Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust and Rapid Clustering of KPIs for Large-Scale Anomaly Detection

For large Internet companies, it is very important to monitor a large number of KPIs (Key Performance Indicators) and detect anomalies to ensure the service quality and reliability. However, large-scale anomaly detection on millions of KPIs is very challenging due to the large overhead of model selection, parameter tuning, model training, or labeling. In this paper we argue that KPI clustering can help: we can cluster millions of KPIs into a small number of clusters and then select and train model on a per-cluster basis. However, KPI clustering faces new challenges that are not present in classic time series clustering: KPIs are typically much longer than other time series, and noises, anomalies, phase shifts and amplitude differences often change the shape of KPIs and mislead the clustering algorithm. To tackle the above challenges, in this paper we propose a robust and rapid KPI clustering algorithm, ROCKA. It consists of four steps: preprocessing, baseline extraction, clustering and assignment. These techniques help group KPIs according to their underlying shapes with high accuracy and efficiency. Our evaluation using real-world KPIs shows that ROCKA gets F-score higher than 0.85, and reduces model training time of a state-of-the-art anomaly detection algorithm by 90%, with only 15% performance loss.
GAIDR: An Efficient Time Series Subsets Retrieval Method for Geo-Distributed Astronomical Data

Time series subsets retrieval is an important problem in astronomical image data analysis, and the original astronomical files are usually stored in geo-distributed data centers. Current data retrieval approach consists of three manual steps, i.e., retrieving original astronomical files, ordering filtered files and obtaining target sub-files. Besides, astronomers need to download or copy entire datasets to their local machines from different data centers before the first step. The retrieval method can only work for a small amount of data. However, astronomical images are big data, indicating that it is time-consuming to process the massive geo-distributed data and requires a significant amount of manual work. In this paper, we propose Geo-Distributed Astronomy Image Data Retrieval (GAIDR), an automatic and efficient subsets retrieval method for astronomical time series data in a geo-distributed environment, and implement a data retrieval system based on it. The retrieval system automatically inquires and returns the target astronomical sub-files by using indexes of astronomical files, including original files index and replica index. Moreover, to further accelerate the retrieval of astronomical time series subsets, a replica strategy is designed in GAIDR method. The replica strategy can be utilized to reduce the size of replica files and merge the related files via replica coordinate-mapping algorithm. It recognizes the hot replica files on the basis of history requests and optimizes replica files data layout according to the temporal and spatial characteristic of data access in time domain astronomy research. Experimental results show that GAIDR method can achieve the highest replica hit ratio among all test methods, and the average response time can be reduced by at least 14.07% comparing with other methods.
ProbPFP: A Multiple Sequence Alignment Algorithm Combining Partition Function and Hidden Markov Model with Particle Swarm Optimization

The substitution score for pairwise sequence alignment is essential in conducting multiple sequence alignment (MSA). The Hidden Markov Model (HMM) and partition function are two methods that are widely chosen for this purpose. Recent studies showed that the accuracy of alignment could be improved by combining the partition function and HMM algorithms or optimizing the parameters of HMM. However, the combination of optimized HMM and partition function, which could greatly improve the accuracy of alignment, was ignored in these studies. This study presents a new MSA algorithm known as ProbPFP that combines the partition function and the HMM optimized by particle swarm optimization (PSO). In this work, the parameters of HMM were first optimized by the PSO algorithm, and the posterior probabilities derived from the HMM were subsequently combined with the results derived from the partition function to compute a comprehensive substitution score for alignment. To assess the effectiveness, ProbPFP was compared with 13 leading aligners, namely, Probalign, CONTRAlign, ProbCons, MUSCLE, MAFFT, COBALT, T-Coffee, ClustalΩ, ClustalW, DIALIGN, PicXAA, Align-m and KALIGN2. The results showed that ProbPFP achieved the highest average sum-of-pairs (SP) scores (0.9015, 0.5984) and average total column (TC) scores (0.8170, 0.3956) on two benchmark sets OXBench and SABmark, as well as the second highest average SP score (0.8250) and average TC score (0.6703) on the benchmark set BAliBASE. We also used the alignments generated by ProbPFP and 4 other leading aligners to rebuild the phylogenetic trees of 6 families from the TreeFam database. The result suggests that the trees from the alignments generated by ProbPFP are closer to the reference trees.
Anchor Selection for Localization in Large Indoor Venues

Many indoor localization systems rely on a set of reference anchors with known positions. A target's location is estimated from a set of distances between the target and its surrounding anchors, and hence the selection of anchors affects the localization accuracy. However, it remains a challenge to select the best set of anchors. In this paper, we study how to appropriately make use of the surrounding anchors for localizing a target. We first construct different candidate anchor clusters by selecting different number of anchors with the strongest received signals. Then for each candidate cluster, we propose a weighted min-max algorithm to provide a location estimation. Finally, we introduce a weighted geometric dilution of precision (w-GDOP) algorithm that combines the estimations from multiple clusters by quantifying their estimation accuracy. We evaluate the performance of our solution through simulations and real-world experiments. Our results show that the proposed anchor selection scheme and localization algorithm significantly improve the localization accuracy in large indoor environments.
Application of LSTM Neural Network for Urban Road Diseases Trend Forecasting

Road disease detection is an important aspect of road management. Data shows that nearly all regions have invested a lot of money and manpower in road management such as road disease detection and road repair. In order to economize on the use of funds, industry and academia have been exploring many new road exploration techniques. The gradual implementation of these technologies has accumulated a large amount of road disease data. This data give us the impression that road management can reduce the investment in road disease detection through reasonable prediction of road diseases. With this large amount of data, we try to use the Long Short Term Memory (LSTM) algorithm in Neural Network as well as time series model to predict the amount of road diseases in the future. LSTM is a special kind of cyclic neural network with long-term dependence on learning. It is able to store information for quite a long time and overcome the problem of gradients that disappear. By using the time series method and the LSTM method on the training and test sets to test the existing data on 421 roads in Beijing, China, we find that the results of the LSTM model are better than that of the time series method under most circumstances.
Cluster-Based Cache Placement in 5G Network

In this paper, we present a cache placement scheme to deal with bandwidth constraint of backhaul links in 5G Small Cell wireless network. We formulated the cache placement problem as graph matching problem and presented an optimal file-helper matching algorithm based on Gale-Shapley optimal matching criterion. We define stability criterion for the matching and found our matching solution stable in the sense that every helper finds at least one file to cache given no file exceed minimum cache size. We derived file-helper based on our framework that employs users' connectivity and their social interaction pattern. Our approach is to perform unique placement of a file within a cluster of helpers to increase the number of files cached within a cluster. We showed through simulation that our algorithm increases the number of file requests served by either a local helper or a helper within the cluster that significantly reduces bottleneck backhaul traffic.
Towards the Optimization of a Ticket-based Authentication Scheme for VANETs

Vehicular Ad hoc NETworks (VANETs) mainly intend to increase road safety by exchanging safety related messages. In order to provide a secure communication in VANETs, a key requirement is to enable receivers to authenticate received messages while preserving the privacy of the identities of sending vehicles. However, if a misbehavior takes place, involved vehicles should be identified and evicted from the network. To this end, we proposed in a previous work [1] a ticket-based authentication scheme for VANETs preserving privacy, in which vehicles use temporary tickets to communicate with other vehicles in the network while conditionally maintaining their privacy. A vehicle’s ticket is formed through two stages: an offline stage and an online stage. Besides, the ticket should be updated whenever its vehicle enters into a new domain (containing few Road Side Units), and modified whenever its validity period expires. In this paper, we propose an optimization of that previously proposed work in order to decrease the cryptographic delay. In fact, the Identity Based Online/Offline Signature (IBOOS) technique and Shamir’s trick are introduced, already verified tickets are stored by receiving vehicles for reference later and the signature size is reduced.
Indoor Location Learning Over Wireless Fingerprinting System With Particle Markov Chain Model

This paper describes research toward a tracking system for locating persons indoor based on low-cost Bluetooth Low Energy (BLE) beacons. Wireless fingerprinting based on BLE beacons has emerged as an increasingly popular solution for fine-grained indoor localization. Inspired by the idea of mobility tracking used in the cellular network, this paper proposes a BLE-based tracking system, designated as BTrack, to learn the location area (LA) of an indoor user based on the reported wireless fingerprinting combined with statistical analysis. We propose a new particle Markov chain model to evaluate the LA-level performance regarding the visibility area in an environment with large obstacles. In the presence of sight obstructions, BTrack is evaluated using a real-world test bed built in a library with tall bookshelves. The performance of the proposed system is evaluated in terms of the mean distance error and the LA prediction accuracy considering the direct line-of-sight. Compared with the existing methods, BTrack reduces the average localization error by 25% and improves the average prediction accuracy by more than 16% given a random mobility pattern.
TADeus-a tool for clinical interpretation of structural variants modifying chromatin organization

Chromosomal rearrangements spanning non-coding regions may convey an abnormal phenotype by various position effects. Alterations in chromosome structure can disrupt interactions between gene promoters and their regulatory elements or place genes in an inappropriate chromosomal domain. Evaluation of the impact of such genomic aberrations on gene expression has been often neglected, especially when other structural variants (SVs) affecting genes are detected. Another reason for not considering such rearrangements has been the lack of appropriate methods or tools to visualize and prioritize the neighboring genes according to their clinical relevance.To address these challenges, we developed TADeus, an easy-to-use open-source web application dedicated for clinical evaluation of variants in genes whose expression may be affected by chromosomal rearrangements. The main functionality of TADeus provides a view of regulatory landscape of regions surrounding breakpoints of the rearrangements by combing chromatin conformation data (Hi-C) with other omics data. TADeus prioritizes genes using number of interrupted enhancer-promoter interactions, the distance from the breakpoints, the phenotypic data, and haploinsufficiency scores. In addition, TADeus can serve as a genomic browser that shows Hi-C matrices with one-dimensional genomic data and allows users to compare Hi-C data from different tissues.
Disambiguating Affective Stimulus Associations for Robot Perception and Dialogue

Effectively recognising and applying emotions to interactions is a highly desirable trait for social robots. Implicitly understanding how subjects experience different kinds of actions and objects in the world is crucial for natural HRI interactions, with the possibility to perform positive actions and avoid negative actions. In this paper, we utilize the NICO robot's appearance and capabilities to give the NICO the ability to model a coherent affective association between a perceived auditory stimulus and a temporally asynchronous emotion expression. This is done by combining evaluations of emotional valence from vision and language. NICO uses this information to make decisions about when to extend conversations in order to accrue more affective information if the representation of the association is not coherent. Our primary contribution is providing a NICO robot with the ability to learn the affective associations between a perceived auditory stimulus and an emotional expression. NICO is able to do this for both individual subjects and specific stimuli, with the aid of an emotion-driven dialogue system that rectifies emotional expression incoherences. The robot is then able to use this information to determine a subject's enjoyment of perceived auditory stimuli in a real HRI scenario.
Distributed Constrained Optimization Using a Projected Differential Inclusion

This paper designs a distributed algorithm for large-scale constrained optimization problems, where the agents cooperate with each other based on local information and communication information. The objective functions may be nonmsooth and the variable is subject to set constraints. This paper proposes a distributed continuous-time solver based on stability theory and optimization theory. The designed projected differential inclusion has solutions even though it is nonconvex and its convergence to one of solutions to the optimization problem is proved rigorously. The projected differential design may also be applied to other more general optimization problems and the methodology is illustrated through a numerical simulation.
A Novel Tour Planning Model using Big Data

Nowadays, many people travel around the world to visit attractions places which have tremendous economic impact on the countries. Tour planning is one of the most beneficial areas in tourism, but the production of tourism tools and tour planning process are not easy tasks. The purpose of the tour planning tools is to increase the satisfaction of tourists, which is provided with a high number of visits and reduced time between visits and travel costs. In this paper, a novel model is proposed for tour planning based on user demands and considering important parameters such as tour cost, traffic volume, weather and time of tour in the city of tourist destination. This model generates various tour plans using big data which comprise traffic data, weather data, events data, tourism data and tourist profile. It offers the best plan according to user priorities and constraints. The main purpose of this model is to select the points of interest by the user, and to arrange the visits based on the knowledge obtained from the big data of the tour planning.
Affective Natural Language Generation by Phrase Insertion

We propose a highly precise, production-ready neural model for affective natural language generation. It is designed to add predefined sentiment to neutral utterances without changing the meaning significantly. It works by inferring phrases and their insertion points. In our work we also propose strict correctness criteria and apply them to our inference results achieving human-level precision.The model is not specific to any particular domain like IoT or restaurants review. We use six selected emotion categories, but we also speculate that the model could be applied to other affective categories, like informal style or politeness, without a design change.
Is Re-ranking Useful for Open-set Person Re-identification?

Re-ranking algorithms can often boost the performance of close-set person re-identification. However, limited efforts have been devoted to answering whether a similar conclusion could be derived on open-set person re-identification. Considering that open-set scenario is more practical in real applications, in this paper, we try to answer this question and do a benchmark study of re-ranking on open-set person re-identification. Specifically, we evaluate three feature descriptors, namely MB-LBP, LOMO, and IDE, and four distance metrics, namely Euclidean, Cosine, RRDA, and XQDA, with their combinations as baseline algorithms. Then, we evaluate four popular re-ranking algorithms, including k-reciprocal Encoding, ECN-3, ECN-4, and DaF. Through extensive benchmark studies on the OPeRIDv1.0 dataset, the results show that re-ranking algorithms, though useful for closed-set person re-identification, are not generally effective for the open-set person re-identification problem. We argue that this is because re-ranking algorithms change the score distributions per query, and hence disrupt the FAR estimation across all queries. Accordingly, we propose to align the re-ranking scores to the original score via the min-max normalization, which verifies our hypothesis above.
Octave Bandwidth Doherty Power Amplifier Using Multiple Resonance Circuit for the Peaking Amplifier

This paper presents a broadband Doherty power amplifier (DPA) design with an octave bandwidth based on a new load network consisting of a quasi-lumped impedance transformer for the carrier amplifier, a multiple resonance circuit for the peaking amplifier, and a broadband post-matching network. The quasi-lumped impedance transformer and the multiple resonance circuit were designed based on accurate equivalent circuits for internal components inside the packaged transistor. Based on power bandwidth analysis, optimum susceptance provided by the multiple resonance circuit at the peaking amplifier, was obtained. The proposed broadband DPA was designed using 45 W gallium–nitride high electron mobility transistor for both carrier and peaking amplifiers. For continuous-wave signals in frequency range of 0.9 to 1.8 GHz, the implemented broadband DPA exhibited a drain efficiency of 54.2% to 73.4% at peak output power of 49.7 to 51.4 dBm and a drain efficiency of 41.7% to 58.0% at output back-off of 6 dB. For the down-link long-term evolution signal with a channel bandwidth of 10 MHz and a peak-to-average power ratio of 6.5 dB, a drain efficiency of 41.3% to 57.4% and an adjacent channel leakage power ratio of −22.5 to −30.2 dBc at an average output power of 43.2 to 449 dBm were achieved at an octave bandwidth.
Identifying Tips Web Sites of a Specific Query based on Search Engine Suggests and the Topic Distribution

This paper proposes techniques of automatically discovering tips Web sites from a large collection of Web pages using a topic model and support vector machine (SVM). Tips refer to practical knowledge or expertise that is used to help accomplish certain tasks in a particular field. We designed several approaches of extracting features with respect to domain names based on their distribution among Web pages and candidate tips Web sites. In addition, search engine suggests, the query keywords used to fetch Web pages from the search engine are also considered to present patterns that can be potential features. It was discovered from our dataset that domain names of tips Web sites (Web sites containing tips on a certain specific theme) are more likely dispersed among topics and Web pages. These domain names also tend to correspond to a larger number of search engine suggests. This paper verifies such observed patterns by training an SVM using those extracted features. Evaluation is performed in precision and recall to measure correctness of classifying whether or not a domain name belongs to a tips Web site.
Improved Autonomous Exploration Technology of Mobile Robot in Unknown Building Environments

This paper addresses the issues of the autonomous exploration and grid-topology hybrid map building of mobile robots in large-scale environments. First, in order to improve the defect of the original method in the special environment, a way to extract the target point based on the free area is proposed. Then, we use the information gain and the extraction method of fast back to the initial topological node to evaluate the target point, so as to solve the problem that the long time no loop closure detection leads to the failure of the map building. Finally, we propose an improved method of building and optimizing the real-time topological map based on the information of the laser data and grid map, which makes the grid-topology hybrid map more concise and the exploration process more efficient. The effectiveness of the proposed autonomous exploration and grid-topology hybrid map building method for mobile robots in unknown environments is verified by experiments.
Design, Synthesis and Application of A Novel Approximate Adder

Approximate computing is a new design paradigm in VLSI design and test. It can improve the performance of error-tolerant applications at the expense of slight loss in computational accuracy. In this paper, we propose a novel approximate adder with a hybrid structure (HYB-adder) which produces results of different precision. The proposed adder is synthesized by utilizing 28nm FD-SOI (fully-depleted silicon-on-insulator) technology. The proposed HYB-adder outperforms existing approximate adder designs regarding mean error distance with comparable area, delay and power consumption. The efficiency is also validated by its application in DCT/IDCT procedures.
Design & Development of $4\times 2$ Microstrip Patch Antenna Array with Circular Polarized Elements for Satellite Application

In this work a new design and development of a $\pmb{4\text{x}2}$ microstrip patch antenna array with circular polarized elements. The basic antenna structure is a rectangular patch antenna with truncated corners. He is designed on 1.58 mm thick FR4 substrate whose dielectric constant is 4.4, with dimensions of about $\pmb{12\times 15\times 1.58\ \text{mm}^{3}}$. The array dimensions of about $\pmb{60\text{x}30\text{x}1.58\ \text{mm}^{3},}$ he is fed by 50 ohm line, and he consists of two symmetrical array of microstrip subarrays, each subarray is a $\pmb{2\text{x}2}$ patches element microstrip. Each array comprised 4 elements of patch.
Improve Diversity-oriented Biomedical Information Retrieval using Supervised Query Expansion

In the paper, we propose a novel diversity-oriented biomedical information retrieval method based on supervised query expansion. Our method aims to obtain the most relevant and diversified terms to enrich user queries for better interpreting the information needs. We first propose a diversity-oriented labeling strategy to annotate the usefulness of candidate expansion terms. We then extract both the context-based and resource-based term features to represent terms as feature vectors. In model training, we propose a diversity-oriented group sampling method to modify the loss function of learning-to-rank for accurate biomedical term ranking. Experimental results on TREC Genomics datasets show that our method is effective in improving the performance of biomedical information retrieval in terms of both the relevance and the diversity.