM efforts.
Unlicensed taxi detection algorithm based on traffic surveillance data

Unlicensed taxi services severely disrupt traffic management and threaten passengers' safety. Traditional approaches to detect unlicensed taxis rely highly on manual work like collecting on-site evidence. However, these approaches are ineffective and inefficient. As to address this hardship, we propose an unlicensed taxi detection algorithm using pass-records data collected from surveillance cameras. First, based on spatio-temporal analysis, we propose path irregularity and time irregularity to distinguish commercial vehicles from non-commercial vehicles. Then, we evaluate the algorithm based on the actual vehicle pass-records data of Guiyang. The results show that our algorithm outperforms baselines in terms of accuracy and running time.
The Vantage Index: Executing Distance Queries at Scale

Due to the proliferation of GPS-enabled devices, vast amounts of trajectory datasets are being collected every day. Analyzing this data efficiently and at scale is a major challenge. Several different types of spatio-temporal queries are used to analyze these datasets. One important query is the distance query on trajectory data which, given a query distance D, a point P and a time span T, finds all trajectories within D of P during T. This query is frequently used in traffic analysis and numerous other applications. In this paper we develop the means to efficiently and scalably analyse large amounts of trajectory data with the distance query. To this end we develop the means to distribute the trajectory data in a distributed infrastructure (Spark) as well as the index needed on the nodes to answer the query locally. As our experiments show, our approach is more efficient when compared to a baseline method.
Machine Learning Approaches for Cancer Analysis

The transcriptome is the entire set of transcripts that are expressed from the genes of an organism under some conditions and at a particular time. The transcriptome can be sequenced into reads in order to find clues about genes and protein sequences, structures, and their functions. While in the past, transcriptome sequencing technology used to be costly and slow, more recently, next generation sequencing technology has emerged, decreasing the cost and increasing the speed of genome, transcriptome and exome sequencing. However, raw sequences come with artifacts, and hence preprocessing the reads is required for downstream analysis. In this dissertation, we have proven that preprocessing sequencing data is required for better performance throughout the genomics processing. In addition, we propose many machine learning models that serve as contributions to solve a biological problem. First, we present Zseq, a linear time method that identifies the most informative genomic sequences and reduces the number of biased sequences, sequence duplications, and ambiguous nucleotides. Zseq finds the complexity of the sequences by counting the number of unique k-mers in each sequence as its corresponding score and also takes into the account other factors, such as ambiguous nucleotides or high GC-content percentage in k-mers. Based on a z-score threshold, Zseq sweeps through the sequences again and filters those with a z-score less than the user-defined threshold. Zseq is able to provide a better mapping rate; it reduces the number of ambiguous bases significantly in comparison with other methods. Evaluation of the filtered reads has been conducted by aligning the reads and assembling the transcripts using the reference genome as well as de novo assembly. The assembled transcripts show a better discriminative ability to separate cancer and normal samples in comparison with another state-of-the-art method. Studying the abundance of select mRNA species throughout prostate cancer progression may provide some insight into the molecular mechanisms that advance the disease. In the second contribution of this dissertation, we reveal that the combination of proper clustering, distance function and Index validation for clusters are suitable in identifying outlier transcripts, which show different trending than the majority of the transcripts, the trending of the transcript is the abundance throughout different stages of prostate cancer. We compare this model with standard hierarchical time-series clustering method based on Euclidean distance. Using time-series profile hierarchical clustering methods, we identified stage-specific mRNA species termed outlier transcripts that exhibit unique trending patterns as compared to most other transcripts during disease progression. This method is able to identify those outliers rather than finding patterns among
Flow Event Telemetry on Programmable Data Plane

Network performance anomalies (NPAs), e.g. long-tailed latency, bandwidth decline, etc., are increasingly crucial to cloud providers as applications are getting more sensitive to performance. The fundamental difficulty to quickly mitigate NPAs lies in the limitations of state-of-the-art network monitoring solutions --- coarse-grained counters, active probing, or packet telemetry either cannot provide enough insights on flows or incur too much overhead. This paper presents NetSeer, a flow event telemetry (FET) monitor which aims to discover and record all performance-critical data plane events, e.g. packet drops, congestion, path change, and packet pause. NetSeer is efficiently realized on the programmable data plane. It has a high coverage on flow events including inter-switch packet drop/corruption which is critical but also challenging to retrieve the original flow information, with novel intra- and inter-switch event detection algorithms running on data plane; NetSeer also achieves high scalability and accuracy with innovative designs of event aggregation, information compression, and message batching that mainly run on data plane, using switch CPU as complement. NetSeer has been implemented on commodity programmable switches and NICs. With real case studies and extensive experiments, we show NetSeer can reduce NPA mitigation time by 61%-99% with only 0.01% overhead of monitoring traffic.
Freedom for the SQL-Lambda: Just-in-Time-Compiling User-Injected Functions in PostgreSQL

As part of the code-generating database system HyPer, SQL lambda functions allow user-defined metrics to be injected into data mining operators during compile time. Since version 11, PostgreSQL has supported just-in-time compilation with LLVM for expression evaluation. This enables the concept of SQL lambda functions to be transferred to this open-source database system. In this study, we extend PostgreSQL by adding two subquery types for lambda expressions that either pre-materialise the result or return a cursor to request tuples. We demonstrate the usage of these subquery types in conjunction with dedicated table functions for data mining algorithms such as PageRank, k-Means clustering and labelling. Furthermore, we allow four levels of optimisation for query execution, ranging from interpreted function calls to just-in-time-compiled execution. The latter—with some adjustments to the PostgreSQL’s execution engine—transforms our lambda functions into real user-injected code. In our evaluation with the LDBC social network benchmark for PageRank and the Chicago taxi data set for clustering, optimised lambda functions achieved comparable performance to hard-coded implementations and HyPer’s data mining algorithms.
Multi-Task Representation Learning Network for Trajectory Recovery

Trajectory recovery can benefit many applications such as migration pattern studies of animal and finding hot routes in the urban city. It is necessary to recover trajectory with limited trajectory points to utilize collected trajectory data in a reasonable and efficient way and to provide the better location based service for users. However, the trajectory data involves complex and nonlinear spatial-temporal impacts which cannot be captured by traditional trajectory recovery methods. Moreover, the existing methods consider little about the correlations between trajectory and traffic pattern in the urban city. The superiority of deep neural network makes it possible to recover trajectory with low data quality. We propose a Multi-Task Representation Learning Network (MRL-Net) framework which models the complex nonlinear spatial-temporal correlations in trajectory data with representation learning technique and capture the dependencies of trajectory points with recurrent neural networks. To the best of our knowledge, it is the first paper to address the trajectory recovery problem with representation learning and multi-task learning. Experiments on real-world trajectory data show that our model is superior to state-of-the-art methods.
Neural-Enhanced Live Streaming: Improving Live Video Ingest via Online Learning

Live video accounts for a significant volume of today's Internet video. Despite a large number of efforts to enhance user quality of experience (QoE) both at the ingest and distribution side of live video, the fundamental limitations are that streamer's upstream bandwidth and computational capacity limit the quality of experience of thousands of viewers. To overcome this limitation, we design LiveNAS, a new live video ingest framework that enhances the origin stream's quality by leveraging computation at ingest servers. Our ingest server applies neural super-resolution on the original stream, while imposing minimal overhead on ingest clients. LiveNAS employs online learning to maximize the quality gain and dynamically adjusts the resource use to the real-time quality improvement. LiveNAS delivers high-quality live streams up to 4K resolution, outperforming WebRTC by 1.96 dB on average in Peak-Signal-to-Noise-Ratio on real video streams and network traces, which leads to 12%-69% QoE improvement for live stream viewers.
Advanced metering infrastructures: security risks and mitigation

Energy providers are moving to the smart meter era, encouraging consumers to install, free of charge, these devices in their homes, automating consumption readings submission and making consumers life easier. However, the increased deployment of such smart devices brings a lot of security and privacy risks. In order to overcome such risks, Intrusion Detection Systems are presented as pertinent tools that can provide network-level protection for smart devices deployed in home environments. In this context, this paper is exploring the problems of Advanced Metering Infrastructures (AMI) and proposing a novel Machine Learning (ML) Intrusion Prevention System (IPS) to get optimal decisions based on a variety of factors and graphical security models able to tackle zero-day attacks.
OmniMon: Re-architecting Network Telemetry with Resource Efficiency and Full Accuracy

Network telemetry is essential for administrators to monitor massive data traffic in a network-wide manner. Existing telemetry solutions often face the dilemma between resource efficiency (i.e., low CPU, memory, and bandwidth overhead) and full accuracy (i.e., error-free and holistic measurement). We break this dilemma via a network-wide architectural design OmniMon, which simultaneously achieves resource efficiency and full accuracy in flow-level telemetry for large-scale data centers. OmniMon carefully coordinates the collaboration among different types of entities in the whole network to execute telemetry operations, such that the resource constraints of each entity are satisfied without compromising full accuracy. It further addresses consistency in network-wide epoch synchronization and accountability in error-free packet loss inference. We prototype OmniMon in DPDK and P4. Testbed experiments on commodity servers and Tofino switches demonstrate the effectiveness of OmniMon over state-of-the-art telemetry designs.
DIDEROT: an intrusion detection and prevention system for DNP3-based SCADA systems

In this paper, an Intrusion Detection and Prevention System (IDPS) for the Distributed Network Protocol 3 (DNP3) Supervisory Control and Data Acquisition (SCADA) systems is presented. The proposed IDPS is called DIDEROT (Dnp3 Intrusion DetEction pReventiOn sysTem) and relies on both supervised Machine Learning (ML) and unsupervised/outlier ML detection models capable of discriminating whether a DNP3 network flow is related to a particular DNP3 cyberattack or anomaly. First, the supervised ML detection model is applied, trying to identify whether a DNP3 network flow is related to a specific DNP3 cyberattack. If the corresponding network flow is detected as normal, then the unsupervised/outlier ML anomaly detection model is activated, seeking to recognise the presence of a possible anomaly. Based on the DIDEROT detection results, the Software Defined Networking (SDN) technology is adopted in order to mitigate timely the corresponding DNP3 cyberattacks and anomalies. The performance of DIDEROT is demonstrated using real data originating from a substation environment.
Behaviour of calves in the first weeks of life

Behavioural observations were conducted on 20 calves (10 bull calves and 10 heifer calves). The first series of observations was made in the first 4 days of life, and the second after a month (within 30 to 33 days of calving). Time from birth to first unassisted standing (standing on all extended legs) ranged from 16 minutes (heifers) to 158 minutes (bulls) for normal calving. Calves born from normal parturitions stood up after an average of 81 minutes and those born from difficult parturitions after 302 minutes. Calves that required assistance at calving showed shorter standing and longer lying time. In the first 4 days of life and after a month, both male and female calves were more often in left lateral recumbency when resting. Heifer calves more often visited the feeder while bull calves more often consumed concentrates, water and hay.
Formalising fault injection and countermeasures

Fault injection is widely used as a method to evaluate the robustness and security of a system against many kinds of faults and attacks. Recent works have considered many ways to demonstrate security risks and viable attacks using fault injection, and some have also proposed countermeasures. However, no general and formal definition of fault injection or countermeasure has been provided that can be used to reason about such attacks. This leaves significant results in this area to be ad-hoc and without broad applicability. This paper presents formal definitions of both fault injection on an arbitrary system and what an effective countermeasure is. These definitions are used to prove that fault injection attacks cannot in general be prevented (by any countermeasure). An example is presented that demonstrates how to construct an effective countermeasure for a specific fault injection that parallels some well known approaches. Further extensions to account for probabilistic behaviour and systems with time are also presented. These definitions and results demonstrate formal proofs about the security and defences of systems in ways that can be used, thus yielding a broadly applicable approach that can formalise fault injections and countermeasures in the future.
An Approach of Predicting Heart Disease Using a Hybrid Neural Network and Decision Tree

Heart disease is one of the leading cause deaths of worldwide. Prediction of cardiovascular infection is a critical challenge in the area of clinical data analysis. Data mining techniques are providing an effective decision and significant results on data that are used widely for predicting. The purpose of this paper is to propose a novel approach with aims to find a noteworthy method to diagnose heart disease prediction. In this research, a unique dataset was created by combining the Cleveland dataset and Stalog heart disease datasets collected from the UCI ML repository. The new dataset contains 14 medical parameters such as age, sex, blood pressure, and 568 instances for training and prediction heart disease. This paper offers a novel methodology of NNDT (Neural Network and Decision Tree) that uses Neural Network for training model and Decision Tree to test classification for better heart disease prediction. The performance of the proposed approach have been compared with Naïve Bayes, Support Vector Machine, Neural Network, Voted Perceptron, and Decision Tree algorithms. The results showed that the accuracy and performance improved as compared to other techniques and methods. This study enables the researchers to analyze the heart disease data with a new approach to predict heart diseases to maintain human health.