Reviewerly/exHarmony huggingface dataset:
Dramatically Reducing Search for High Utility Sequential Patterns by Maintaining Candidate Lists

A ubiquitous challenge throughout all areas of data mining, particularly in the mining of frequent patterns in large databases, is centered on the necessity to reduce the time and space required to perform the search. The extent of this reduction proportionally facilitates the ability to identify patterns of interest. High utility sequential pattern mining (HUSPM) seeks to identify frequent patterns that are (1) sequential in nature and (2) hold a significant magnitude of utility in a sequence database, by considering the aspect of item value or importance. While traditional sequential pattern mining relies on the downward closure property to significantly reduce the required search space, with HUSPM, this property does not hold. To address this drawback, an approach is proposed that establishes a tight upper bound on the utility of future candidate sequential patterns by maintaining a list of items that are deemed potential candidates for concatenation. Such candidates are provably the only items that are ever needed for any extension of a given sequential pattern or its descendants in the search tree. This list is then exploited to significantly further tighten the upper bound on the utilities of descendent patterns. An extension of this work is then proposed that significantly reduces the computational cost of updating database utilities each time a candidate item is removed from the list, resulting in a massive reduction in the number of candidate sequential patterns that need to be generated in the search. Sequential pattern mining methods implementing these new techniques for bound reduction and further candidate list reduction are demonstrated via the introduction of the CRUSP and CRUSPPivot algorithms, respectively. Validation of the techniques was conducted on six public datasets. Tests show that use of the CRUSP algorithm results in a significant reduction in the overall number of candidate sequential patterns that need to be considered, and subsequently a significant reduction in run time, when compared to the current state of the art in bounding techniques. When employing the CRUSPPivot algorithm, the further reduction in the size of the search space was found to be dramatic, with the reduction in run time found to be dramatic to moderate, depending on the dataset. Demonstrating the practical significance of the work, experiments showed that time required for one particularly complex dataset was reduced from many hours to less than one minute.
Reconstruction of Compressively Sensed Images using Regularized Sparse Dictionary Learning and Adaptive Spectral Filtering

Sparse representation using over-complete dictionaries have shown to produce good quality results in various image processing tasks. Dictionary learning algorithms have made it possible to engineer data-adaptive dictionaries that have promising applications in image compression and image enhancement. The most common sparse dictionary learning algorithms use the techniques of matching pursuit and K-SVD iteratively for sparse coding and dictionary learning respectively. While this technique produces good results, it requires a large number of iterations to converge to an optimal solution. In this article, we use a closed-form stabilized convex optimization technique for both sparse coding and dictionary learning. The approach results in providing the best possible dictionary and the sparsest representation resulting in minimum reconstruction error. We have used the proposed algorithm for compressed sensing of satellite images. Once the image is reconstructed from the compressively sensed samples, we use adaptive spatial and frequency domain filtering techniques to move towards exact image recovery. It is seen from the results that the proposed algorithm provides much better reconstruction results than conventional sparse dictionary techniques for a fixed number of iterations. Depending upon the number of details present in the image, the proposed algorithm is seen to reach the optimal solution with a significantly lower number of iterations. Consequently, high PSNR and low MSE is obtained using the proposed algorithm for our compressive sensing framework.
Analysis of Initial Value Effect of Differential Chaotic Shift Keying Communication System

Abstract Chaotic signals have many unique properties, chaotic digital modulation technology has many advantages over traditional digital modulation technology, so it has good application value in the field of information security. In order to analyze the differential chaotic shift keying technology more comprehensively, based on the research of the DCSK, FM-DCSK and RM-DCSK differential chaotic shift keying techniques, the MATLAB simulation platform is used to simulate the balance of the different sequences and the initial values of different chaotic keying systems are used to simulate the error performance of different systems. The experimental results show that the peak of the balance of chaotic map may lead to the peak value of the system error rate. Therefore, the peak point should be avoided when the initial value of the sequence is selected. Different digital modulation techniques and chaotic mapping systems have their own characteristics, and when the reasonable parameters are selected according to the characteristics, the system performance can be optimized.
Person Re-Identification Net of Spindle Net Fusing Facial Feature

In the field of person re-identification, the extraction of pedestrian features is mainly focused on the extraction of features from the whole pedestrian or limb torso, and the facial features are less used. The facial features is integrated into the network to enhance pedestrian recognition accuracy rate. By introducing the MTCNN facial extraction network in the framework of person re-identification network Spindle Net, and improves the accuracy of person re-identification by improving the weight of facial features in the overall pedestrian characteristics. The experimental results show that the accuracy of Rank-1 on the CUHK01, CUHK03, VIPeR, PRID, i-LIDS, and 3DPeS data sets is 7% higher than that of Spindle Net.
On Blended Learning Mode of Comprehensive English Based on MOOC

As an important open educational resource, MOOC provides a kind of new teaching mode and learning method for teachers and students, and integrates online education with traditional education. The blended learning mode of comprehensive English based on MOOC has realized the construction of teaching mode of pre-class learning, face-to-face teaching and after-class learning. This model can fully mobilize students’ learning enthusiasm, cultivate students’ autonomous learning ability, strengthen the role of teachers in teaching guidance, and effectively improve the creativity and effectiveness of comprehensive English teaching activities for college English majors.
A Study of New Students' Perceptions and Expectations Major in Physical Education of Graduate School of Education

The purposes of this study were to find out the reasons of the admission into a graduate school of education majoring in physical education (PE) and expectations of graduate school life and studying. For the purpose of this study, an in-depth interview was conducted with five new master’s students of G university located in Kyunggido Province, Korea. As a result of this study, first, the new comers mostly they entered the program not only their intentions to be a PE teacher but also their parents’ supports. Second, they expected for the quality school curriculum and instructions, they also expected their senior colleagues’ positive inspirations. Third, they also wanted to become a good certified teacher in the future. And, fourth, they clearly all were going to take the teacher certification examinations to be a physical education teacher, but they knew how difficult to pass the exam so they really planned to study not only during the program but also later even after the graduation to become the PE teacher.
Method of Structural Annotations Creation

The research is dedicated to extracting the semantics from nature language texts applying the big data analysis. In the research, the authors focus on the solution of the particular task of text processing. In the current article authors would like to introduce a new original method of extracting semantics from text in natural language as a completed result of 4-years research. The method was implemented for texts in Russian, but there is a simple way to adapt it for other languages. The method allows working with any text got from any source including html pages. Also the method is suitable for processing linked data or any other type of the data having RDF formatting like triples “object - predicate - subject”. The main idea of the method is to create a syntactic tree - structured annotations - for any sentence in the text, then create logical links between these trees. After that resulting trees are uploaded to DBMS for the future processing and getting results. As DBMS we use the specialized system ORM Polar. We'd chosen it because of its fast data processing and great ability of system scaling.
Against 'Sentiment'

The position of this paper is that sentiment analysis lacks construct validity: it systematically fails to measure emotions in text. This position is elaborated through theoretical examination of the existing approach, which is shown to allow only rough correlates of emotion confounded with other properties of texts. Crude correlates are regarded by this critique as unacceptable in the absence of a theory capable of guiding their improvement. An alternative statistical approach for analysis of sentiment in text is proposed, along with discussion of how it would need to be developed.
Software development and hardware means of hidden usb-keylogger devices identification

Abstract Identifying keylogger is a simple task implemented by analyzing program code and identifying suspicious activity. The keylogger identification made as a technical device is a difficult operation in connection with hiding the electromagnetic fields of such devices. Therefore, the implementation of a firmware module for detecting hidden USB-keylogger devices is very relevant at present. The article presents the technical modules and block diagram of the developed device. An algorithm for detecting hidden devices is demonstrated and the analysis of the device operation is done. Conclusions and recommendations for identifying hidden keylogger devices were formulated.
Optimizing GZIP compression accelerator for Zynq FPGAs

In this paper we present the architecture and design optimizations brought to an FPGA GZIP compressor we previously designed. The initial design could reach a maximum throughput of 1.34 Gbps on a I'irtexb FPGA while the optimized implementation can reach up to 1.84 Gbps using a Zynq-7020 FPGA. The hardware compressor is up to 8x times faster than the software version running on an ARM CPU that has a clock frequency 6. 7x times higher.
Detection of Concept-Drift for Clustering Time-Changing Categorical Data: An Optimal Method for Large Datasets

Finding optimal method for clustering of time-changing categorical data in large datasets is always lagging and has attracted significant interest in clustering domain. As the data in the World Wide Web has been growing exponentially, change in the concept occurs between every cluster called concept-drift. Firstly, the given dataset is divided into subsets, called sliding windows. Since the new data enters always into the database, effectiveness of the clustering method needs to be evaluated continuously and dynamically. This paper focuses on an optimal method for clustering time-changing categorical data. This method evaluates the effectiveness of the clustering process for each of new input data subset and considers the stability with respect to the previous clustering method. The proposed algorithm follows iterative procedure to obtain optimal solution of the clustering method with few constraints. Further, it detects drifting concepts from the optimal method. This paper proposes a detection of drift and the optimal method for catching the progression trend of cluster structures in time-changing categorical domain. Finally, the effectiveness of proposed algorithm is illustrated using the experimental study on KDD-CUP'99 dataset for various sizes of sliding windows.
Improvement of usability in user interfaces for massive data analysis: an empirical study

Big Data challenges the conventional way of analyzing massive data and creates the need to improve the usability of existing user interfaces (UIs) in order to deal with massive amounts of data. How the UIs facilitate the search for information and helps in the end-user’s decision-making depends on developers and designers, who have no guides for producing usable UIs. We have proposed a set of interaction patterns for designing massive data analysis UIs by studying 27 real case studies of massive data analysis. We evaluate if the proposed patterns improve the usability of the massive data analysis UIs in the context of literature search. We conducted two replications of the same controlled experiment, one with 24 undergraduate students experienced in scientific literature search and the other with eight researchers who are experienced in biomedical literature search. The experiment, which was planned as a repeated measures design, compares UIs that have been enhanced with the proposed patterns versus original UIs in terms of three response variables: effectiveness, efficiency, and satisfaction. The outcomes show that the use of interaction patterns in UIs for massive data analysis yields better and more significant effects for the three response variables, enhancing the discovery and visualization of the data. The use of the proposed interaction design patterns improves the usability of the UIs that deal with massive data. The patterns can be considered as guides for helping designers and developers to design usable UIs for massive data analysis web applications.
Truth-Conditional Cognitivism and Inferential Competence

When dealing with ʻmeaningʼ and related notions, one cannot ignore what for a long time was the dominant paradigm in semantics, namely what I shall refer to as truth-conditional cognitivism. According to this paradigm, truth-conditional formal semantics for natural language, in Montagovian or Davidsonian form, is a theory of semantic competence. In this chapter, I shall argue that truth-conditional formal semantics can account for the structural or compositional aspect of human semantic competence, i.e., the ability to understand complex linguistic expressions on the basis of the knowledge of the meanings of the component words and the way in which these words are combined. Furthermore, I shall argue that adding a set of meaning postulates to a standard Montagovian semantic system makes it possible to also model a significant portion of human lexical competence. This is the aspect that is responsible for performances such as material inference, judgments based on semantic relations, word definition, and so on (inferential competence). In the following chapter, however, I shall argue that meaning postulates are insufficient when it comes to explicating the full content of lexical competence.
Atomic Norm Minimization Methods for Continuous DOA estimation in Colored Noise

This paper aims to solve the problem of continuous direction-of-arrival (DOA) estimation, and is focused on developing grid-less sparse methods to colored noise environment. We propose a two-stage grid-less model based on noise suppression and sparse representation. Then the simplified two-dimensional atomic norm minimization algorithm is proposed to estimate parameters. We further extend a reduced-complexity algorithm to the reconstruction of difference covariance matrix. The unknown DOAs are retrieved from the recovered matrix and the source number can be obtained as a byproduct. Numerical simulations lastly validate the computational efficiency of the proposed algorithms.
Multimedia Information Retrieval

My main research interests include product search and visual question answering (VQA), lying in the field of information retrieval (IR), which aims to obtain information system resources relevant to an information need from a collection. Product search focuses on the E-commerce domain and aims to retrieve products which are not only relevant to the submitted queries but also fit users' personal preferences; Visual question answering aims to provide a natural language answer for a given image and a free-form, open-ended, natural-language question about this image, which requires semantic understanding on natural language and visual content, as well as knowledge extraction and logic reasoning.
O2O Business Personalized Recommendation Research Based on Bipartite Graph

Bipartite graph networks are widely used as an analytical method for complex networks. Aiming at the cold start problem of the traditional recommendation algorithm, this paper proposes to combine the bipartite graph recommendation algorithm with the random walk algorithm, and then add the geographical influence factor to improve the effect of O2O ecommerce personalized recommendation. The experiment was conducted by selecting 45,974 users in the Yelp data set as an example. The online review was used as a weight to construct a user-business bipartite graph network and found to have a "small world" feature. The experimental results show that the combination of the random walk algorithm can make recommendations accurately, and add the characteristics of localization, by comparing the cities of the businesses, can be more in line with the actual situation. The accuracy of the final model algorithm is improved, which verifies the effectiveness of the model.
Analysis of Innovation Activities in Aviation Industry

Innovation activities represented by Six Sigma (6σ) led to improvements not only in manufacturing industries but also in various business fields. In the aviation industry, Six Sigma has been used as a tool of innovation since the beginning of 2000, and it has developed into a comprehensive form of innovation activity that includes various improvement tools. In this study, the innovation activities in K company that is a representative company of aviation industry are summarized in the last 10 years, and the effectiveness of the innovation tools and the performance of the tasks are also analyzed. The results of 2,091 projects over the past decade have been analyzed from various perspectives. First, we found out the tools that were used frequently at each DMAIC step, showed their frequency, and analyzed the evaluation results for the project. The project was evaluated from grade 1 (highest level) to grade 7 (lowest level) with an average grade of 4.1 for the overall project. The evaluation grades of the projects were compared and analyzed in terms of the qualifications of the leader, the roadmap for the implementation of the project, the financial effect, the size of the financial effect, the business classification, and the project execution period. These results may suggest new perspectives for companies considering or adopting innovation programs.
Study of a Method for Effective Noise Suppression in Passive Personnel Screening Systems

The paper discusses approaches to increase the sensitivity of passive personnel screening systems by integrating sequential frames with a moving subject. Several state-of-the-art methods of computer vision are considered for this purpose, which can be used to track moving subjects even on very different frames. The results of experiments using the computer vision method DensePose, based on the use of artificial neural networks, are presented. Using DensePose, the segmentation of a moving subject and textural UV-coordinates for the surface model of the human body are found on frames, which are used in the described frame-by-frame integration method. Considering obtained results, the shortcomings of the proposed frame integration method are identified and listed. The directions of further research are suggested.
Detecting High Risk Zones Using a Spatial Clustering of Pipe Breaks

With limited budgets and an aging system, infrastructure managers have increasingly sought cost-effective means to evaluate asset condition. While physical inspections can provide high quality data, they can be cost prohibitive at times. Pipe replacement is also expensive and replacing pipe that is still in good condition is not fiscally efficient. As such, an alternative method is to use available information to perform a desktop analysis, estimating the current conditions by exploring past performance records to help prioritize inspection and replacement. The spatial clustering of pipe breaks is an approach to observe the location of failures and identify regions with abnormally high failure rates. A clustering analysis fits well into a wider asset management framework, where the identified clusters act as possible evidence of distress which then might be targeted for physical inspection or replacement. We present an algorithm for scanning and clustering break data on pipe networks and demonstrate its application. Empirical findings show that the identified clusters can provide a high capture rate of future failures, proving itself a useful tool for decision support.
Dynamic 3D Environment Perception Using Monocular Vision and Semantic Segmentation

This paper presents a complete system for traffic environment perception based on a single color image source. The acquired color images are segmented into road and obstacle areas using a U-Net style Convolutional Neural Network (CNN). The segmented image is mapped into a bird's-eye view using the automatically calibrated camera parameters. Obstacle scans are extracted from the bird's-eye view image, highlighting the contact points between the obstacles and the road. The scans are postprocessed to increase the connectivity between obstacle parts. The processed scans are used to generate the measurement likelihood values for all observable cells of a dynamic occupancy grid, taking into consideration the expected measurement errors with respect to the distance from the camera. A particle-based occupancy grid is used to track the environment at cell level, and then the occupied cells are grouped into individual objects. The system is able to estimate stable cuboids with measured width, length, orientation and speed for the moving individual objects such as vehicles and pedestrians, and also to identify generic occupied areas for the continuous structures such as fences or barriers.
Research on the Application of Machine Reading Comprehension in Adaptive Evaluation System

The essence of "Internet plus" is the relationship and its intelligent connection mode, which promotes the cross-border integration of information communication technology with all walks of life and reduces the information asymmetry. At a time when so much new knowledge is generated every day in the form of text that no one can digest on their own, machine reading comprehension can process vast amounts of information for people to use. With the development of psychometric theory and network technology, computerized adaptive testing has more and more advantages than traditional testing. Most of the current selection strategies are designed based on project response theory and cognitive diagnosis theory, which cannot point out students' learning ability, while cognitive diagnosis can not only evaluate students' ability, but also point out the knowledge of the subjects. This article tries to explore the application of adaptive machine reading comprehension in the measurement system, and build a machine reading comprehension model, introduces the application value of the machine in reading comprehension, secondly to build an adaptive measurement system can be used to machine reading model, finally attempts to solve through the experiment measurement system of text slices and accurately identify the problem.