Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy and Safety of Repeated Percutaneous Radiofrequency Thermocoagulation for Recurrent Trigeminal Neuralgia

Background: Percutaneous radiofrequency thermocoagulation (PRT) is used to treat trigeminal neuralgia (TN) with a satisfactory pain relief but a high recurrence rate. Objective: To explore the efficacy and safety of repeated PRT for recurrent TN as compared to patients who received the first PRT. Methods: Between January 2013 to May 2013, 31 patients with recurrent TN who have been treated with PRT previously were recruited and underwent repeated PRT (group A), and compared with 41 TN patients received the first initial PRT (group B). Visual Analog Scale (VAS) score was assessed preoperatively and postoperatively after 2 years of follow-up, and compared in terms of initial pain relief, complications, and recurrence rate between the two groups. Results: In group A, 27 patients (87.0%) were pain free immediately, and 30 patients (96.8%) experienced pain relief at 48 h, whereas that was 37 patients (90.0%) and 40 patients (97.6%) in group B (p ≧ 0.05). Patients in group A who remained an “excellent” or “good” pain relief condition (VAS score ≦ 1) were 96.8% at 6 months, 83.9% at 1 year, 74.2% at 2 years, whereas the percentage in group B was 97.6, 85.4, and 73.2% (p ≧ 0.05). Conclusion: For patients with recurrent TN after PRT, repeated PRT might be considered as a useful treatment option when other treatments fail. In addition, the frequency and severity of adverse events for repeated PRT were similar as compared to initial PRT.
Efficient Matrix Profile Computation Using Different Distance Functions

Matrix profile has been recently proposed as a promising technique to the problem of all-pairs-similarity search on time series. Efficient algorithms have been proposed for computing it, e.g., STAMP [13], STOMP [15] and SCRIMP++ [10]. All these algorithms use the z-normalized Euclidean distance to measure the distance between subsequences. However, as we observed, for some datasets other Euclidean measurements are more useful for knowledge discovery from time series. In this paper, we propose efficient algorithms for computing matrix profile for a general class of Euclidean distances. We first propose a simple but efficient algorithm called AAMP for computing matrix profile with the "pure" (non-normalized) Euclidean distance. Then, we extend our algorithm for the p- norm distance. We also propose an algorithm, called ACAMP, that uses the same principle as AAMP, but for the case of z-normalized Euclidean distance. We implemented our algorithms, and evaluated their performance through experimentation. The experiments show excellent performance results. For example, they show that AAMP is very efficient for com- putting matrix profile for non-normalized Euclidean distances. The results also show that the ACAMP algorithm is significantly faster than SCRIMP++ (the state of the art matrix profile algorithm) for the case of z-normalized Euclidean distance.
Quadratic Autoencoder for Low-Dose CT Denoising

Recently, deep learning has transformed many fields including medical imaging. Inspired by diversity of biological neurons, our group proposed quadratic neurons in which the inner product in current artificial neurons is replaced with a quadratic operation on inputs, thereby enhancing the capability of an individual neuron. Along this direction, we are motivated to evaluate the power of quadratic neurons in representative network architectures, towards “quadratic neuron based deep learning”. In this regard, our prior theoretical studies have shown important merits of quadratic neurons and networks. In this paper, we use quadratic neurons to construct an encoder-decoder structure, referred to as the quadratic autoencoder, and apply it for low-dose CT denoising. Then, we perform experiments on the Mayo low-dose CT dataset to demonstrate that the quadratic autoencoder yields a better denoising performance.
Voronoi-based Efficient Surrogate-assisted Evolutionary Algorithm for Very Expensive Problems

Very expensive problems are very common in practical system that one fitness evaluation costs several hours or even days. Surrogate assisted evolutionary algorithms (SAEAs) have been widely used to solve this crucial problem in the past decades. However, most studied SAEAs focus on solving problems with a budget of at least ten times of the dimension of problems which is unacceptable in many very expensive real-world problems. In this paper, we employ Voronoi diagram to boost the performance of SAEAs and propose a novel framework named Voronoi-based efficient surrogate assisted evolutionary algorithm (VESAEA) for very expensive problems, in which the optimization budget, in terms of fitness evaluations, is only 5 times of the problem’s dimension. In the proposed framework, the Voronoi diagram divides the whole search space into several subspace and then the local search is operated in some potentially better subspace. Additionally, in order to trade off the exploration and exploitation, the framework involves a global search stage developed by combining leave-one-out cross-validation and radial basis function surrogate model. A performance selector is designed to switch the search dynamically and automatically between the global and local search stages. The empirical results on a variety of benchmark problems demonstrate that the proposed framework significantly outperforms several state-of-art algorithms with extremely limited fitness evaluations. Besides, the efficacy of Voronoi-diagram is furtherly analyzed, and the results show its potential to optimize very expensive problems.
Deep transfer across domains for face antispoofing

Abstract. A practical face recognition system demands not only high recognition performance but also the capability of detecting spoofing attacks. While emerging approaches to face antispoofing have been proposed in recent years, most of them perform poorly on unseen samples. The generalizability of face antispoofing needs to be significantly improved before it can be adopted by practical application systems. The main reason for the poor generalization of current approaches is the variety of materials among the spoofing devices. As the attacks are produced by putting a spoofing display (e.g., paper, electronic screen, forged mask) in front of a camera, the variety of spoofing materials makes the spoofing attacks quite different. Another reason for the poor generalizability is that limited labeled data are available for training for face antispoofing. We focus on improving the generalizability of convolutional neural network (CNN)-based face antispoofing methods across different kinds of datasets. We propose a deep domain transfer CNN using sparsely unlabeled data from the target domain to learn features that are invariant across domains for face antispoofing. Experiments on five face spoofing datasets show that the proposed method significantly improves the cross-test performance only with a small number of unlabeled samples from the target domain.
Beyond Monetary Incentives

In this article, we aim to gain a better understanding into how paid microtask crowdsourcing could leverage its appeal and scaling power by using contests to boost crowd performance and engagement. We introduce our microtask-based annotation platform Wordsmith, which features incentives such as points, leaderboards, and badges on top of financial remuneration. Our analysis focuses on a particular type of incentive, contests, as a means to apply crowdsourcing in near-real-time scenarios, in which requesters need labels quickly. We model crowdsourcing contests as a continuous-time Markov chain with the objective to maximise the output of the crowd workers, while varying a parameter that determines whether a worker is eligible for a reward based on their present rank on the leaderboard. We conduct empirical experiments in which crowd workers recruited from CrowdFlower carry out annotation microtasks on Wordsmith—in our case, to identify named entities in a stream of Twitter posts. In the experimental conditions, we test different reward spreads and record the total number of annotations received. We compare the results against a control condition in which the same annotation task was completed on CrowdFlower without a time or contest constraint. The experiments show that rewarding only the best contributors in a live contest could be a viable model to deliver results faster, though quality might suffer for particular types of annotation tasks. Increasing the reward spread leads to more work being completed, especially by the top contestants. Overall, the experiments shed light on possible design improvements of paid microtasks platforms to boost task performance and speed and make the overall experience more fair and interesting for crowd workers.
Differentially-Private Two-Party Egocentric Betweenness Centrality

We describe a novel protocol for computing the egocentric betweenness centrality of a node when relevant edge information is spread between two mutually distrusting parties such as two telecommunications providers. While each node belongs to one network or the other, its ego network might include edges unknown to its network provider. We develop a protocol of differentially-private mechanisms to hide each network’s internal edge structure from the other; and contribute a new two-stage stratified sampler for exponential improvement to time and space efficiency. Empirical results on several open graph data sets demonstrate practical relative error rates while delivering strong privacy guarantees, such as 16% error on a Facebook data set.
Mise au point d’une méthode d’annotation morphosyntaxique fine du serbe (Developping a method for detailed morphosyntactic tagging of Serbian)[In French]

Cet article présente une expérience d’annotation morphosyntaxique fine du volet serbe du corpus parallèle ParCoLab (corpus serbe-français-anglais). Elle a consisté à enrichir une annotation existante en parties du discours avec des traits morphosyntaxiques fins, afin de préparer une étape ultérieure de parsing. Nous avons comparé trois approches : 1) annotation manuelle ; 2) préannotation avec un étiqueteur entraîné sur le croate suivie d’une correction manuelle ; 3) réentraînement de l’outil sur un petit échantillon validé du corpus, suivi de l’annotation automatique et de la correction manuelle. Le modèle croate maintient une stabilité globale en passant au serbe, mais les différences entre les deux jeux d’étiquettes exigent des interventions manuelles importantes. Le modèle ré-entraîné sur un échantillon de taille limité (20K tokens) atteint la même exactitude que le modèle existant et le gain de temps observé montre que cette méthode optimise la phase de correction.
An asynchronous mesh NoC based booth multiplication

The multiplier is one of the most important arithmetic units, which is an essential part of an IC system and affects its efficiency dramatically. The Booth structure of a multiplier is a wide-used and efficient structure of real IC systems, and the performance of a Booth multiplier would be mainly determined by partial product issues, e.g., multiplication and addition, as well as the frequency of the clock. Then, one of the potential improvements would be the asynchronous control to reduce power by cancelling the global clock, the other be the structure and implementation of the algorithm for high performance, and the last could be the benefits of reconfiguration for sharing process elements. In this paper, we proposed a Booth algorithm which organized by an asynchronous NoC, e.g., the partial product generators are located into a configurable network, and other part of the circuit are organized and adjusted by an asynchronous mechanism. The design is finally implemented by an 8-bit asynchronous Booth multiplier, which is synthesized and post simulated with a Xilinx Virtex-7 FPGA development board.
Extracting time series variation of topic popularity in microblogs

Extracting topics and their popularities in microblogs is a promising approach to discover popular topics in the world. To challenge this task, some methods that estimate popularity of topics based on Latent Dirichlet Allocation (LDA) has been proposed. However, LDA fails to extract favorable topics on a collection of short text documents such as microblogs because the word co-occurrence information in an individual document is sparse. Therefore, in order to extract topics from microblogs, we should use a model specialized for short text documents. In this paper, we propose a topic popularity estimation method using Biterm TopicModel (BTM), which can alleviate the problem caused by document level word co-occurrence sparsity. We extract topics from the microblog documents with BTM for each time period and estimate the frequency of each topic occurrence. The proposed method can analyze the popularity of topics in a real time because we apply anefficient inference algorithm for BTMonsmall batches of tweets. Experiments on tweets collection show that some of the topics extracted by the proposed method correspond to the real world events and a topic burstiness gets higher when the event occurs.
Analysis of Item Selection Behavior in Online Shopping

Opportunities to purchase products from online shopping sites continue to increase. One feature of online shopping sites is searching for products by entering queries on input forms. Analyzing user search behavior is critical for appropriate search support and sales improvement. In this paper, we assigned five search behavior codes from continuous logs: replacing keywords (R), modifying keywords (M), adding keywords (A), deleting keywords (D), and continuing same keywords (C). Using code sequences, we analyzed frequent search patterns and the relationship between initial search patterns and search length. As a result of evaluation experiments, we found that behavior, which moves to another search result page without changing the query, occupies a majority of search behavior. Search behavior also becomes longer when it follows the initial stage of the search.
Real-Time Head Action Recognition Based on HOF and ELM

SUMMARY Head action recognition, as a speciﬁc problem in action recognition, has been studied in this paper. Di ﬀ erent from most existing researches, our head action recognition problem is speciﬁcally deﬁned for the requirement of some practical applications. Based on our deﬁnition, we build a corresponding head action dataset which contains many challenging cases. For action recognition, we proposed a real-time head action recognition framework based on HOF and ELM. The framework consists of face detection based ROI determination, HOF feature extraction in ROI, and ELM based action prediction. Experiments show that our method achieves good accuracy and is e ﬃ cient enough for practical applications.
Network intrusion detection algorithm based on deep neural network

With the rapid development of network technology, active defending of the network intrusion is more important than before. In order to improve the intelligence and accuracy of network intrusion detection and reduce false alarms, a new deep neural network (NDNN) model based intrusion detection method is designed. A NDNN with four hidden layers is modelled to capture and classify the intrusion features of the KDD99 and NSL-KDD training data. Experiments on KDD99 and NSL-KDD dataset shows that the NDNN-based method improves the performance of the intrusion detection system (IDS) and the accuracy rate can be obtained as high as 99.9%, which is higher when compared with other dozens of intrusion detection methods. This NDNN model can be applied in IDS to make the system more secure.
Clause-level Negative-opinion Analysis for Classifying Reviews on Multiple Domains

Today, vast amounts of reviews are posted on the internet. Businesses must extract negative opinions of products and services from reviews to improve their products and services. Still, some issues related to automatic extraction of negative sentiment must be addressed if they intend to use the information to improve their products and services. (1) Reviews are usually long texts. Finding only negative opinions for improvement is therefore time-consuming. (2) Many studies proposed about sentiment classification using machine learning. When we use the machine learning technique, we must prepare a lot of training data of the same product and services as test data. It is high cost. As described herein, we propose a clause-level sentiment classification method using Conditional Random Field (CRF) to address the issue (1). Also, we describe experiments of sentiment classification on reviews of multiple domains for the issue (2).
Flow Graph Generation Method for Visualizing Procedural Texts

The amount of procedural texts, which describe the steps to reach a goal or solve a problem, continues to increase. This paper proposes flow graph generation methods for visualizing procedural texts. Examples of typical procedural texts include cooking texts, operation manuals for appliances/devices, and technical papers and patents that consist of methods or algorithms. The flow graphs of procedural texts allow a process's entire structure to be grasped more simply and for differences or shared parts to be detected. To generate a flow graph, we developed a structure analysis method using a domain-specific dictionary. We applied our proposed methods to an actual recipe and a manual for a computer printer and discussed our results and the remaining problems.
Analysis of Study Program Selection Patterns Using FP-Growth and ECLAT Methods

A set of data that correlates form patterns. In determining the patterns of data sets using association rules. The University has a variety of faculties that have several study programs. Many factors that influence prospective students determining the choice of their study program. Researchers consider it important to look for patterns of prospective students in determining their study program choices. With these patterns can help prospective students determining their choices. Forming these patterns uses the association rules with the FP-Growth and ECLAT methods. Researchers conducted a survey to students from various universities on the island of Java, Indonesia. Of the 35 transaction data obtained, the support minimum value is 20% and the confidence value is 45%. There are some association rules obtained, namely 12 rules using FP-Growth algorithm and 9 rules using ECLAT algorithm. Of these rules, interest factors, accreditation, and job prospects are the main factors influencing the choice of study programs.
Object tracking with the multi-templates regression model based MS algorithm

To deal with the problems of occlusion, pose variations and illumination changes in the object tracking system, a regression model weighted multi-templates mean-shift (MS) algorithm is proposed in this paper. Target templates and occlusion templates are extracted to compose a multi-templates set. Then, the MS algorithm is applied to the multi-templates set for obtaining the candidate areas. Moreover, a regression model is trained to estimate the Bhattacharyya coefficients between the templates and candidate areas. Finally, the geometric center of the tracked areas is considered as the object’s position. The proposed algorithm is evaluated on several classical videos. The experimental results show that the regression model weighted multitemplates MS algorithm can track an object accurately in terms of occlusion, illumination changes and pose variations.
Low-rank representation for semi-supervised software defect prediction

Software defect prediction based on machine learning is an active research topic in the field of software engineering. The historical defect data in software repositories may contain noises because automatic defect collection is based on modified logs and defect reports. When the previous defect labels of modules are limited, predicting the defect-prone modules becomes a challenging problem. In this study, the authors propose a graph-based semi-supervised defect prediction approach to solve the problems of insufficient labelled data and noisy data. Graph-based semi-supervised learning methods used the labelled and unlabelled data simultaneously and consider them as the nodes of the graph at the training phase. Therefore, they solve the problem of insufficient labelled samples. To improve the stability of noisy defect data, a powerful clustering method, low-rank representation (LRR), and neighbourhood distance are used to construct the relationship graph of samples. Therefore, they propose a new semi-supervised defect prediction approach, named low-rank representation-based semi-supervised software defect prediction (LRRSSDP). The widely used datasets from NASA projects and noisy datasets are employed as test data to evaluate the performance. Experimental results show that (i) LRRSSDP outperforms several representative state-of-the-art semi-supervised defect prediction methods; and (ii) LRRSSDP can maintain robustness in noisy environments.
PS-Tree-based Efficient Boolean Expression Matching for High Dimensional and Dense Workloads

Boolean expression matching is an important function for many applications. However, existing solutions still suffer from limitations when applied to high-dimensional and dense workloads. To overcome these limitations, in this paper, we design a data structure called PS-Tree that can efficiently index subscriptions in one dimension. By dividing predicates into disjoint predicate spaces, PS-Tree achieves high matching performance and good expressiveness. Based on PS-Tree, we first propose a Boolean expression matching algorithm PSTBloom. By efficiently filtering out a large proportion of unmatching subscriptions, PSTBloom achieves high matching performance, especially for high-dimensional workloads. PSTBloom also achieves fast index construction and a small memory footprint. Compared with state-of-the-art methods, comprehensive experiments show that PSTBloom reduces matching time, index construction time and memory usage by up to 84%, 78% and 94%, respectively. Although PSTBloom is effective for many workload distributions, dense workloads represent new challenges to PSTBloom and other algorithms. To effectively handle dense workloads, we further propose the PSTHash algorithm, which divides subscriptions into disjoint multidimensional predicate spaces. This organization prunes partially matching subscriptions efficiently. Comprehensive experiments on both synthetic and real-world datasets show that PSTHash improves the matching performance by up to 92% for dense workloads.
Identifying the Role of Primary and Secondary Interactions on the Mechanical Properties and Healing of Densely Branched Polyimides

We present a systematic study of the role of the aromatic dianhydride structure on the self-healing behavior of dimer diamine-based polyimides. By means of solid-state NMR and rheology, we studied the molecular and microscale dynamics of four polyimides comprising the same aliphatic branched diamine yet with variable dianhydride rigidities and correlated these to their macroscopic healing kinetics measured by tensile testing. Following the two-step kinetics of the healing process, we were able to differentiate and quantify the extent of mechanical strength recovery in each of the healing stages separately. Moreover, the detailed rheology and solid-state NMR allowed us to shed light on the role of the aromatic interactions and branches on the mechanical properties and mechanical integrity during macroscopic healing. The study reveals the relevance and interplay of primary and secondary interactions in the development of non-cross-linked strong and healing polymers able to maintain mechanical integrity during healing.