Reviewerly/exHarmony huggingface dataset:
Ellipsoidal and Gaussian Kalman Filter Model for Discrete-Time Nonlinear Systems

In this paper, we propose a new technique—called Ellipsoidal and Gaussian Kalman filter—for state estimation of discrete-time nonlinear systems in situations when for some parts of uncertainty, we know the probability distributions, while for other parts of uncertainty, we only know the bounds (but we do not know the corresponding probabilities). Similarly to the usual Kalman filter, our algorithm is iterative: on each iteration, we first predict the state at the next moment of time, and then we use measurement results to correct the corresponding estimates. On each correction step, we solve a convex optimization problem to find the optimal estimate for the system’s state (and the optimal ellipsoid for describing the systems’s uncertainty). Testing our algorithm on several highly nonlinear problems has shown that the new algorithm performs the extended Kalman filter technique better—the state estimation technique usually applied to such nonlinear problems.
Face Detection Ensemble with Methods Using Depth Information to Filter False Positives

A fundamental problem in computer vision is face detection. In this paper, an experimentally derived ensemble made by a set of six face detectors is presented that maximizes the number of true positives while simultaneously reducing the number of false positives produced by the ensemble. False positives are removed using different filtering steps based primarily on the characteristics of the depth map related to the subwindows of the whole image that contain candidate faces. A new filtering approach based on processing the image with different wavelets is also proposed here. The experimental results show that the applied filtering steps used in our best ensemble reduce the number of false positives without decreasing the detection rate. This finding is validated on a combined dataset composed of four others for a total of 549 images, including 614 upright frontal faces acquired in unconstrained environments. The dataset provides both 2D and depth data. For further validation, the proposed ensemble is tested on the well-known BioID benchmark dataset, where it obtains a 100% detection rate with an acceptable number of false positives.
Critical Infrastructure Security Against Drone Attacks Using Visual Analytics

The recent developments in the field of unmanned aerial vehicles (UAV or drones) technology has generated a lot of interdisciplinary applications, ranging from remote surveillance of energy infrastructure, to agriculture. However, in the context of national security, low-cost drone equipment has also been viewed as an easy means to cause destructive effects against national critical infrastructures and civilian population. Addressing the challenge of real-time detection and continuous tracking, this paper proposed presents a holistic architecture consisting of both software and hardware design. The software-based video analytics component leverages upon the advancement of Region based Fully Convolutional Network model for drone detection. The hardware component includes a low-cost sensing equipment powered by Raspberry Pi for controlling the camera platform for continuously tracking the orientation of the drone by streaming the video footage captured from the long-range surveillance camera. The novelty of the proposed framework is twofold namely the detection of the drone in real-time and continuous tracking of the detected drone through controlling the camera platform. The framework relies on the capability of the long-range camera to lock into the drone and subsequently track the drone through space. The analytics processing component utilises the NVIDIA$$\circledR $$ GeForce$$\circledR $$ GTX 1080 with 8 GB GDDR5X GPU. The experimental results of the proposed framework have been validated against real-world threat scenarios simulated for the protection of the national critical infrastructure.
Live Streaming: A New Platform for ESL Learning

Research on the application of live streaming in ESL learning is still quite scarce among researchers especially in Malaysia.However, a number of studies on live streaming in the field of marketing and branding as well as the culture of live streaming are evolving rapidly.A lot of researches also have been done on games streaming.As live streaming has become more significant in all field, a study on the impacts of live streaming in the education field should become important, too.Therefore, this leads to the purpose of the study which is to explore the possibility of live streaming as a new platform for ESL learning.Since Gen Z requires teaching and learning process that suits their learning preference.Hence, live streaming serves as a good platform for them to indulge in the ESL learning process.A lot of reading has been done to give emphasis on the significance of the study.Live streaming will be helpful in ESL learning to keep pace with globalization, produce independent learners, provide authentic learning experiences as well as give rooms for real life communication and interactivity.The discussion of this study would hopefully shed lights to researcher to investigate further in this area.
Hierarchical syntactic models for human activity recognition through mobility traces

Abstract Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known as grammatical inference . We also introduce a measure of similarity that accounts for the intrinsic hierarchical nature of such models, and allows to identify the common traits in the paths induced by different activities at various granularity levels. Our method has been validated on a dataset of real traces representing movements of users in a large metropolitan area. The experimental results show the effectiveness of our similarity measure to correctly identify a set of common coarse-grained activities, as well as their refinement at a finer level of granularity.
Cryptanalysis of CLT13 Multilinear Maps with Independent Slots

Many constructions based on multilinear maps require independent slots in the plaintext, so that multiple computations can be performed in parallel over the slots. Such constructions are usually based on CLT13 multilinear maps, since CLT13 inherently provides a composite encoding space, with a plaintext ring $$\bigoplus _{i=1}^n \mathbb {Z}/g_i\mathbb {Z}$$ for small primes $$g_i$$ 's. However, a vulnerability was identified at Crypto 2014 by Gentry, Lewko and Waters, with a lattice-based attack in dimension 2, and the authors have suggested a simple countermeasure. In this paper, we identify an attack based on higher dimension lattice reduction that breaks the author's countermeasure for a wide range of parameters. Combined with the Cheon et al. attack from Eurocrypt 2015, this leads to the recovery of all the secret parameters of CLT13, assuming that low-level encodings of almost zero plaintexts are available. We show how to apply our attack against various constructions based on composite-order CLT13. For the [FRS17] construction, our attack enables to recover the secret CLT13 plaintext ring for a certain range of parameters; however, breaking the indistinguishability of the branching program remains an open problem.
Predicting Software Vulnerabilities Using Topic Modeling with Issues

The existence of software vulnerabilities is an indicator of the reliability and safety of software products. Software vulnerabilities can be predicted using metrics derived from developers, organization, code and textual data. In this work, we aim to predict the software vulnerabilities using issue records in two different datasets. The first dataset consists of six-months of issue records collected in a corporate, whereas the second dataset consists of Wireshark project bug records from 2017 to 2018. Prediction models were established using six different machine learning for which textual descriptions of issue records were converted into topic models. A regression model was established for the corporate company in which textual description of issue records were used as the input, and the number of vulnerabilities were used as the output of the model. A classification model was established for Wireshark dataset in which textual descriptions of bug records were used as input of the model, and the class of vulnerable-prone or not is used as the output. The best regression model results are 0.23, 0.30, 0.44 MdMRE values, respectively. The best classification model result is 74% recall score.
MSQ-Index: A Succinct Index for Fast Graph Similarity Search

Graph similarity search under the graph edit distance constraint has received considerable attention in many applications, such as bioinformatics, data mining, pattern recognition and social networks. Existing methods for this problem have limited scalability because of the huge amount of memory they consume when handling very large graph databases with tens of millions of graphs. In this article, we present a succinct index that incorporates succinct data structures and hybrid encoding to achieve improved query time performance with minimal space usage. Specifically, the space usage of our index requires only 5-15 percent of the previous state-of-the-art indexing size while at the same time achieving several times acceleration in query time on the tested data. We also improve the query performance by augmenting the global filter with range searching, which allows us to perform similarity search in a reduced region. In addition, we propose two effective lower bounds together with a boosting technique to obtain the smallest possible candidate set. Extensive experiments demonstrate that our proposed approach is superior both in space and filtering to the state-of-the-art approaches. To the best of our knowledge, our index is the first in-memory index for this problem that successfully scales to cope with the large dataset of 25 million chemical structure graphs from the PubChem dataset. The source code is available online.
Breast Tumor Cellularity Assessment Using Deep Neural Networks

Breast cancer is one of the main causes of death worldwide. Histopathological cellularity assessment of residual tumors in post-surgical tissues is used to analyze a tumor's response to a therapy. Correct cellularity assessment increases the chances of getting an appropriate treatment and facilitates the patient's survival. In current clinical practice, tumor cellularity is manually estimated by pathologists; this process is tedious and prone to errors or low agreement rates between assessors. In this work, we evaluated three strong novel Deep Learning-based approaches for automatic assessment of tumor cellularity from post-treated breast surgical specimens stained with hematoxylin and eosin. We validated the proposed methods on the BreastPathQ SPIE challenge dataset that consisted of 2395 image patches selected from whole slide images acquired from 64 patients. Compared to expert pathologist scoring, our best performing method yielded the Cohen's kappa coefficient of 0.69 (vs. 0.42 previously known in literature) and the intra-class correlation coefficient of 0.89 (vs. 0.83). Our results suggest that Deep Learning-based methods have a significant potential to alleviate the burden on pathologists, enhance the diagnostic workflow, and, thereby, facilitate better clinical outcomes in breast cancer treatment.
XGBoost Imputation for Time Series Data

Data quality plays an important role in the data-driven based biomedical informatics research because the effectiveness of these research heavily relies on the completeness of data being collected. The problem of missing values, however, is commonly encountered in research practice, which impedes researchers to build accurate models and then make reasonable decisions. Simply removing the data instances having missing value(s) is a candidate strategy, but risks incorporating biases or even yielding incorrect models. [1]. Thus, imputing accurate missing values is a prerequisite to training good machine learning models.
Destructure-and-restructure matrix approximation

A matrix approximation is used to predict the missing values of a matrix, which has many applications in recommender systems. After predicting the missing values of a user-item rating matrix, we can recommend unrated items to a user according to their approximated values. This paper proposes a new approximation scheme for large and sparse global matrices, which is called a destructure-and-restructure matrix approximation. The basic idea is to first destructure a global matrix into many local matrices, and then restructure it from local matrix approximations. However, distance computation is a challenging issue in matrix destructure because no prior knowledge about the most appropriate feature vectors and distance measures are available. To deal with this issue, we propose a novel scheme that does not require any distance computation for local matrix construction, which is based on the application of convergence probabilities of a graph random walk. At first, a user-item bipartite graph is built from the global matrix. After performing random walk on the bipartite graph, we select several user-item pairs as anchors. Then, another random walk with restart is applied to construct local matrices from anchors. For each local matrix, we propose a weighted matrix factorization that is based on the rating distribution of the training data. In the restructure process, we first compute the approximation credibility of each local matrix, and we then finally obtain the global matrix approximation weighted from credible local approximations. Our experiments on five real-world datasets show that the proposed solution outperforms the state-of-the-art schemes in terms of lower prediction errors and higher coverage ratios.
Traffic Sign Detection and 3D Localization via Deep Convolutional Neural Networks and Stereo Vision

Traffic sign detection is considered as a fundamental task for autonomous driving and Advanced Driver Assistance Systems (ADAS). Therefore, an algorithm to spacially locate the detected traffic signs is required in order to make these detections useful. In this paper, an algorithm to detect, classify and spatially locate multiple traffic signs in different scenarios is presented. Detection and classification are made simultaneously via YOLOv3, using RGB images. 3D sign localization is achieved by estimating the distance from the traffic sign to the vehicle, by looking at detector bounding boxes and the disparity map generated by stereo vision. Moreover, a new traffic sign dataset called LSITSD is created to solve the issues that most of traffic sign available datasets have, such as missing or incorrect labels, or the small amount of images provided. The obtained results show the performance and the robustness of the proposed algorithm in detecting the traffic signs and the accuracy of the 3D localization estimation of the detected traffic signs.
Thai Named Entity Recognition Using Bi-LSTM-CRF with Word and Character Representation

Named Entity Recognition (NER) is a handy tool for many natural language processing tasks to identify and extract a unique entity such as person, location, organization and time. In English and Chinese, NER has been thoroughly researched and is able to be applied in more practical settings. Its development in Thai is still limited because of rare resources and language difficulties such as the lack of boundary indicator for words, phrases and sentences. In this paper, we present an application of Bi-LSTM-CRF with word/character level representation, to solve this problem. Firstly, we prepared texts by tokenizing a sentence to a bunch of words. We then prepared word representation and Bi-LSTM character representation. In the end, we built a recurrent neural network combined with CRF to learn the sequence of text and extract the knowledge to build NER recognition to overcome this problem. Our model was evaluated by the NER opensource corpus from a Facebook group ThaiNLP. The results of our model yielded precision, recall, and F1 at 91.79%, 91.51% and 91.65% respectively.
Self-Optimization in Smart Production Systems using Distributed Reinforcement Learning

This paper introduces a novel approach for self-learning in highly flexible, modular manufacturing systems enabling fast reconfiguration and online adaptation to changing production requirements. The approach is based on a distributed optimization scheme such that production modules are equipped with their own optimization agent with its local objectives to be optimized. The communication and coordination of the agent is limited to the basically required amount. The approach is based on the recently developed deep deterministic policy gradient (DDPG) approach, a high performing algorithm from the family of actor-critic reinforcement learning algorithms. As DDPG is based on single agent learning, we develop a fully distributed multi-agent learning setting with different levels of information about the neighbors. We apply the approach to a laboratory scale distributed bulk good production testbed with very encouraging results. Particularly, we found very reasonable control strategies by learning the agents from scratch.
Implementation of fuzzy tsukamoto method in decision support system of journal acceptance

Abstract Journal acceptance is a difficult problem to solve since in the practice it involves some reviewers who can produce different decisions from various perspectives. Therefore, a decision support system is needed to assist the reviewers to decide the acceptance of the paper. This study was purposed to develop the decision support system using the Fuzzy Tsukamoto method for journal acceptance. The Fuzzy Tsukamoto method described the relationship between input and output of the system by using a set of fuzzy if-then rules. From the comparison results, It is obtained that the accuracy from the result of comparison of the manual method, expert decision, and DSS of journal acceptance using Fuzzy Tsukamoto Method is 95% with 5% errors. Based on the results of the accuracy and error, it shows that the DSS of journal acceptance using the Fuzzy Tsukamoto Method is accurate and has high precision.
Implementation of RFID Attendance System with Face Detection using Validation Viola-Jones and Local Binary Pattern Histogram Method

In this study, attendance system was created by combines RFID attendance technology and face recognition. The attendance system is an important thing to facilitate attendance data retrieval. Attendance system often encountered in the technological era such as today is the RFID attendance system. Although the attendance system is already sophisticated, this system still has shortcomings such as the occurrence of entrusted cards. In this research, the writer has two inputs, namely ID on the RFID card and a face image. Face images will be process by two methods there are the Viola-Jones method that use to detecting the face objects in the image, and the Local Binary Pattern Histogram method as face recognition. Furthermore, if the face was recognized then the RFID card ID data and face recognition data will compare, the results of this process will be attended data in the database. Based on the results of test have been done by the author, the response system shows very good results. In the first test of the RFID sensor in 11 tests, the sensor was able to distinguish all cards that had been registered and cards that had not been registered. This test is carried out from distance of 1 cm to 4 cm. Furthermore, in the second test of facial recognition, in 100 trials of facial recognition testing with different facial poses, the system can recognize faces 93 times so system has 93% success rate. This success rate is influenced by several factors such as the test carried out at 364 lux illumination strength, the test distance at > 20cm and <; 70cm, as well as the maximum testing angle 45°.
A New End-to-End Long-Time Speech Synthesis System Based on Tacotron2

End-to-end speech synthesis breaks away from the original system framework and directly converts text into speech. Although it is shown that Tacotron2 is superior to traditional piping systems in terms of speech naturalness, it still has many defects. A flaw in tacotron2 is mentioned in this paper., which impacts negatively upon the synthesis quality and the synthesized length of speech. It is cumulative error between training process (forward) and synthesis process (inference). In order to improve this problem, an unsupervised GAN (Generative Adversarial Network) model was proposed based on the Tacotron2. The proposed GAN model can also optimize the prosody of synthesize speech because of the prosody discriminator is also designed in our model. For further reduce the cumulative error mentioned above, this paper propose a training strategy called "random down" based on Tacotron2. And then demonstrate that the unimportant attention weights could be a contributing factor to cumulative error when the input sequence is too long. For this, a window has been added to the attention weights. Through these method, the length of the speech synthesis is improved to about 1000 encoder output. The prosody of synthetic speech has also been improved.
Network enforcement as denunciation endorsement? A critical study on legal enforcement in social media

Facing the increasing prevalence of hate speech and other personality rights violations on social media, the legislature tries to improve the law enforcement by involving the social media companies. In 2017, the German Bundestag passed a new law, the Network Enforcement Act (NetzDG), which requires website owners to review the content published on their pages upon it being reported by users and to remove it as soon as possible in the event of a breach of the law. This forces social media operators to establish efficient reporting systems which can create breeding grounds for user denunciation. To get an overview of whether this is really the case, we briefly discuss state surveillance and denunciation and analyse the statistics of content blockings and deletions in five countries (USA, Germany, United Kingdom, Russia and Turkey). In Germany the number of blockings and deletions of content has risen for Facebook, Twitter and YouTube due to the NetzDG, but it has also been found that the majority of reports were unjustified as they did not lead to any restriction or deletion of the content. When comparing the German numbers with those of the other countries investigated, it becomes apparent that after the introduction of the NetzDG in Germany, more content is being blocked and deleted. Also, a comparison of the various laws regulating online content in these countries was conducted. It was found that not only Germany has tightened the regulation of online content and that other countries are also trying to transform the Internet into a more hate free environment.