Reviewerly/exHarmony huggingface dataset:
Community Feedback Analysis Using Latent Semantic Analysis (LSA) To Support Smart Government

Developing and prioritizing development planning is the main task of the government. Development priority programs carried out in government activities can have direct or indirect impacts on the community. Community participation in providing responses or feedback from activity programs can be an important contribution for the government in realizing good governance. In the city of Surabaya there is a website portal called media center that can accommodate community participation. Communities can submit complaints, suggestions, appreciation and information requests through various channels such as government websites, e-mail, telephone and social media, then their complaints are collected and published on the media center portal. This study aims to identify and analyze community feedback from official government portal using the K-means clustering and Latent Semantic Analysis (LSA) methods to produce topics of development problems that are occurring. After getting the topic then interpret it into categories according to the priorities of the development program called RKPD. In clustering using k-means clustering and LSA, the cluster formed is 17 clusters with 5 categories including public service at first position followed by infrastructure, city utilities, living environtment, transportation system and mass transport, and education service.
Anomaly Detection of Trackside Equipment Based on GPS and Image Matching

The health of trackside equipment often seriously affects the safe driving of the train, while manually checking their status is an indispensably laborious task. In order to automate the task, this paper addresses the problem of automatically and accurately detecting anomalies of equipment located along the track through GPS and image calibration techniques. Considering the unnoticeable changes in equipment, including screws missing and cable corrosion, detection by classification-based machine learning methods is difficult to implement, thus in this paper we propose and conduct a novel detection mechanism by an efficient way of image subtraction. Especially, our method consists of four steps. The first step is to collect images of the trackside equipment and their corresponding mileage in the same route several times through cameras and GPS devices installed in the inspection train. Then, by introducing an improved RANSAC algorithm, the GPS data is further corrected. Secondly, we define one pass of the collected image data along with GPS information as template, and for the rest passes that need to be detected, the first frame alignment operation is operated through GPS and image information. For the third step, the SURF algorithm is implemented for image matching, and then the subtraction operation is conducted between each matched image pairs. Finally, we use empirical filtering mechanism to remove false positives that we have detected. Experimental results demonstrate the efficiency and effectiveness of our proposed method for anomalies detection of trackside equipment.
Matlab Simulation of Algorithms for Face Detection in Video Surveillance

Face detection is an application widely used in video surveillance systems and it is the first step for subsequent applications such as monitoring and recognition. For facial detection, there are a series of algorithms that allow the face to be extracted in a video image, among which are the Viola & Jones waterfall method and the method by geometric models using the Hausdorff distance. In this article, both algorithms are theoretically analyzed and the best one is determined by efficiency and resource optimization. Considering the most common problems in the detection of faces in a video surveillance system, such as the conditions of brightness and the angle of rotation of the face, tests have been carried out in 13 different scenarios with the best theoretically analyzed algorithm and its combination with another algorithm The images obtained, using a digital camera in the 13 scenarios, have been analyzed using Matlab code of the Viola & Jones and Viola & Jones algorithm combined with the Kanade-Lucas-Tomasi algorithm to add the feature of completing the tracking of a single object. This paper presents the detection percentages, false positives and false negatives for each image and for each simulation code, resulting in the scenarios with the most detection problems and the most accurate algorithm in face detection.
Children’s Age and Gender Recognition from Raw Speech Waveform Using DNN

We propose raw speech waveform-based end-to-end deep neural network (DNN) architectures to estimate age and gender of children within the age range of 4–14 years. To achieve this objective, we design single-task and multi-task learning DNN configuration. In the multi-task learning DNN, we use age and gender as separate label in two output layers and jointly optimize the total objective loss. We use a data-driven approach of learning feature from raw waveform within the DNN, which provides the learning process freedom to learn gender and age discriminative features during training. Interleaving time-delay neural network and long short-term memory (TDNN-LSTM) layers with time-restricted self-attention mechanism has been used for modeling of speech temporal dynamics. Experimental results provide a comparative analysis of single-task and multi-task learning process for age and gender recognition from children's speech.
Implementation Of SaaS Cloud Computing Services On E-Learning Applications (Case Study: PGRI Foundation School)

Abstract Learning activities in PGRI Foundation schools, especially in SMA PGRI Kudus still use the conventional model that is directly face to face, so it is necessary to develop online learning media or what is now often referred to as e-learning to help teachers and students take material and do assignments by online. With online learning, teachers are expected to be able to update learning material and students can access it without constraints on place and time, besides assignments to students can be well controlled. In general, all PGRI Foundation schools in Kudus district have the same business process as well as the information system and technology requirements. But in reality, each school develops its architecture and information system separately. On the other hand, the majority of schools do not have enough budget to procure infrastructure to support the information systems needed specifically for learning activities such as e-learning. Cloud computing is often referred to as a new paradigm in the world of information technology related to resource efficiency. Cloud Computing has a service layer known as Software as a Service (SaaS). SaaS has several advantages and conveniences offered, including those related to resource efficiency and speed in developing an application. With SaaS, an application can be used at the same time by many tenants, with each tenant still being protected, so that application development costs can be further reduced. This research uses object-oriented system development methods with UML tools. This research aims to help schools in the Kudus Regency PGRI foundation improve the quality of learning by utilizing e-learning media without the need to think about the process of procurement, installation and configuration of hardware or software.
Frequency tracking and mitigation method based on CPHD filter and adaptive multiple linear Kalman notch filter for multiple GNSS interference

In this paper, a frequency tracking method based on a cardinalized probability hypothesis density (CPHD) filter with cardinality compensation and fuzzy-c means (FCM) clustering is proposed to precisely estimate multiple interference frequencies in the received Global Navigation Satellite System (GNSS) signal. In addition, an adaptive multiple linear Kalman notch filter is applied in order to mitigate multiple GNSS interference signals by using tracking results from the proposed CPHD filter. The cardinality refers to the number of interference frequencies, and estimation accuracy on the cardinality has an effect on the tracking and mitigation performance of the filter. For that reason, the cardinality compensation process and FCM clustering are added in the CPHD filter. The proposed tracking and mitigation method is evaluated by theoretical analysis including simulations. It is confirmed that the proposed algorithm has better tracking and mitigation performance compared with the conventional algorithms.
Using Machine Learning Technologies for Carrying out Statistical Analysis of Block Ciphers

This article presents the application of machine learning technologies to cryptography tasks, in particular, the statistical analysis of block ciphers. The author uses the Inception V3 neural network model, which is traditionally used for images recognition. A technology for adapting ciphertexts to the developed technique is proposed. The results of experiments on encrypted sequences are presented. The advantages of statistical analysis by machine learning over the classical methods of statistical testing are revealed. In this article, we demonstrate that in some cases using the machine-learning techniques it is possible to distinguish reduced-round ciphers ciphertexts from the random sequences having a smaller samples comparing with traditional statistical methods.
Action Recognition Using Deep 3D CNNs with Sequential Feature Aggregation and Attention

Action recognition is an active research field that aims to recognize human actions and intentions from a series of observations of human behavior and the environment. Unlike image-based action recognition mainly using a two-dimensional (2D) convolutional neural network (CNN), one of the difficulties in video-based action recognition is that video action behavior should be able to characterize both short-term small movements and long-term temporal appearance information. Previous methods aim at analyzing video action behavior only using a basic framework of 3D CNN. However, these approaches have a limitation on analyzing fast action movements or abruptly appearing objects because of the limited coverage of convolutional filter. In this paper, we propose the aggregation of squeeze-and-excitation (SE) and self-attention (SA) modules with 3D CNN to analyze both short and long-term temporal action behavior efficiently. We successfully implemented SE and SA modules to present a novel approach to video action recognition that builds upon the current state-of-the-art methods and demonstrates better performance with UCF-101 and HMDB51 datasets. For example, we get accuracies of 92.5% (16f-clip) and 95.6% (64f-clip) with the UCF-101 dataset, and 68.1% (16f-clip) and 74.1% (64f-clip) with HMDB51 for the ResNext-101 architecture in a 3D CNN.
Predictive compression of molecular dynamics trajectories

Molecular dynamics simulations help to understand the complex behavior of molecules. The output of such a simulation describes the trajectories of individual atoms as snapshots of atom positions in time. Many compression schemes were developed to reduce the amount of data needed for storing long trajectories. This is achieved by limiting the precision of coordinates, encoding differences instead of absolute values, dimensionality reduction by principal component analysis, or by using polynomials approximating vertex trajectories. However, compression schemes using actual bonds between atoms have not been utilized to their full potential. Therefore, we developed a lossy compression method that captures the local, mostly rotational movement of atoms with respect to their bonded neighbors and predicts their positions in each frame. This allows full control over the data distortion. In our experiments, the method achieves data rates which are substantially better than the rates achieved by competing methods at the same error level.
An Optical Neural Network Architecture based on Highly Parallelized WDM-Multiplier-Accumulator

Future applications such as anomaly detection in a network and autonomous driving require extremely low, submicrosecond latency processing in pattern classification. Towards the realization of such an ultra-fast inference processing, this paper proposes an optical neural network architecture which can classify anomaly patterns at sub-nanosecond latency. The architecture fully exploits optical parallelism of lights using wavelength division multiplexing (WDM) in vector-matrix multiplication. It also exploits a linear optics with passive nanophotonic devices such as microring resonators, optical combiners, and passive couplers, which make it possible to construct low power and ultra-low latency optical neural networks. Optoelectronic circuit simulation using optical circuit implementation of multi-layer perceptron (MLP) demonstrates sub-nanosecond processing of optical neural network.
A New C&amp;C Channel Detection Framework Using Heuristic Rule and Transfer Learning

A great many of botnet detection methods focus on recognizing the significant C&C channels. Most of them require a C&C training set to build a behavior detection model. However, when lacking such training set for new or unknown botnets, these methods may become inefficient or even invalid.To overcome it, we propose a new general framework for C&C channel detection. It neither needs us to know the families of bots or prepare a training set nor requires deploying malicious activity monitors. Also, it is capable of mining useful knowledge from the historical dataset to boost its detection performance. In our framework, we put forward a clustering method and several heuristic rules to aggregate and label partial C&C traffic, a sample selection function to mine useful historical knowledge and a transfer learning based model to find other C&C channels. We evaluated our framework on two datasets and achieved the best C&C F-measure of about 0.886 and 0.960 respectively. Moreover, the comparison result further indicates its performance advantage and better behavior learning ability.
Semi-supervised Attentive Mutual-info Generative Adversarial Network for Brain Tumor Segmentation

Recent years have witnessed tremendous success in medical image analysis using deep learning methods. However, most of the work are based on fully-supervised learning, which requires sizeable and reliable labeled data. This is one of the major bottlenecks in applying these methods into practice. To reduce the labeling burden, semi-supervised learning that enables models to be learned from both labeled data and unlabeled data has been applied in many tasks, including medical image analysis. This work presents a semi-supervised attentive mutual- info generative adversarial network (SAM-GAN) for brain tumor segmentation. SAM-GAN is injected with comprehensive attention mechanism and a novel local mutual information maximization mechanism. The attention module contains channel attention block and spatial attention block, where the channel attention block indicates what to focus, while the spatial attention block indicates where to focus. Moreover, the mutual information maximization mechanism is used to focus the network on the local dependencies of the images, leading to more powerful representation ability of the network. Experimental results on two brain tumor segmentation tasks show that the proposed SAM- GAN outperforms traditional fully-supervised methods and other semi-supervised competitors, which shows the effectiveness of the proposed model.
Hybrid Method for Biomedical Image Denoising

Convolutional neural networks (CNN) show very good performance and achieve impressive results in biomedical image denoising. Nevertheless CNN-based methods strongly depend on the used training set and even small differences in the input data can cause output disturbance. Thus new more reliable hybrid denoising methods were suggested. They include combinations of CNN and "classical" algorithms like Non-Local Means, BM3D, Bilateral, Anisotropic diffusion, Total Variation (TV), etc. However hybrid methods need non-reference automatic parameters estimation for classical algorithms. In this paper we present a hybrid DnCNN+BM3D method with automatic choice of the strength parameter for BM3D method. To control biomedical image structures by multiscale ridge based approach we analyze presence of regular structures in the ridge areas at the difference between noisy and filtered images. Test results for retinal image dataset DRIVE, natural images BSD and for real CT image show practical applicability of the method.
Discriminative Embedding Autoencoder With a Regressor Feedback for Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize the novel object categories using the semantic representation of categories, and the key idea is to explore the knowledge of how the novel class is semantically related to the familiar classes. Some typical models are to learn the proper embedding between the image feature space and the semantic space, whilst it is important to learn discriminative features and comprise the coarse-to-fine image feature and semantic information. In this paper, we propose a discriminative embedding autoencoder with a regressor feedback (DEARF) model for ZSL. The encoder learns a mapping from the image feature space to the discriminative embedding space, which regulates both inter-class and intra-class distances between the learned features by a margin, making the learned features be discriminative for object recognition. The regressor feedback learns to map the reconstructed samples back to the the discriminative embedding and the semantic embedding, assisting the decoder to improve the quality of the samples and provide a generalization to the unseen classes. The DEARF model is validated extensively on the benchmark datasets, and the experiment results show that the DEARF model outperforms the state-of-the-art models, and especially in the generalized zero-shot learning (GZSL), significant improvements are achieved.
Performance and Evaluation of Different Kernels in Support Vector Machine for Text Mining

Text mining is the subfield of data mining. Text analysis or mining is enormously growing field for research simultaneously to artificial intelligence and data mining. Unstructured data or multimedia data is being constantly generated via social media Web sites, call center logs, blogs, and so on. Therefore, the explosion of textual data in a social media network is overwhelming. Text analysis or mining is widely being used to determine meaningful and noteworthy information for the huge amount of unstructured multimedia or heterogeneous data. In this paper, we compare the performance of text classification through support vector machine using multimedia data. This text analysis uses the technique of different fields like predict, machine learning, information, visualization, and natural language processing. The use of support vector machine for learning text nonlinear classifier is determined here. The reason for using SVM is that its performance is more accurate as compared to other soft computing tools and algorithms. In this paper, different kernel tricks have applied with nonlinear classifier for classification of text data mining. The results of proposed experiments predict that support vector machine with radial basis function achieves the highest overall accuracy.
Deep latent variable models for generating knockoffs

Selective inference is an emerging field in big data analytics; it targets on conducting variable selection and providing statistical inference at the same time. Among various selective inference frameworks, the model‐X framework offers the most flexible tool to equip almost any machine learning method with the ability for false discovery rate (FDR) controlled variable selection. This paper provides a practical and flexible approach to generate knockoffs. We propose to fit a latent variable model for generating knockoffs. Under general conditions, the knockoffs can be generated by approximate inference of a latent variable, which captures all the correlation of predictors. We propose an algorithm based on recent advancement in stochastic variational inference to approximately reconstruct the distribution of data via the latent variables. We demonstrate that our proposed method can achieve FDR control and better power than existing knockoff generation methods in various simulated settings and a real data example for finding mutations associated with drug resistance in human immunodeficiency virus type 1 patients.
Data-Driven 3D Neck Modeling and Animation

In this article, we present a data-driven approach for modeling and animation of 3D necks. Our method is based on a new neck animation model that decomposes the neck animation into local deformation caused by larynx motion and global deformation driven by head poses, facial expressions, and speech. A skinning model is introduced for modeling local deformation and underlying larynx motions, while the global neck deformation caused by each factor is modeled by its corrective blendshape set, respectively. Based on this neck model, we introduce a regression method to drive the larynx motion and neck deformation from speech. Both the neck model and the speech regressor are learned from a dataset of 3D neck animation sequences captured from different identities. Our neck model significantly improves the realism of facial animation and allows users to easily create plausible neck animations from speech and facial expressions. We verify our neck model and demonstrate its advantages in 3D neck tracking and animation.
A Collaborative Landscaping Exercise of IoT Design Methods

We brought together the creators of seven Internet of Things design methods for a collaborative landscaping exercise of these design methods. Together, the creators tried out all of these IoT design methods and located them on a design process model. A visual analysis of this landscaping exercise reveals large discrepancies between the application area the original creators of each IoT design method had in mind, and the understanding of most other experts. We subsequently argue that the design research community can benefit from collaborative approaches towards understanding each other's methods. We conclude that the design research community can benefit from more appropriate mappings when choosing the most appropriate IoT design method for a particular task within a specific context.
Emotions Extracted from Text vs. True Emotions–An Empirical Evaluation in SE Context

Emotion awareness research in SE context has been growing in recent years. Currently, researchers often rely on textual communication records to extract emotion states using natural language processing techniques. However, how well these extracted emotion states reflect people's real emotions has not been thoroughly investigated. In this paper, we report a multi-level, longitudinal empirical study with 82 individual members in 27 project teams. We collected their self-reported retrospective emotion states on a weekly basis during their year-long projects and also extracted corresponding emotions from the textual communication records. We then model and compare the dynamics of these two types of emotions using multiple statistical and time series analysis methods. Our analyses yield a rich set of findings. The most important one is that the dynamics of emotions extracted using text-based algorithms often do not well reflect the dynamics of self-reported retrospective emotions. Besides, the extracted emotions match self-reported retrospective emotions better at the team-level. Our results also suggest that individual personalities and the team's emotion display norms significantly impact the match/mismatch. Our results should warn the research community about the limitations and challenges of applying text-based emotion recognition tools in SE research.