Reviewerly/exHarmony huggingface dataset:
Performance Analysis on Modified Hybrid DB Scan Clustering Technique To Enhance Average Total Execution Time

This paper shows security and privacy of user data stored in cloud services is of utmost concern especially for commercial public cloud platforms and service providers. The user data stored on cloud is encrypted, and it is desired that data mining operations on encrypted data must be executed efficiently. The proposed system implements a data mining operation namely clustering on encrypted data on cloud platforms using TCP / IP protocol. The clustering operation is performed using standard K-Means, Spectral, Hierarchical, Hybrid DBSCAN and simple DBSCAN clustering technique optimized for encrypted data.
A method of filtering bad text in network based on topic sensitive words tagging

Abstract Bad information filtering is conducive to creating a healthy network. A two-level filtering method based on topic and sensitive words is proposed. In the first stage, the network text is filtered by using thesaurus, by setting the weight of different topics. In the second stage, according to the frequency, position and sensitivity of sensitive words, the value of bad tendency is obtained by weighting the web text. Finally, taking the text set recognition of bad financial publicity content in the network as an example, the result proves that it can improve the efficiency and accuracy of filtering of bad investment information.
Women Empowerment Employing ICT In Pakistan

In the modern world, digital revolt has changed our way of life. Information and communication technology (ICT) is a diverse set of technological tools and resources for creating, disseminating, storing, adding value, and managing information. ICT can be a powerful catalyst for expanding women's political and social rights. Through technological capacity building, the women shall prove to be a great asset for the economic engine of the country as well. The role of women is emerging in the socio-economic, political, and physical space; therefore, their empowerment will contribute to sustainability and capacity building, which will contribute to sustainability and catastrophe risk reduction in the long run. In developing countries, especially in rural areas, women with information and communication technologies can improve their ability to cope with different situations. This study investigates the role of ICT in women empowerment, challenges of ICT use for women empowerment, the impact of women on society after ICT, and issues of ICT for women.
Modified Flower Pollination Algorithm for Function Optimization

The flower pollination algorithm (FPA) was firstly proposed in 2012 as one of the population-based metaheuristic optimization search techniques. It is conceptualized by the pollination behavior of flowering plants. In this paper, the new enhanced version of the original FPA named the modified flower pollination algorithm (MoFPA) is proposed to improve its search performance for function optimization. The switching probability of the original FPA used for selection between local and global pollinations is changed from the fixed manner to the random manner according to the pollination behavior of flowering plants in nature. To perform its effectiveness, the proposed MoFPA is tested against ten standard benchmark functions compared with the original FPA. As simulation results, it was found that the proposed MoFPA performs superior search performance for function optimization to the original FPA with higher success rates and faster search time consumed.
Annotation System to Build Cyberbullying and Hate Speech Detection Model Training Dataset

During 2019, Indonesian people experienced the election period which triggers many hate speech and cyberbullying cases on Twitter. A detection tool to screen social media data can be used to avoid the spread of negative content. A supervised machine learning approach can be used to build this detection tool. However, it needs thousands of labeled data to develop the machine learning model with high accuracy. In the current study phase, an annotation system was proposed to help the researchers to label Twitter raw data collected in the previous phase. An open-source tool was utilized to build a user-friendly web-based system. Three main features are proposed including multi labels annotation, multi-users validation, and dashboard page. This system can help the annotators to perform labeling task for thousands of text data.
Lightweight Network for Vietnamese Landmark Recognition based on Knowledge Distillation

In our modern world, smart devices, e.g., mobile phones, IoT devices, have become the norm, leading to a vast increase in demand for a smart-ecosystem. Among other technologies that are being researched and applied, there is a trend of embedding Artificial Intelligence modules on these devices. One of the most challenging problems for embedding on smart devices is maintaining good accuracy while reducing the computational cost and speed. State-of-the-art Deep Convolution Neural Networks cannot run on smart devices due to a lack of resources. The need to find such a model is the motivation for our proposal of a lightweight network for landmark recognition using knowledge distillation. Our purpose is not to create a network with higher accuracy; instead, we try to devise a fast and light neural network while keeping approximately similar accuracy of SOTA models by utilizing knowledge distillation. Our proposed student model achieves a decent result with 7.33% accuracy lower than the teacher SOTA model (91.8%), while decreases the processing time by 73.04%. Our experimental results show promising potential for further explorations and research in knowledge distillation. We have also collected a dataset for Vietnam landmarks for our experiments. This data can be used to train a similar network for Vietnam landmarks recognition or other related purposes.
Gesture Recognition for Note Generation in VR Rhythm Game

This paper describes a gesture recognition algorithm for note generation in virtual reality (VR) rhythm game. VR rhythm game is a game in which a user has to move according to the rhythm or music in a virtual environment and usually takes the form of hitting objects, called notes, flying to the user with hands or tools. Notes need be generated to match the rhythm in the game. However, manual generation is often a time-consuming and tedious work. Previous research has focused mainly on the development of algorithms that automatically generate notes from music. However, notes made according to music can improve a sense of rhythm, but they do not have spatial characteristics, so it is difficult to induce spatially meaningful movements. In order to improve the drawback, we propose a method for note generation in a VR rhythm game from a dance consisting of hand taps. Dynamic Time Warning (DTW) method was applied to recognize the hand taps and determine the point at which to position the note. The experimental results show that our method accurately recognizes hand tap gestures and their location, and generates notes accordingly.
Classifying Automated Programming Contracts Using TanH2 Decision Tree Classifier

The process of drafting software coding contracts with only a slight intermediation by the developers remains pivotal in systematically coming up with optimized assertions (contracts) rendering extensive and enriched testing of software modules. The framework put forward begins with shaping up of the behavior and structural relevant dependency specifications as conditions or restraints on a decision tree and thereafter materializes them into automated coding contracts. Contract refining nuances are hence adopted in order to leverage on enhanced feasible assertions. This is exercised by customized K-means clustering algorithm to build accurate clusters of contracts analogous to each other. The proposed classification of isolated contracts is then made by adapting activation function of neural networks to compute entropy during decision tree classifier construction as a TanH2 entropy classifier tree method with improved memory and computation relevant performances and adequate effectiveness of faults exposure ability.
Volume Measuring Method for Forming Additive Manufacturing Based on SGBM

For volume measurement problems in the processes of additive manufacturing, a volume measurement method is proposed through using the SGBM algorithm in this paper. In this method, the internal and external parameters of the camera are calibrated by the binocular vision system. And the image processes by SGBM are performed for the image acquired by binocular stereo vision system. The processes are including filtering, correction and stereo matching to obtain the disparity information of the object to be tested in the left and right cameras. The volume of the work-piece to be tested can be obtained by the calculation that measured the three-dimensional coordinate information through the disparity information. The experimental results illustrate that the method is of certain reliability and accuracy for volume measurement.
A Detection System for the Elderly based on Embedded Systems

As a result of ageing of population, more and more old people live alone. It is dangerous for the elderly to fall at home. A fall detection system for the elderly based on embedded systems was been studied. The system is integrated by acceleration sensors, Beidou positioning modules and communication modules. These modules cooperate with each other and can be used to monitor the user's posture in real time. The Signal Magnitude Vector algorithm (SMV) is used to distinguish whether the elderly fell. If fall was happened, the user's location information will be automatically sent to the emergency contact, and an alarm sounds. The system is easy to implement and has the advantages of high recognition rate and convenient portability.
Set Operation Aided Network for Action Units Detection

As a large number of parameters exist in deepmodel based methods, training such models usually requires many fully AU-annotated facial images. This is true with regard to the number of frames in two widely used datasets: BP4D[31] and DISFA [18], while those frames were captured from a small number of subjects (41, 27 respectively). This is problematic, as subjects produce highly consistent facial muscle movements, adding more frames per subject would only adds more close points in the feature space, and thus the classifier does not benefit from those extra frames. Data augmentation methods can be applied to alleviate the problem to a certain degree, but they fail to augment new subjects. We propose a novel Set Operation Aided Network (SO-Net) for action units detection. Specifically, new features and the corresponding labels are generated by adding set operations to both the feature and label spaces. The generated new features can be treated as a representation of a hypothetical image. As a result, we can implicitly obtain training examples beyond what was originally observed in the dataset. Therefore, the deep model is forced to learn subject-independent features, and is generalizable to unseen subjects. SO-Net is end-to-end trainable, and can be flexibly plugged in any CNN model during training. We evaluate the proposed method on two public datasets, BP4D and DISFA. The experiment shows a state-of-the-art performance, demonstrating the effectiveness of the proposed method.
Adaptive Graph Learning for Semi-supervised Classification of GCNs

Graph convolutional networks (GCNs) have achieved great success in social networks and other aspects. However, existing GCN methods generally require a wealth of domain knowledge to obtain the data graph, which cannot guarantee that the graph is suitable. In this paper, we propose adaptive graph learning for semi-supervised classification of GCNs. Firstly, the hypergraph is used to establish the initial neighborhood relationship between data. Then hypergraph, sparse learning and adaptive graph are integrated into a framework. Finally, the suitable graph is obtained, which is inputted into GCN for semi-supervised learning. The experimental results of multi-type datasets show that our method is superior to other comparison algorithms in classification tasks.
Kinematics and dynamics analysis of lower limbs based on human motion data

For patients with an aging population and patients suffering from movement disorders, the combination of robotic systems and biomedical engineering principles has brought good news to the majority of patients. The analysis of human gait laws can help better develop and research robotic systems. Therefore, this article is based on the idea of using the gait trajectory of a healthy person as the input reference trajectory of the robot, using NOKOV three-dimensional infrared motion capture equipment and three-dimensional force measurement platform equipment to collect human gait movement data and plantar force data, and analyze the collected data Kinematics and dynamics characteristics of human body data.
Design to Eat Smart

Seemingly trivial eating habits, such as eating too fast, has been linked to diverse and rather serious health issues. While some technology-mediated interventions have promoted healthy dietary habits, designing successful pervasive interventions remains challenging. This paper contributes to the design of eating interventions in three ways: 1) a comprehensive review of 62 studies which focused on interventions targeting eating habits; 2) a generative design framework with multiple design parameters; 3) and, an exploration of the potential efficacy of the developed framework. These contributions will improve designers' comprehension and ability to apply current trends and state of art technologies in the design space. The work presented here aims to validate the generative nature of our design framework presented in this paper and further propose new exploratory directions.
Research on Incentive Utility Multi-attribute decision-making Model and its Application

This paper puts forward the concept of incentive utility, which converts the value of each attribute in multi-attribute decision-making into a unified index for measurement, thus simplifying multi-attribute decision-making. In this paper, a comprehensive evaluation model based on the TOPSIS of incentive utility is established. Through the establishment of incentive index, the utility of incentive and the calculation of ideal point of utility, the disadvantage of "ideal point is not ideal" in the TOPSIS is overcome. It can better synthesize the sub-attribute value and realize the subjective multi-attribute decision-making. The example shows that the model can apply subjective factors to decision-making scientifically, and the result is more reasonable and effective.
Security Analysis of a Multi-secret Sharing Scheme with Unconditional Security

Harn has introduced a (t, n) threshold secret sharing scheme recently, in which shareholders’ shares are not disclosed in the secret reconstruction phase. The benefit is that the outside adversary cannot learn the secret even if it is recovered by more than t shareholders. Moreover, Harn has further extended this scheme into a multi-secret sharing scheme so that multiple secrets can be recovered individually at different stagies. Both schemes are claimed to achieve the perfectness property using heuristic arguments. However, in this paper, we show that the above claim is false and these schemes are not perfect. In the first scheme, the coalition of $$t-1$$ shareholders can conclude that the secret is not uniformly distributed. And in the multi-secret sharing scheme, when the public parameters satisfy some special conditions, the coalition of $$t-1$$ shareholders can even use the recovered secrets to preclude some possible values for the unrecovered secrets.
Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy

Brains with complex distortion of cerebral anatomy present several challenges to automatic tissue segmentation methods of T1-weighted MR images. First, the very high variability in the morphology of the tissues can be incompatible with the prior knowledge embedded within the algorithms. Second, the availability of MR images of distorted brains is very scarce, so the methods in the literature have not addressed such cases so far. In this work, we present the first evaluation of state-of-the-art automatic tissue segmentation pipelines on T1-weighted images of brains with different severity of congenital or acquired brain distortion. We compare traditional pipelines and a deep learning model, i.e. a 3D U-Net trained on normal-appearing brains. Unsurprisingly, traditional pipelines completely fail to segment the tissues with strong anatomical distortion. Surprisingly, the 3D U-Net provides useful segmentations that can be a valuable starting point for manual refinement by experts/neuroradiologists.
Cloud interoperability based on a generic cloud service description: Mapping OWL-S to GCSD

The lack of standardized service descriptions in cloud environments causes the vendor lock-in, which hinders cloud service interoperability. Approaches like standards, brokers, and semantic technologies were proposed in the literature to enable cloud service interoperability. However, these propositions are specific to a certain technology or a particular cloud model, or they consider a specific cloud actor, which makes them restricted and not generic. In this paper, we discuss the importance of Cloud Service Description (CSD) standardization to enable cloud service interoperability. We propose a novel service interoperability model based on a generic CSD model (GCSD). The proposed transformation mediator uses mapping rules to transform heterogeneous cloud service descriptions into the GCSD uniform description. Consequently, it makes them interoperable. This work is in its early stages. Therefore, it covers only the transformation from OWL-S to the GCSD. Eventually, we provide use cases to illustrate the transformations from OWL-S to the GCSD.
Lazy Search Trees

We introduce the lazy search tree data structure. The lazy search tree is a comparison-based data structure on the pointer machine that supports order-based operations such as rank, select, membership, predecessor, successor, minimum, and maximum while providing dynamic operations insert, delete, change-key, split, and merge. We analyze the performance of our data structure based on a partition of current elements into a set of gaps Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> based on rank. A query falls into a particular gap and splits the gap into two new gaps at a rank r associated with the query operation. If we define B=Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> |Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> |log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (n/|Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> |), our performance over a sequence of n insertions and q distinct queries is O(B+min(n log log n, n log q)). We show B is a lower bound. Effectively, we reduce the insertion time of binary search trees from Θ(log n) to O(min(log(n/|Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> |)+ log log|Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> |, log q)), where Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> is the gap in which the inserted element falls. Over a sequence of n insertions and q queries, a time bound of O(n log q+q log n) holds; better bounds are possible when queries are non-uniformly distributed. As an extreme case of non-uniformity, if all queries are for the minimum element, the lazy search tree performs as a priority queue with O(log log n) time insert and decrease-key operations. The same data structure supports queries for any rank, interpolating between binary search trees and efficient priority queues. Lazy search trees can be implemented to operate mostly on arrays, requiring only O(min(q, n)) pointers, suggesting smaller memory footprint, better constant factors, and better cache performance compared to many existing efficient priority queues or binary search trees. Via direct reduction, our data structure also supports the efficient access theorems of the splay tree, providing a powerful data structure for non-uniform element access, both when the number of accesses is small and large.
Quantum approximate optimization and k-means algorithms for data clustering

Abstract Noisy intermediate-scale quantum (NISQ) devices are cutting-edge technology expected to demonstrate potential and advantages of quantum computing over classical computing. Its low number of qubits and imperfection from noises restrict running full-scale quantum algorithms on such devices; however, quantum advantages can still be obtained. To achieve quantum advantages from NISQ devices, the hybrid quantum-classical algorithms were introduced. Quantum approximate optimization algorithm (QAOA) is a variational hybrid algorithm, which utilizes a NISQ device as a sub-unit for specific tasks and performs most calculations on a classical computer. QAOA provides an approximate solution, with arbitrary precision as the number of operations increases, for optimization problems. In this work we investigate the possibility of applying QAOA to a clustering problem and compare its performance with the classical k-means algorithm. It turns out that the weights in graph connectivity can degrade the algorithm operation and make it more difficult to approximate the solution. We also benchmark the QAOA by comparing the approximated solutions with the exact one obtained from a classical clustering algorithm.
On the Tradeoff between Computation-Time and Learning-Accuracy in GAN-based Super-Resolution Deep Learning

The trade-off between accuracy and computation should be considered when applying generative adversarial network (GAN)-based image generation to real-world applications. This paper presents a simple yet efficient method based on Progressive Growing of GANs (PGGAN) to exploit the trade-off for image generation. The scheme is evaluated using the LSUN dataset.