Miracle-dz/UniFAR-FTUs huggingface dataset:
ParIS: The Next Destination for Fast Data Series Indexing and Query Answering

We propose ParIS, the first disk-based data series index that inherently takes advantage of modern hardware parallelization, in order to accelerate processing times. Our experimental results demonstrate that ParIS completely removes the CPU latency during index construction for disk-resident data. In terms of exact query answering, ParIS is more than 2 orders of magnitude faster than the current state of the art index scan method, and more than 3 orders of magnitude faster than the optimized serial scan method. ParIS owes its efficiency not only to the effective use of multi-core and multi-socket architectures, in order to distribute and execute in parallel both index construction and query answering, but also to the exploitation of the Single Instruction Multiple Data (SIMD) capabilities of modern CPUs, in order to further parallelize the execution of individual instructions inside each core.
Countermeasure of Statistical Inference in Database Security

The security management of databases involves complex issues for the companies. The complexity of security measure needs to apply increases with the most complex database. The statistical queries associated with statistical database uses the aggregate functions such as min, max, average and count to provide statistical information about entries. The data vulnerabilities raise the security issues because of a clever combination of statistical queries to obtain confidential information for the individual entry. Undeniably, malpractices of user allow taking advantage of the aggregate functions and compromising the confidentiality. The statistical interference analysis poses significant risk for the security database system. This research proposes intelligent framework which is useful to hide sensitive information in case of combination of statistical queries. Firstly, the framework is useful to define the sensitive information for the decision making of the administrator to set some rules. Secondly, the framework explores the relationship between sensitive and other attributes in case of rule discrimination based on attribute-orientation and thus allows making a decision on attributes useful to drive confidential information.
Jupyter Notebooks for Generous Archive Interfaces

To help scholars to extract meaning, knowledge and value from large volumes of archival content, such as the Dutch Common Lab Research Infrastructure for the Arts and Humanities (CLARIAH), we need to provide more ‘generous’ access to the data than can be provided with generalised search and visualisation tools alone. Our approach is to use Jupyter Notebooks in combination with the existing archive APIs (Application Programming Interface). This gives access to both the archive metadata and a wide range of analysis and visualisation techniques. We have created notebooks and modules of supporting functions that enable the overview, investigation and analysis of the archive. We demonstrate the value of our approach in preliminary tests of its use in scholarly research, and give our observations of the potential value for archivists. Finally, we show that good archive knowledge is essential to create correct and meaningful visualisations and statistics.
Chaotic neurochips for fuzzy computing

A massive chaotic neural network (CNN) is demonstrated with a fixed-point Hebbian synaptic weight dynamic: an instantaneous input, and a piecewise negative logic output. The variable slope of the output versus the input becomes a software control of the collective chaos hardware. Two applications are given. The mean synaptic weight field plays an important role for fast pattern recognition capability in examples of both the habituation and the novelty detections. Another novel usage of CNN is to be a bridge between neural learning and learnable fuzzy logic.
Tongue coating’s metabolic markers in Dampness-heat syndrome of chronic gastritis patients

Objective: The aim of the study was to investigate the characteristic index and metabolic markers of tongue coating in the patients with chronic gastritis (CG). Methods: From October 2014 to July 2015, 24 healthy volunteers and 60 patients with chronic gastritis (30 with the dampness-heat syndrome and 30 with non-dampness-heat syndrome) were enrolled in the study. The metabolites of tongue coating among three groups were measured by gas chromatography/mass spectrometry (GC/MS) method. Results: The characteristics of tongue coating were different among dampness-heat syndrome group, non-dampness-heat syndrome group and control group. The tongue coating was thicker and greasier of the patients with chronic gastritis in dampness-heat syndrome group. Three metabolic markers were detected in tongue coating by gas chromatography/mass spectrometry, accompanied with a retention time of14.994955, 14.47272 and 7.190636 between the dampness-heat syndrome group and non-dampness-heat syndrome group. By searching the NIST2005 database, these three metabolic biomarkers identified as Cystathionine or 3,5-Di-t-butyl-4-hydroxybenzoic acid, ethyl ester; Pyroglutamic acid and Putrescine were closely correlated with the metabolic pathways of the dampness-heat syndrome. Conclusions: The tongue coating of chronic gastritis with dampness-heat syndrome was thicker and greasier. These three metabolites biomarker in dampness-heat syndrome of chronic gastritis may be associated with the synthesis and decomposition of amino acids, suggesting that the pathway of amino acid metabolism may mediate the dampness-heat syndrome of chronic gastritis.
Domain specific automatic Chinese multiple-type question generation

Question Generation is a rising research field of artificial intelligence in education and staff training. It is an effective strategy to conduct knowledge evaluation and performance appraisal. How to generate a variety of good quality questions has been an essential issue. In this paper, we propose an automatic method that generates Chinese multiple-type question by combing diverse knowledge bases and similar language features. Our research is mainly focused on Tradition Chinese Medicine (TCM) domain because it contains many Chinese-based resources and complex relationships. But the method is flexible and can be easily applied to other domains. Experimental results demonstrate that the proposed method obtain a good result.
Time-Efficient RFID-Based Stocktaking with a Coarse-Grained Inventory List

RFID-based stocktaking uses RFID technology to verify the presence of objects in a region e.g., a warehouse or a library. The existing approaches for this purpose assume that an inventory list of objects in the interrogation region of an RFID reader is known. This is not true in some cases. For example, for a handheld RFID reader, only the objects in a larger region (e.g., the warehouse) rather than in its interrogation region can be known. The additional objects significantly increase the time required for stocktaking. In this paper, we propose a time-efficient stocktaking algorithm called CLS (Coarse-grained inventory list based stocktaking) to solve this problem. We transform the problem to a missing tag identification problem with a large missing rate. CLS enables multiple missing objects to hash to a single time slot and thus verifies them together. CLS also improves the existing approaches by utilizing more kinds of RFID collisions and reducing approximately one-fourth of the amount of data sent by the reader. Extensive simulations are performed and the results show CLS outperforms the best existing algorithm.
Protein Network Analysis by the Interaction Force between Protein Molecules

Biological systems are often represented as protein networks which are complex sets of binary interactions between different entities. And protein networks analysis is viable method to understand disease machinery, protein functions and drug repositioning. However, it is a huge challenge because of the diversity and complexity of networks. Many algorithms based on graph theory have been proposed to identify protein complexes in protein interaction networks. In this paper, we present a novel algorithm (IFPM) that analyzes protein network by the interaction force between protein molecules. The protein network can reach a stable state only if the interaction force between proteins in the network is in equilibrium state. According to the definition of Van der Waals force, IFPM defines the interaction force between protein molecules based on the structure of protein network. The proposed algorithm is tested on several publicly protein networks with comparative algorithms. The results show that IFPM performs better than other existing methods and our algorithm provides an accurate and scalable method of detecting protein complexes within protein networks.
Identification of lncRNA-disease association using bi-random walks

There is evidence to suggest that lncRNAs are associated with distinct and diverse biological processes. The dysfunction or mutation of lncRNAs are implicated in a wide range of diseases. An accurate prediction of potential lncRNA-disease association can benefit the diagnosis of diseases and help us to gain a better understanding of the molecular mechanism. Although many related algorithms have been proposed, there still have much room for improvement. In this paper, we develop an algorithm, BiWalkLDA, to predict lncRNA-disease association by using bi-random walks. It constructs a lncRNA-disease network by integrating interaction profile and gene ontology information. Then, bi-random walks was applied to three real biological datasets. Results show that our method outperforms other algorithms in predicting lncRNA-disease association in terms of both accuracy and specificity. The source code of BiWalkLDA can be freely accessed at https://github.com/screamer/BiwalkLDA.
Prediction of Direct Normal Irradiance using a Novel Multi-model Algorithm

The prediction of solar radiation is a hot issue in the field of solar energy and time series data analysis. Due to the rapidly development of photovoltaic (PV), most related works just focus on the Global Horizontal Irradiance (GHI), while the Direct Normal Irradiance (DNI) is more important for another new solar energy technology: solar thermal. The DNI is related to GHI, however, the they are not corresponding one by one. Furthermore, the change rules of DNI is very various in different meteorological conditions. For example, in the cloudy day the DNI is more likely to change sharply than the clear day. Thus, a method to deal with these different weather conditions is needed. This paper proposes a multi-model algorithm to matching this new requirement, by building a complex network, we describe the relationship between the different sample, and then using a community detection method based on modularity optimization, we cluster the samples into different classes. Finally, we deal with these classes by different extreme learning machine models. The experimental result based on the real data proven this multi-model algorithm is useful.
Towards a Prolonged Productivity in Industry 4.0: A Framework for Fatigue Minimisation in Robot-Robot Co-Manipulation

Industry 4.0 envisions the integration of flexible and quickly reconfigurable robotic systems in assembly lines. This has led to the development of light-weight and adaptive collaborative robots with limited power and payload constraints. Hence, repetitive tasks or those that demand high forces may exceed such limits, resulting in robot damage and lost productivity. To address this problem, we propose a novel framework to prolong the lifetime of collaborative robots while guaranteeing the desired level of productivity. To address this, we propose a method that minimises a function of robot fatigue, an index able to model the robot motor usage at the joint level. This index features the desired external force, task duration, hardware parameters, and fatigue history. Moreover, future tasks are considered through fatigue thresholds imposed on specific joints, computed according to the robot safety requirements. We proved the effectiveness of the approach by comparing its results in terms of fatigue and torque with the well-known minimum effort approach. The results showed that our method ensures that the fatigue thresholds are not exceeded.
Online Prediction Method of Cement Clinker f-Cao Based on K-ELM

Online prediction of f-Cao content is of great significance for timely control of cement rotary kiln to ensure its stable operation. In this paper, an on-line prediction method of f-Cao content based on kernel extreme learning machine (K-ELM) is proposed. According to the cement process, the input and output of the K-ELM model and its time point matching relationship are determined. On this basis, considering the continuity of cement production, the sliding time window method is combined with the K-ELM prediction method to update the K-ELM model to achieve online prediction of f-Cao content. Finally, taking the production data of a cement company as an example, the online prediction method of f-Cao content was verified. The experimental results showed that the variation trend of the online predicted value of f-Cao was similar to that of the laboratory test value of f-Cao. This paper provided a certain technical basis for in-depth study of f-Cao online prediction to further realize the energy saving and quality improvement of clinker production.
Distributed Rough Set Based Feature Selection Approach to Analyse Deep and Hand-crafted Features for Mammography Mass Classification

Breast cancer has a high incidence among women worldwide. This, together with the recent developments in deep learning based convolutional networks, have motivated research towards the enhancement of Computer Aided Diagnosis (CAD) systems. In this paper, the performance of a densely connected convolutional network (DenseNet) for breast cancer was investigated for the malignant/benign classification of mammographic masses. Different mammography data sets were collected to investigate the capacity of this network for learning a combination of these databases. To achieve this, internal low-level, mid-level and high-level features/abstracts were extracted from the model together with hand-crafted features, generating a vast amount of data. Using the distributed rough set based feature selection approach (Sp-RST), significant features were selected from both deep learning based features and hand-crafted ones, and fed into a learning model with separate and combined data approaches for the classification of mammographic masses. Results show that by using Sp-RST as a powerful technique capable of performing big data preprocessing, DenseNet had the representational capacity to learn mammographic abnormalities.
Thermal Study of Permanent Magnet Direct Drive Wheel Motor

This work deals with thermal design of permanent magnet synchronous motor (PMSM). The designed motor is the in-wheel traction motor for automotive. The motor consist of an external rotor and inner stator with water cooling system. The thermal calculation is based on the simplified lumped parameters thermal model and is verified by the finite element methods FEM. The thermal simulation is made for the machine under various operating conditions of the car.
Achieving Horizontal Scalability in Density-based Clustering for URLs

Clustering has become an important means to analyze large datasets when labeled data is not available. The volume of data and its variety however challenge classical clustering algorithms, with density-based ones suffering from severe scalability issues.In this paper, we propose a way to perform density-based clustering efficiently by exploiting the horizontal scalability offered by big data solution such as Apache Spark. We are motivated by recent techniques for Internet monitoring that rely on clustering to group similar events and spot anomalies. We focus specifically on textual data, such as URLs or server logs. Computing the distance between points, here represented as strings, becomes a major issue. Indeed, when datasets become large, most of density-based clustering algorithms are bottlenecked by the computation of all the distances between any pairs of elements. To overcome this, we propose to decouple the distance computation, easily amenable to parallelization, from the algorithm execution. By using this approach, we can easily exploit the benefits of distributed platforms like Apache Spark or MapReduce. A faster execution of the algorithms is thus guaranteed, together with more flexibility in the choice of the clustering method.We make both the code and the dataset publicly available, to both guarantee the repeatability of the experiments, and possibly offering a new benchmark dataset.
Stepped List Decoding for Polar Codes

In the successive cancellation list (SCL) decoding of polar codes, as the list size increases, the error correction performance improves. However, a large list size results in high computational complexity and large memory requirement.In this paper, we investigate the list decoding process by introducing a new parameter named path metric range (PMR) to elucidate the properties of the evolution of the path metrics (PMs) within the list throughout the decoding process. Then, we advocate that the list size can change stepwise depending on PMR. As a result, we propose a stepped list decoding scheme in which the error correction performance of the conventional list decoding is preserved while the path memory may reduce by 75%, the size of the internal LLR memory and partial sums memory can drop by 50%, and the computational complexity may halve. The reduction in complexity is SNR-independent and achieved without introducing any computational overhead.
An Incremental Self-Learning Algorithm with Robustness against Impulsive Noise

In this paper, we propose a self-learning algorithm with the incremental learning capability, in which correntropy is selected as the cost function. Based on the correntropy concept of information theoretic learning (ITL), the incremental self-learning algorithm is presented for nonlinear system identification and the real world prediction problems against impulsive noises. Correntropy is a measure of information content that represents a similarity measure between two arbitrary random variables and has the advantages of rejecting outliers or impulsive noises. The proposed algorithm combines the neural network structure and fuzzy model, wherein the network hidden node (i.e. rule base) starts with empty and grows online based on the incremental learning method. Moreover, the maximum correntropy criterion (MCC) is applied to update the weight parameters for self-learning during the incremental process. The incremental process under two criteria is validated in detail through a nonlinear identification problem under impulsive environment. The performance evaluation of the proposed algorithm is also carried out on a nonlinear system identification and Time Series Data Library under both noise-free and impulsive noise conditions. The simulation results demonstrate that the proposed algorithm has better identification and prediction accuracy with the least number of rules and training time, and it also owns robustness for handling superior impulsive noise when compared to other algorithms.
Shaping Deadline Coflows to Accelerate Non-Deadline Coflows

Data-parallel applications generate a mix of coflows with and without deadlines. Deadline coflows are mission-critical and must be completed within deadlines, while non-deadline coflows desire to be completed as soon as possible. Scheduling such mix-coflows is an important problem in modern datacenters. However, existing solutions only focus on one of the two types of coflows: they either solely focus on meeting the deadlines of deadline-aware coflows or reducing the coflow completion times (CCTs) of non-deadline coflows. In this paper, we study the problem of optimizing deadline and non-deadline coflows simultaneously. To this end, we present a new optimization framework, mixCoflow, to schedule deadline coflows with the objective of minimizing and balancing their bandwidth footprint, such that non-deadline coflows can be scheduled as early as possible. Specifically, we develop the mathematical model and formulate the scheduling problem for deadline coflows as a lexicographical min-max integer linear programming (ILP) problem. Through rigorous theoretical analysis, this ILP problem has been proved to be equivalent to a linear programming (LP) problem that can be solved with standard LP solvers. By solving this LP, mixCoflow is able to balance the bandwidth footprint of deadline coflows while guaranteeing their deadlines. As a result, non-deadline coflows can be scheduled as soon as possible whenever they arrive. To demonstrate the effectiveness of our work, we have conducted extensive simulations based on a widely used Facebook data trace. The simulation results verify that mixCoflow can achieve significant improvement on the average CCT of non-deadline coflows, at no expense of increasing the deadline miss rates of deadline coflows, when compared to the state-of-art solutions.
Optimizing the Efficiency of Data Transfer in Dataflow Architectures

Dataflow architectures have been proposed in response to several emerging problems in processor design, such as computational efficiency, design complexity and power efficiency. A dataflow architecture is composed of multiple processing elements (PEs) that are organized into the form of grid. The instructions are compiled by compiler and are explicitly mapped to the PE grid using the instruction placement algorithm. The instruction placement algorithms generally take load balancing, low communication delay, and resource contention as input conditions, but most of them ignore the router congestion of the dataflow network. By applying three kinds of typical instruction placement algorithms on the dataflow model, we found that the dynamic packets that are transferred through the dataflow network is not evenly distributed. Due to the reason that dataflow network usually adopts the uniform structure for each router, congestion is susceptible to occur in the directions dealing with larger load. Partial congestion of the network will degrade the transfer efficiency of the dataflow network, which directly affects the execution efficiency of the dataflow processor. In order to optimize the transfer efficiency of routers and execution efficiency of dataflow processor under unevenly distributed network load, we proposed a cost-efficient hardware mechanism to dynamically detect the imbalances in different directions in each router and adaptively reallocate resources in the bottleneck router. The proposed mechanism is transparent to compiler and instruction placement algorithms. Besides, it can be easily applied as a supplementary hardware optimization to any instruction placement algorithm causing such kind of partial congestion problem. We evaluated the proposed hardware mechanism on a dataflow model, and the results show that our mechanism increases the average computational performance by 15.9%, with an increase in the average utilization of functional units by 15.6%. Crucially, our approach results in relatively small increase in the area and power consumption of less than 1%. In conclusion, the evaluation results suggest that our approach is an effective improvement for the efficiency of data transfer in dataflow processors.