Reviewerly/exHarmony huggingface dataset:
Integrated Algorithm for Unsupervised Data Clustering Problems in Data Mining

Unsupervised data clustering investigation is a standout amongst the most valuable apparatuses and an enlightening undertaking in data mining that looks to characterize homogeneous gatherings of articles depending on likeness and is utilized in numerous applications. One of the key issues in data mining is clustering data that have pulled in much consideration. One of the famous clustering algorithms is K-means clustering that has been effectively connected to numerous issues. Scientists recommended enhancing the nature of K-means, optimization algorithms were hybridized. In this paper, a heuristic calculation, Lion Optimization Algorithm (LOA), and Genetic Algorithm (GA) were adjusted for K-Means data clustering by altering the fundamental parameters of LOA calculation, which is propelled from the characteristic enlivened calculations. The uncommon way of life of lions and their participation attributes has been the essential inspiration for the advancement of this improvement calculation. The GA is utilized when it is required to reallocate the clusters using the genetic operators, crossover, and mutation. The outcomes of the examination of this calculation mirror the capacity of this methodology in clustering examination on the number of benchmark datasets from UCI Machine Learning Repository.
Efficient Handling of Incomplete basic Partitions by Spectral Greedy K-Means Consensus Clustering

Cluster ensemble approaches are combining different clustering results into single partitions. To enhance the quality of single partitions, this paper examines a comparative study of different methods with advantage and drawbacks. Performing spectral ensemble cluster (SEC) via weighted k-means are not efficient to handle incomplete basic partitions and big data problems. To overcome the problems in SEC, Greedy k-means consensus clustering is combined with SEC. By solving the above challenges, named spectral greedy k-means consensus clustering (SGKCC) is proposed. The proposed SGKCC efficient to handle incomplete basic partitions in big data which enhance the quality of single partition. Extensive evaluation NMI and RI used to calculate the performance efficiency compared with existing approach proving the result of proposed algorithm.
Path Planning for Mobile Robot Using Improved Adaptive Rapidly-exploring Random Tree

Path planning based on RRT has achieved great achievements in a complex and high dimensional environment. RRT algorithms require the user to choose an appropriate stepsize and bias probability before path planning, however, selecting algorithm parameters suitable for various environments is not only difficult but also time consuming. An improved adaptive RRT algorithm is proposed in this paper and has two notable features. Firstly, it can automatically determine the initial range of stepsize and bias probability according to the relative complexity of the robots working environment, which is ever highly problem dependent and time-consuming process. Secondly, it can automatically adjust these two parameters on the basis of the collision detection result as the iteration continue. Various numerical experiments are demonstrated and validated to illustrate the effectiveness and obvious advantages of the proposed algorithm over the basic RRT algorithm under different environmental conditions.
Bayesian Hierachical Clustering for Bidikmisi Environment: Results of successful and unsuccessful scholarship cluster

Abstract This research focuses on conducting Bidikmisi data clusters. Group analysis or cluster analysis is one of the multivariate techniques used with the aim of classifying an observation that has certain characteristics. This analysis groups observations into a group so that in one group there are similarities or homogeneity with each other. In contrast, intergroup is expected to have differences or heterogeneity. The clustering process uses the Bayesian Hierachical Clustering (BHC) approach, aiming to present a complete taxonomy of grouping evaluation measurements that will be used for empirical studies.
A Service Selection Method Based on Ordinal Classification for Historical Records

The goal of service selection is to select services that satisfy user’s requirements from candidate services with the same function and different qualities of service (QoS). Traditional service selection methods require users to provide the weight for each QoS attribute, but users sometimes cannot provide accurate weights in practice. Moreover, because service providers may be untrustworthy, they may not provide reliable QoS values. Under these situations, the traditional service selection methods will be not very effective. To address this problem, we propose a service selection method based on ordinal classification for historical records. In this method, both QoS attributes and user-given ratings in historical records are considered as ordinal values, and an ordinal classification model will be learned from these data. For the historical records without user-given ratings, their ratings can be predicted by this model. Finally the services will be selected based on these ratings. The proposed method can work well even without the QoS attribute weights provided by user and the QoS values provided by service provider. We compare the proposed method with three weighted-based service selection methods and eight classification-based methods, which demonstrates that the proposed method can obtain better service selection results and have the best robustness.
An ontology for sharing and managing information in disaster response: An illustrative case study of flood evacuation

Abstract Information management and sharing is an essential ingredient, but a difficult and challenging problem for disaster response management. This paper proposes an ontology as a model to organize and structure information in order to improve the information management and sharing in disaster response management. The ontology was designed and developed based on philosophically grounded foundational ontologies. It was also implemented in ontological languages and demonstrated and evaluated in a case study of the flood evacuation process. This paper also provides a systematic approach to develop a well-founded domain ontology that addresses both static and dynamic aspects of a given domain.
The discursive construction of the moral and legal statuses of fansubbers in the Chinese press 2006–2018

Fansubbers (short for “fan subtitlers”) are confronted with a moral-legal dilemma in China. On the one hand, they are highly appreciated by the general public because they work without pay and altruistically provide free access to subtitled audiovisual contents that are otherwise unavailable. On the other hand, they distribute unlicensed contents and violate the copyright law in China. Their practices cause damages to copyright holders and thus call for legal actions. Given the conflicting moral and legal statuses, it is necessary to understand how the Chinese press frame and represent fansubbers, as the media play a crucial role in shaping people’s opinions. Drawing on a one-million-word corpus of news reports between 2006 and 2018, this study examines the media frames, discourse topics, and representations about fansubbers in China. Based on keyword, collocation, and concordance analyses, four discourse topics were identified: sharing, volunteerism, copyright, and commercialization. The former two pointed to a moral frame, while the latter two fed into a legal frame. The diachronic analysis demonstrated that the topic of sharing was most prominent in eight of 13 years, while the topic of copyright spiked about every five years. The corpus findings also unravel the complexities of discursive strategies in two aspects: (a) representational strategies work in combination to selectively frame fansubbers and (b) the same discursive strategies can simultaneously moralize and delegitimize fansubbers. This study sheds light on the multifaceted, dynamic, and complex nature of media representations about fansubbers with a conflicting social position.
Automatic Speech Recognition of Quechua Language Using HMM Toolkit

In this paper, we present the implementation of an Automatic Speech Recognition system (ASR) for southern Quechua language. The software can recognize both continuous speech and isolated words. The ASR was developed using Hidden Markov Model Toolkit (HTK) and the corpus collected by Siminchikkunarayku. A dictionary provides the system with a mapping of vocabulary words to sequences of phonemes; the audio files were processed to extract the speech feature vectors (MFCC) and then, the acoustic model was trained using the MFCC files until its convergence. The paper also describes a detailed architecture of an ASR system developed using HTK library modules and tools. The ASR was tested using the audios recorded by volunteers obtaining a 12.70% word error rate.
Decision Tree Algorithms for Accurate Prediction of Movie Rating

In this paper, we aim to find an accurate algorithm for implementing information mining systems utilizing Weka tool to foresee the achievement or disappointment of motion pictures dependent on a few important qualities with respect to system by systematically analyzing decision tree algorithms. The weightage for each attribute is calculated in the first stage, and then weightage of that movie is calculated by combined calculation of attributes using decision tree algorithms. Here we are trying to implement this idea on mainly three decision tree algorithms. J48 algorithm, Random Forest, Hoeffiding tree. Here, we try to find key factors for a profitable movie. This model assists with discovering the rating of the upcoming motion picture through qualities or attributes of that movie.
Vision to Language: Captioning Images using Deep Learning

The challenging problem of image captioning and its solution has been approached using an amalgam of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The results obtained using this Encoder-Decoder architecture have proven to be quite satisfactory. This work attempts to study the comparative performance of various encoder architectures for image captioning under CNN-RNN background. We show here the various BLEU scores evaluated using three different experimental settings for the encoder part. Firstly, the encoder used is the VGGNet [1] model which was pre-trained for ILSVRC-2012 [2], followed by ResNet [3] model for ILSVRC-2015 and finally the complex yet less expensive InceptionV3 [7] model.
Specificity of editorial and publishing training of electronic textbooks for the New Ukrainian School

The article is based on the analysis of ten electronic textbooks for the first grade of the New Ukrainian Schools. It was put to the competition by the publishers and presented in the electronic library of the Institute for the Modernization of the Content of Education (four textbooks «I am exploring the world», three textbooks in art, two electronic and one electronic textbook, textbook «Ukrainian Language. ABC Book. Parts 1 and 2») two models of the editorial and publishing preparation of projects of multimedia interactive electronic textbooks are defined and described. The first is «outsourcing model» which means involvement a technology company or multimedia publishing house in the creating of electronic textbooks. The second is «standalone model» which means independent creation of multimedia electronic textbooks by specialists of traditional publishing who use the specialized third-party software. It’s concluded that the market for interactive multimedia electronic textbooks can develop in several directions: more publishers of printed textbooks enter the market (in case if technological platforms that are available for Ukrainian publishers on the criterion «price – quality» and software for interactive programming will be provided) or filling of the niche of this market by tech companies and digital multimedia publishers (the main example is multimedia publishing house «Розумники» («A Clever Ones»)).
Fast Universal Adversarial Perturbation

Deep neural networks are known to be vulnerable to the attack of very small perturbation vectors. One recent method, named universal adversarial perturbation (UAP), is able to generate a single image-agnostic single adversarial perturbation vector commonly applicable to all images in a given data set at one time. However, the computational complexity of UAP increases with the size of data set, thus being very slow to work, especially for large datasets. In this paper, we proposed a fast UAP method, which significantly improves the former's efficiency. Particularly, the proposed method generates a universal perturbation in a mini-batch way with respect to a certain deep classifier, based on multiDeepFool, a newly proposed method that computes an adversarial example for a batch of inputs. The experiments show that our fast UAP is more efficient than the previous UAP algorithm with the nearly same fooling ratio.
A Configurable Dual-Mode PRINCE Cipher with Security Aware Pipelining in 65nm for High Throughput Applications

A dual-mode PRINCE encryption cipher is implemented, in 65nm technology, that is configurable between pipelined and fully unrolled modes. Correlation Power and EM analysis on test-chip measurements show minimal exploitability of leakage from intermediate registers in pipelined mode. The pipelined PRINCE designs are seen to exhibit similar side channel resistance to unrolled designs while providing higher frequency and throughput. The overall dual-mode system has a minimum MTD of 460K and a projected maximum throughput of 492 Mega encryptions per second.
Comment on Verification of deterministic solar forecasts: Choice of models, and estimation procedure

• Solar forecasts have been calculated using physics-based and statistical models, and sky imagery. • Statistical models and sky imagery work well over short to intermediate horizons. • Physics models are more accurate over longer horizons (beyond a few hours). • The relative performance of models is also affected by the estimation procedure.
Combining attribute content and label information for categorical data ensemble clustering

Ensemble clustering has been attracting increasing attention in recent years, because it is able to combine multiple base clusterings (ensemble members) into a more robust clustering. It mainly consists of two parts, generating multiple ensemble members and finding a final partition. The construction of the information matrix plays an important role for finding a final partition. In general categorical data ensemble clustering framework, most existing information matrices are constructed only relying on label information of ensemble members without considering original information of data sets. To solve this problem, a new ensemble clustering framework for categorical data is proposed, in which the information matrix considers label information and original data information together, and is instantiated into the ALM matrix in this paper. The ALM matrix takes account of not only the distribution of attribute content in each ensemble member, but also the relationship among ensemble members based on the distribution. To simplicity, the k-means technique is used to cluster the ALM matrix and form a new ensemble clustering algorithm. The experimental results have shown the benefits of the ALM matrix by comparing the proposed algorithm with other ensemble clustering algorithms.
Eyeing the patterns: Data visualization using doubly-seriated color heatmaps

In current times, data are being generated at an ever-increasing rate and researchers are challenged to extract meaningful information from this large volume of data. The information extraction can be facilitated by visualization of data, which can be invaluable for deepening insights about trends and exceptions and may facilitate the discovery of patterns, outliers, or other anomalies or characteristics. These visual depictions are provided using various tools, including heatmaps. Heatmaps are matrices of colored cells that represent underlying numeric data. In a typical heatmap, each row represents an object, each column represents a condition, time point, instance, or other property, and the color of each cell indicates the associated data value. However, observations of randomly ordered data are rarely enlightening and rearrangement of rows and columns into clusters of similar data has proven to be of great value. A number of approaches have been developed to tackle this combinatorial problem, including Bond Energy Algorithm, the Traveling Salesman Problem Model, TSP + k, and Hierarchical Clustering. Identifying optimal solutions for the first three of these approaches is NP-hard and the fourth requires exponential computation time. Despite these computational demands, optimality is sometimes pursued. However, approximate algorithms have been developed to address the need for more efficient tools. These approximation techniques are widely varied in both computation time and quality of results. Importantly, even for optimal solvers, these approaches carry assumptions and biases, some of which are quite subtle and commonly overlooked, yet may impact results in significant ways. In short, the choice of rearrangement method should be mindful of the particular characteristics of the data at hand. Another issue in heatmap construction is the failure to properly preprocess data prior to rearrangement. In this chapter, we summarize the history of heatmaps, scrutinize various aspects of data preprocessing, and then examine several algorithms to reorder data rows and columns such that similar data are clustered together. More specifically, we review Bond Energy Algorithm, the Traveling Salesman Problem Model, TSP + k, and Hierarchical Clustering, noting assumptions, strengths, and weaknesses of each approach. This chapter concludes with thoughts for potential future directions of heatmap research.
A Reputation Model Considering Buyer Subjectivity and Product Return Behavior

With the popularity of e-marketplace, the reputation system has received great attention. For accurately evaluating the trustworthiness of sellers in their transactions, a new computational reputation model is proposed, which considers the buyer credibility. The credibility of a buyer is further determined by the buyer's subjectivity and his/her behavior in returning products. The buyer subjectivity in our paper not only reflects the difference between the buyer's rating and the average rating, but also reflects the fluctuation of the ratings provided by the buyer himself/herself. The product return behavior can directly indicate whether the buyer is satisfied with the goods they purchased or not. It has been shown that the profit-maximized equilibrium in the proposed system can be achieved, where sellers behave honestly in providing products and buyers not return the received products. Theoretical analysis and simulation based experiments have been conducted to demonstrate that the proposed reputation model can well evaluate the sellers' behavior and the proposed system is robust against typical attacking behaviors.
EAST: Encoding-Aware Sparse Training for Deep Memory Compression of ConvNets

The implementation of Deep Convolutional Neural Networks (ConvNets) on tiny end-nodes with limited non-volatile memory space calls for smart compression strategies capable of shrinking the footprint yet preserving predictive accuracy. There exist a number of strategies for this purpose, from those that play with the topology of the model or the arithmetic precision, e.g. pruning and quantization, to those that operate a model agnostic compression, e.g. weight encoding. The tighter the memory constraint, the higher the probability that these techniques alone cannot meet the requirement, hence more awareness and cooperation across different optimizations become mandatory. This work addresses the issue by introducing EAST, Encoding-Aware Sparse Training, a novel memory-constrained training procedure that leads quantized ConvNets towards deep memory compression. EAST implements an adaptive group pruning designed to maximize the compression rate of the weight encoding scheme (the LZ4 algorithm in this work). If compared to existing methods, EAST meets the memory constraint with lower sparsity, hence ensuring higher accuracy. Results conducted on a state-of-the-art ConvNet (ResNet-9) deployed on a low-power microcontroller (ARM Cortex-M4) validate the proposal.
A Real-Time Filtering Approach for Plot-Sequences Outputted by Multi - Frame Track - Before- Detection

This paper addresses the real-time filtering problem respect to the multi-frame track-before-detection (MF - TBD). Instead of making detections on each single frame, MF - TBD jointly processes multiple raw data frames at each time, which can remain and utilize more target information. Thus, different from the conventional detection procedure which provides single point plot to a tracker, output of MF - TBD is plot-sequence. However, for batch processing MF - TBD, there are repetitive parts (named plot-sets) under a number of consecutive batches in time series while the correlation among each plot in the plot-set is complex and unpractical to computation. In this paper, by avoiding the computation of correlation, a novel filtering approach is developed for the plot-sequences outputted by batch processing MF - TBD. Last, simulation results show that the proposed algorithm can correctly estimate target trajectories and significantly enhance the tracking accuracy compared with the MF - TBD without our filtering algorithm.
Multi-frame Track-before-detect Algorithm for Passive Sonar System

It has been widely verified superiority of multi-frame track-before-detect (MF - TBD) for weak-target detection and tracking. In this paper we apply MF - TBD approach to solve weak unknown and time-varying number of targets detection and tracking problem for passive sonar system with excellent concealment. MF - TBD signal processing method is exclusive developed for the passive sonar system. Compared with conventional tracking structures, MF - TBD establishes the joint estimation strategy of target kinematic states based on maximum a posteriori probability (MAP) criteria. By directly processing multi-frame unthresholded raw bearing time record (BTR) data, MF-TBD achieves energy accumulation for weak targets and improves detection performance. Finally, simulation results are performed to verify its efficiency for passive sonar system.