Miracle-dz/UniFAR-FTUs huggingface dataset:
Correcting genomic deletion calls with complex boundaries from next generation sequencing data

Along with tumor growth, somatic alternations are continually accumulating, some of which leads to the formations of clonal populations. Genomic deletion is a major type of such genomic alternations. Although tens of computational methods were published, in the past decade, for detecting genomic deletions from next generation sequencing data, the existing algorithms often suffer an accuracy loss when they encounter the cases of deletion calls with complex boundaries. It is reported that a genomic deletion that occurs in different sub-clones may present nearby boundaries. Such deletion is considered as a deletion with complex boundaries. The existing approaches either ignore the complex-boundary cases by reporting the pair of boundaries with the largest numbers of supporting reads, or even provide incorrect results due to the interference data signals. To overcome this weakness, in this paper, we propose a heuristic method, SV-Del, to help the popular methods correct the detection errors, which are introduced by complex boundaries. The results of an existing method are the given candidate calls. SV-Del filters these calls and identifies the ones with complex boundaries. The proposed method first adopts a segmented extension algorithm and utilizes the longest variable splitting-read strategy to detect the possible pairs of boundaries in each candidate region. Then, it uses the longest variable splitting-reads to correct the detection errors which may introduced by clonal SNVs. To differentiate the detection errors from possible pairs of deletion boundaries, SV-Del estimates the numbers of sub-clones across sampled candidate regions, and then it uses a gradually separating algorithm to attain and refine the candidate calls. We applied SV-Del on a series of simulated datasets which are generated by different settings. The experiment results demonstrate that the detection accuracy is significantly improved comparing to the original results. SV-Del is also shown robust. The source codes and software package of SV-Del are uploaded at https://github.com/Hope523/SV-Del for academic uses only.
Efficient Dynamic Time Warping for Big Data Streams

Many common data analysis and machine learning algorithms for time series, such as classification, clustering, or dimensionality reduction, require a distance measurement between pairs of time series in order to determine their similarity. A variety of measures can be found in the literature, each with their own strengths and weaknesses, but the Dynamic Time Warping (DTW) distance measure has occupied an important place since its early applications for the analysis and recognition of spoken word. The main disadvantage of the DTW algorithm is, however, its quadratic time and space complexity, which limits its practical use to relatively small time series. This issue is even more problematic when dealing with streaming time series that are continuously updated, since the analysis must be re-executed regularly and with strict running time constraints. In this paper, we describe enhancements to the DTW algorithm that allow it to be used efficiently in a streaming scenario by supporting an append operation for new time steps with a linear complexity when an exact, error-free DTW is needed, and even better performance when either a Sakoe-Chiba band is used, or when a sliding window is the desired range for the data. Our experiments with one synthetic and four natural data sets have shown that it outperforms other DTW implementations and the potential errors are, in general, much lower than another state-of-the-art approximated DTW technique.
Improving Tor Hidden Service Crawler Performance

Tor is a popular anonymous network that provides the dark web called the hidden service not accessible with traditional web browsers. The number of hidden services using Tor's anonymous network has been steadily increasing every year with the demand of user privacy concerns as well as illegal services that abuse Tor's anonymity. As a result, how to collect and analyze the contents of Tor hidden service is not yet in full swing. In addition, due to the slow access speed of the Tor browser, it is difficult to observe the dynamics of the hidden services. In this work, we present an efficient Tor hidden service crawling method that can monitor the status of hidden services for the analysis of authentic hidden service contents. We have designed our Tor crawler with the virtualization software, Docker, and parallel Tor browsers to improve the crawling performance. In order to maximize the utilization, we analyzed the collected Tor hidden services by a clustering technique and reduced the crawling targets. Crawling time was 76% improved with 5 Docker containers with 5 browsers. From clustering same hidden services, we can also reduce 39% of crawling the Tor hidden services. From January 1, 2018 to May 31, 2018, we collected 25,261 onion addresses and found 2,527 unique hidden services. We crawled this hidden services twice a day, collect 456,739 HTML pages. We classified 722 hidden services contents and analyzed the Tor hidden service dynamics through collected HTML pages and crawler's log data. As a result, we found that the percentage of illegal services is higher in hidden services, which is actually in service.
A New Human Eye Tracking Method Based on Tracking Module Feedback TLD Algorithm

Existing human eye tracking research is based on the off-line sample training cascade classifier and the traditional tracking algorithm. However, it is difficult to adapt to situations where human eye is partially blocked, gets morphological changes or scale changes and so on. In order to solve these problems, the tracking-learning-detection algorithm for online single-target long-term tracking is used and improved. A novel eye tracking method—the human eye tracking-learning-detection algorithm with tracking feedback is proposed. The detection area is adjusted adaptively and narrowed by the tracking feedback. Above problems are solved, the interference of similar targets is avoided, and the speed of human eye tracking is enhanced. The experimental results show that the algorithm has high tracking accuracy and frames per second.
Linear Models with Many Cores and CPUs: A Stochastic Atomic Update Scheme

Linear models are fast to train, apply, and still state of the art for sparse and high dimensional problems. Their computational efficiency makes them difficult to parallelize, with the standard multi-core approaches often diverging after more than 8 cores are added. We propose a Stochastic Atomic Update Scheme (SAUS) for training linear models on many core machines. It is simple to implement, reduces the number of divergent cases, and obtains greater speedups by being able to effectively use an 80-core server.
Distributed NoSQL Data Stores: Performance Analysis and a Case Study

NoSQL data-stores are commonly used to provide flexibility and availability for big data handling. However, there is a lack of comprehensive studies about which NoSQL data-store performs the best from the two scalability aspects, (scale-up, and scale-out), in a distributed and parallel processing environment. This paper compares the popular NoSQL data-stores (Cassandra, HBase, and MongoDB) and analyzes the resulting performance. Our experiments measure throughput, latency, and run-time of the evaluated data-stores on a big data set that consist of standard benchmarking workloads. Our results provide that the performance of each NoSQL data-store varies according to two main factors, (a) the type of executed operation, (read, scan, update, write, and insert), and (b) the level of distribution.
Exact Phase Retrieval by Least-Squares Optimization

The concerned phase retrieval problem is to estimate a vector from a number of magnitude (phaseless) measurements. Due to the non-convexity of the phase retrieval problem, the semi-definite relaxation technique is usually adopted, which enables the non-convex phase retrieval problem to be addressed by semi-definite constrained optimization. Following the spirit of the classic system identification, this paper shows that the phase retrieval problem can be addressed by solving a leastsquares optimization problem only, without the semi-definite constraint. A sufficient condition for the exact phase retrieval is provided, which is analogous to the signal persistent excitation in the system identification field. In view of the connection between the phase-retrieval problem and the Wiener system identification problem, the provided solution is extended to solve the Wiener system identification problem with an absolute operator at the system output. Finally, the effectiveness of the presented algorithm is demonstrated by numerical simulations.
Postfault LVRT Performance Enhancement of DFIG Using a Stage-Controlled SSFCL-RSDR

To enhance the dynamic low-voltage ride-through (LVRT) performance of a doubly fed induction generator (DFIG) based wind turbine during the postfault stage, a novel LVRT scheme is proposed by integrating a stator-side superconducting fault current limiter (SSFCL) with a two-step rotor-side series dynamic resistor (RSDR). The basic principle, circuit modeling, and switching strategy of the SSFCL and RSDR are presented to form the primary and backup protection schemes. As compared to conventional SSFCL-based LVRT scheme, the results obtained in the proposed SSFCL-RSDR-based scheme show that the amplitudes of electromagnetic transient oscillations are largely reduced with shorter recovery time. This enhances significantly the internal and external LVRT behaviors in the DFIG during and after the fault simultaneously.
DisPatch: Distributed Pattern Matching over Streaming Time Series

Matching a dictionary of patterns (i.e. subsequences) against a streaming time series to identify occurrences is one of the primary components of real-time monitoring systems such as earthquake monitoring, power consumption monitoring, and patient monitoring. These domains critically depend on timely alarms immediately after events (i.e. earthquake, fire, seizure, etc.) start. Until now, the problem has been solved independently by smart pruning, efficient approximation, and pattern indexing without bounding the delay between pattern occurrence and detection time. Moreover, complexity of the dictionary matching problem is quickly growing with larger dictionary sizes, faster data streams, and stricter delay requirements; pushing existing pattern matching systems to their limits. In this paper, we describe a robust distributed matching system, called DisPatch (Distributed Pattern Matching), that matches a pattern with a guaranteed maximum delay after the pattern appears in the stream. We develop and evaluate a novel distribution strategy and integrate state-of-the-art algorithmic optimization techniques to horizontally scale to a high data rate and a large dictionary size. We show three use cases of DisPatch in seismic, patient and power consumption monitoring.
Exploring Methods for Comparing Similarity of Dimensionally Inconsistent Multivariate Numerical Data

When developing multivariate data classification and clustering methodologies for data mining, it is clear that most literature contributions only really consider data that contain consistently the same attributes. There are however many cases in current big data analytics applications where for same topic and even same source data sets there are differing attributes being measured, for a multitude of reasons (whether the specific design of an experiment or poor data quality and consistency). We define this class of data a dimensionally inconsistent multivariate data, a topic that can be considered a subclass of the Big Data Variety research. This paper explores some classification methodologies commonly used in multivariate classification and clustering tasks and considers how these traditional methodologies could be adapted to compare dimensionally inconsistent data sets. The study focuses on adapting two similarity measures: Robinson-Foulds tree distance metrics and Variation of Information; for comparing clustering of hierarchical cluster algorithms (such clusters are derived from the raw multivariate data). The results from experiments on engineering data highlight that adapting pairwise measures to exclude non-common attributes from the traditional distance metrics may not be the best method of classification. We suggest that more specialised metrics of similarity are required to address challenges presented by dimensionally inconsistent multivariate data, with specific applications for big engineering data analytics
Cross-topic Rumour Detection in the Health Domain

Nowadays information diffusion has become more and more immediate and fast thanks to social media and its services. However, lack of controls and moderation in resources as social microblogs often leads to spread unverified information, such as rumours, which can become a threat to the society. To improve life quality and good information diffusion, various automatic systems have been studied for rumour detection in microblogs at level of aggregation of posts, whereas a few effort has been tried to the most challenging scenario where the rumour has to be recognized at level of each single post. In this work, we direct our efforts towards individual post rumour detection: we investigate how features describing influence potential, personal interest and network characteristic perform on two different datasets of posts collected from Twitter using two different health-related keywords. As a further contribution, we study what happens in cross-topic tests, i.e. when the rumour detection system is trained with posts with an hashtag and tested on samples with a different one.
Influence Maximization in Evolving Multi-Campaign Environments

Online Social Networks (OSNs) are being extensively used in a variety of campaigns, with the objective to raise the awareness of the audience regarding a specific piece of information, e.g, product awareness, political positions, etc. An important characteristic is the correlations of different strength and types, i.e., either positive or negative, that appear among the plethora of information diffused in the network. Therefore, the breadth of the diffusion of a specific campaign is significantly affected by the spread of the correlated information. In addition to the correlations of the diffusing information, another important factor affecting the spread of a specific information, is the network connectivity. Social networks are constantly evolving, with links among users being continuously formed or discontinued and communication patterns being highly fluctuating. The aim of this work is to address the problem of Influence Maximization in Online Social Networks by bridging the gap of the existing literature regarding the plethora of correlated information simultaneously propagating in the network and the dynamic communication patterns observed among users in the network. Towards this, we propose a mechanism that estimates users’ interaction probabilities and formulate a propagation model that captures how the exposure to many correlated campaigns impacts on the users’ behavior to support any of them. We finally design a greedy seed selection approach that approximates the optimal solution at a ratio of 1 − 1/e and prove through extensive experimental evaluation the superiority of our approach compared to state-of-the-art approaches.
A Predictor-based ADRC for input delay systems subject to unknown disturbances

This paper presents a predictor-based active disturbance rejection control (PADRC) scheme for input delay systems. In the proposed control scheme, the extended state observer (ESO) is modified by delaying the control signal to compensate the time delay from the observation loop. Moreover, a predictor is designed in time domain to obtain estimated states for current time and cancel the effects of input delay on the closed-loop system, where the approximation of disturbances is considered for corrective prediction purposes. Furthermore, a PD controller is designed to compensate disturbances and control the system. Then, the stability of the closed-loop system is analyzed by the Lyapunov function method. Finally, the effectiveness of the PADRC is demonstrated by simulations.
Improving Word Representation by Tuning Word2Vec Parameters with Deep Learning Model

Deep learning has become one of the most popular machine learning methods. The success in the text processing, analysis and classification has been significantly enhanced by using deep learning. This success is contributed by the quality of the word representations. TFIDF, FastText, Glove and Word2Vec are used for the word representation. In this work, we aimed to improve word representations by tuning Word2Vec parameters. The success of the word representations was measured by using a deep learning classification model. The minimum word count, vector size and window size parameters of Word2Vec were used for the measurement. 2,8 million Turkish texts consisting of 243 million words to create word embedding (word representations) and around 263 thousand documents consisting of 15 different classes for classification were used. We observed that correctly selected parameters increased the word representation quality and thus the accuracy of classification.
Sparse FIR Filter Design using Iterative MOCSA

In this paper, sparse FIR digital filter design using iterative multiobjective cuckoo search algorithm (MOCSA) is presented. The least-squares frequency response error and the l1 norm of selected non-zero filter coefficients of a sparse digital filter are jointly minimized by iterative multiobjective cuckoo search algorithm until convergence. After convergence, a final round of single-objective cuckoo search is performed to minimize the least-square frequency response error of the sparse digital filter to arrive at an optimal solution. With the same set of sparsity levels, the sparse digital filter design results obtained by the iterative MOCSA indicates that smaller weighted least-squares frequency response errors can be obtained as compared to those of another optimization method.
Flow-Level Traffic Engineering in Conventional Networks with Hop-by-Hop Routing

A fine-grained traffic engineering (TE) that enables per-flow control is considered to be necessary in future Internet. In this paper, we study to realize flow-level TE in conventional networks, where hop-by-hop routing is available, and advanced technologies such as SDN and MPLS are not deployed. Based on analysis and modelling on real Internet traffic, we propose to detect and schedule a few large flows in real time, which dominate the traffic amount. The proposed scheme leverages advanced algorithms for detection, computes the rerouting paths in a centralized server, uses extended OSPF to distribute the routing, and uses a few ACL entries for flow-level forwarding. We formalize the link weight assignment-based large flow scheduling problem and prove that the problem is NP-hard. We develop algorithms to compute the routing and reduce extra LSA number required. We present a set of theoretical results on the TE performance bounds when the number of large flows varies. Experiment and simulation results show that our scheme can reroute large flows within 0.5 second, and the maximum link utilization is within 102% of the optimal solution for source and destination addresses-based flows, while the extra LSA number is small.
Analysis and Correction of Combined Channel Mismatch Effects in Frequency-Interleaved ADCs

In this paper, a comprehensive system behavioral model of frequency-interleaved analog-to-digital converters (FI-ADCs) is presented. The model addresses mismatch errors due to imperfect channel separation, incomplete anti-aliasing, in-phase/quadrature branch imbalances, and jitter-induced distortions. Using this model, an integrated analysis of combined channel mismatch effects is provided. Given error parameters or their distributions, closed-form expressions of the expected output signal-to-noise ratio and image rejection ratio are formulated for FI-ADCs with an arbitrary number of channels. The inherent circuit behaviors, sampling clock jitters, and local oscillator phase noise effects are also discussed. Based on the analysis and modeling, a discrete-time equivalent model for FI-ADC systems is derived. Therefrom, an integrated channel mismatch compensation scheme is proposed that contrasts existing signal recovery approaches which calibrate specific types of errors separately. The performance of this novel method is compared with conventional solutions using extensive simulations. Very favorable numerical results are observed.
Effect of conductivity on the thermal and electrical properties of the stress grading system of an inverter-fed rotating machine

The thermal and electrical characteristics of a stress grading system are studied by simulations and measurements of the electric field and temperature distributions in the overhang region of a form wound medium voltage motor coil. Simulations show that a highly conductive stress grading tape (SGT) reduces the maximum electric field in this region but increases heat production and temperature. In contrast, a highly conductive armor tape (CAT) decreases the maximum electric field in this region and also reduces the heat production and temperature in the stress grading system but also produces a slight increase in the electric field in the SGT region. For the simulation studies, accurate measurements of the electrical and thermal conductivities of materials are made, taking into account the effects of the vacuum pressure impregnation (VPI) process, temperature, and tape builds. The electrical conductivity of single and double half-lap layers of CAT and SGT are measured from 30 to 100 °C. For the evaluation of the simulation model, the temperature profile along the stress grading system was measured and simulated under pulsed voltage at room and near typical operating temperatures.
Improved Polar SCL Decoding by Exploiting the Error Correction Capability of CRC

Novel successive cancellation list (SCL) decoding schemes of Polar codes by exploiting the error correction (EC) capability of the cyclic redundancy check (CRC) codes are proposed in this paper. Different from the schemes in the literature that only considered the error detection capability of CRC, the proposed schemes aim to improve the performance of CRC-aided Polar decoding to the most by benefiting from the potential EC capability of CRC. Specifically, we first propose a basic decoding scheme, named CRC error-correction-aided successive cancellation list (CRC-EC-SCL) decoding, by introducing the look-up-table-based CRC EC method into the Polar SCL decoding process. Then, based on the basic scheme, we further propose the segmented-CRC-EC-SCL decoding scheme. By segmented-CRC, the information block is divided into multiple segments and each segment is attached with a shorter CRC sequence. By conducting the CRC-EC-aided coding for each of the segments, multiple erroneous bits can be corrected early and error propagation can, thus, be efficiently suppressed. The simulation results show that both of the proposed decoding schemes have a significant performance gain over the classic CRC-EC-SCL decoding scheme. Especially for the segmented-CRC-EC-SCL, the performance gain at a frame error rate of 10−4 is about 0.4 dB. Moreover, the average computation complexity of the segmented-CRC-EC-SCL is close to the classic scheme.