Reviewerly/exHarmony huggingface dataset:
Continuous Authentication of Smartphone Users using Machine Learning

Current smartphone security techniques offer less reliability. As an instance, personal identification numbers can be easily guessed or hacked, fingerprint scan requires a hardware to operate, and face recognition can be affected by light, other people in the background, or different poses by the users. In addition, they are befitting for one-time security, therefore commonly used at the time of login to verify users. However, what if there's a change of user while accessing the smartphone, and the phone is accessed by an intruder after login. To deal with this issue, continuous authentication is applied which regularly and unnoticeably will address the challenges of verifying users via behavioral features, such as keystroke, hand, and orientation activities. The goal of this research project is to design and implement a behavior-based security method and detect intrusion using machine learning. Hand-movement, grasp, and orientation are three behavioral features that can be effectively used to continuously authenticate users. In-built inertial sensors including accelerometer, gyroscope, magnetometer, and orientation are used to unnoticeably represent sensitive micro-movements of hand and orientation pattern when a user accesses the smartphone screen. The researchers in this project investigated large datasets of different smartphone users with different interaction sessions. To detect the behavior of smartphone users, the research applies various supervised machine learning algorithms on the dataset of smartphone users. The experimentation results indicate that the presented approach is promising and can be effectually implemented for continuous authentication of smartphone users.
Sparse Reconstruction on Robust Dictionary for Accurate Salient Object Detection

Considering the low accuracy of existing salient object detection algorithms in addressing boundary objects problem, this paper proposes a novel algorithm which detects salient object through Sparse Reconstruction on Robust Dictionary. Firstly, the prior information of the salient objects connected with image boundary is statistically analyzed based on public datasets. Secondly, the evaluation and optimization of dictionary elements are performed to construct robust dictionary via removing low background boundary superpixels and adding high background internal superpixels. saliency maps are generated according to the sparse reconstruction errors on the new dictionary. Finally, integration of multi-level saliency maps is achieved by means of linear weighted fusion strategy with learned weight coefficients. Extensive experimental results demonstrate that the proposed algorithm is superior to the state-of-the-art models on benchmark datasets.
RNN and Mel-spectrogram based acoustic balancing and bouncing of ping pong ball

This paper basically addresses real time neural network control of a peculiarly mechanical system using multi-channel audio input. The aim distinctly is to balance a bouncing ball on a wooden frame by taking into account the acoustic feedback from sensors mounted on the corners of the wooden frame. We first considered the audio data from sensors at an initially known position to create a Mel-Spectrogram that results as the sole input of a recurrent neural network. In our application, the essential subtle differences between the spectrograms from multiple sources are used to drive the position of actuators which determine the position and rotation of a parallel manipulator. We present a real time simulated implementation of this method which operates under physically accurate conditions.
Improved Word Alignment System for Myanmar-English Machine Translation

Word alignment is an essential task for every Statistical Machine Translation (SMT) system. An alignment is the arrangement of two or more alignments between the parallel sentences. The problem of word alignment in SMT is to find the strong alignment in the corresponding sentence pairs. Moreover, popular word alignment system (GIZA++) needs improvement in Myanmar-English machine translation because Myanmar is inflected, and it is also a language scarce resource. For this reason, this paper presents the idea of word alignment system by adding the extra resources: word and Name Entity Recognition (NER) translation pairs to the existing training data to improve the word alignment system. Experimental results show that the proposed word alignment system reduces the Alignment Error Rate (AER) than baseline.
Parzen Window Approximation on Riemannian Manifold

In graph motivated learning, label propagation largely depends on data affinity represented as edges between connected data points. The affinity assignment implicitly assumes even distribution of data on the manifold. This assumption may not hold and may lead to inaccurate metric assignment due to drift towards high-density regions. The drift affected heat kernel based affinity with a globally fixed Parzen window either discards genuine neighbors or forces distant data points to become a member of the neighborhood. This yields a biased affinity matrix. In this paper, the bias due to uneven data sampling on the Riemannian manifold is catered to by a variable Parzen window determined as a function of neighborhood size, ambient dimension, flatness range, etc. Additionally, affinity adjustment is used which offsets the effect of uneven sampling responsible for the bias. An affinity metric which takes into consideration the irregular sampling effect to yield accurate label propagation is proposed. Extensive experiments on synthetic and real-world data sets confirm that the proposed method increases the classification accuracy significantly and outperforms existing Parzen window estimators in graph Laplacian manifold regularization methods.
On sharp and single-conclusion justification models

Abstract Justification awareness models (JAMs) were proposed by S. Artemov as a tool for modelling epistemic scenarios such as Russell’s prime minister example. It was demonstrated that the sharpness and the single-conclusion property of a model play an essential role in the epistemic usage of JAMs. The problem to axiomatize these properties using the propositional justification language was left opened. We propose the solution and define a decidable justification logic $\textsf{J}_{\textit{ref}}$ that is sound and complete with respect to the class of all sharp single-conclusion justification models. We also provide the complete axiomatizations for the classes of all single-conclusion justification models and all sharp justification models.
Go-selfies: A Fast Selfies Background Removal Method Using ResU-Net Deep Learning

The selfies play an important role in recording meaningful moment in human's daily life. In most cases, before sharing photos, people often synthesis attractive images on some phone applications, such as Photoshop. While these kinds of software have reached good performance nowadays, they are too complex for simple life usage. In this work, we proposed an automatic segmentation model unique to segment human selfies photos. We first constructed a large photo segmentation database and built 8 different models based on resolution, image size and whether or not to use transfer learning and picked the best one among them. We then applied cyclical learning rate method and pre-trained encoder network to fine tune our models. Finally, our best model tested on Google images demonstrated satisfying promising results on both accuracy scores and losses, which will be the precondition in real-time segmentation. We named this lovely web product as "Go Selfies".
Comparison of Tissue Classification Performance by Deep Learning and Conventional Methods on Colorectal Histopathological Images

The automatic evaluation is essential for the diagnosis and treatment of the disease of pathological images. Computer-aided systems are becoming more common day by day in this area. In this study, multi-class (8 different classes) tissue types were studied in colon cancer histopathological images. Data mining algorithms are used in the diagnosis phase in the health field. As a conventional method, first of all, the properties of the images are extracted and then the texture classification process is performed with data mining algorithms. The Gray Level Co-occurrence Matrix (GLCM), Discrete Cosine Transform (DCT), Local Binary Pattern (LBP) are used in textural feature extraction. Along with these attributes, machine learning algorithms, such as k-nearest neighbors (KNN), support vector machines (SVM), random forests (RF), logistic regression (LR) were used for classification. As another method, to remove the attributes and perform classification at the same time, tissue classification was performed using deep learning (convolutional neural network) on histopathological images. Tissue classification was automated using transfer learning based on ResNet-18 architecture, one of the convolutional neural network architectures. According to the determined feature and classification algorithm, the performance rates are also given comparatively. Our experiments showed that RF classifier with LBP and GLCM features provided 82% accuracy, while the deep learning method based on ResNet-18 architecture achieved 88.5% accuracy.
Design of Chinese Character Recognition Based on AlexNet Convolution Neural Network

Based on the general digital scanning of paper documents, a Chinese character recognition model is designed by using convolution neural network and image processing technology. The model is developed based on Python and TensorFlow framework, and printed Chinese character recognition is completed by using improved AlexNet convolution neural network structure. The recognition system includes data preprocessing, text area location, single character segmentation, character recognition and result output. The experimental results show that, on the premise of high recognition accuracy, the network model is small and fast recognition, and the recognition rate can basically meet the needs of practical use.
A Novel Image Compression Approach Using DTCWT And RNN Encoder

Limitations in storage capacity and communication bandwidth made researchers to work more on image compression. Many algorithms have been implemented to compress an image for better compression ratio. Recently the application of neural networks to compress image increasing gradually and achieved great success. The proposed work uses Recurrent Neural Networks (RNN) based encoding scheme in wavelet domain transform. Dual-Tree Complex Wavelet Transform (DTCWT) uses three level tree structured Discrete Wavelet Transform (DWT) used to get eight frequency subbands, the lower sub-band output is given to RNN for final encoding process. The experimental results show that the proposed work achieves better compression ratio for higher pixel values as compared to state-of-the algorithms. The Compression Ratio (CR) can be increased for lower pixel values by using more number of iterations for the neural networks and also by modifying the wavelet components.
Task Planning of Manipulator Based on Dynamic Space Constraint and Torque Sensor

It is a main direction of industrial application that manipulator performs grasping task in task space and combines obstacle avoidance planning. This paper presents an improved RRTConnect path planning algorithm IRRTC based on dynamic constrained space and admittance control. The sliding constraints on the Cartesian position space is used to search for the reachable path of the traction robot by the algorithm. For orientation control, a priori path is formed by artificially given constraint interval, and then the dynamic admittance control is modified by using the end grasping load moment estimated by the joint torque sensor of the manipulator as the constraint. At the same time, the algorithm designs a local escape algorithm based on the current state and target state of the robotic arm, which overcomes the problem that the traditional RRTConnect and RRT cannot escape the local minimum. The simulation and experimental results of the Xmate3 redundant manipulator show that a feasible path can be quickly searched based on constraints and local escape strategies, and a relatively optimal trajectory can be planned based on the original path by the optimized path and also meet the constraints. Within 3S, the average planning success rate is increased to over 90%, and without time constraint, the success rate is 100%, the efficiency is higher than other algorithms. Because the admittance control is used in conjunction with the joint torque feedback to maintain the dynamic correction of the end orientation, which improves the stability of the manipulator. The algorithm can be artificially designed to solve the obstacle avoidance and task constraints in disorderly grasping.
A Real-time Trademark Detection Method

Abstract In natural scenes, it is an important and challenging task to improve the accuracy while maintaining the real-time capability of trademark detection. For the needs of real-time trademark detection in natural scenes, a method called LOGO-YOLOv3 based on improved YOLOv3 is proposed in this paper. Channel attention and spatial attention mechanisms are introduced into the Darknet53 feature extraction network. And an additional layer is added to the detection layer, which will be combined with the original three detection layers of YOLOv3 to construct a feature pyramid networks containing four detection layers with different scales. This method can not only greatly reduce the false detection of trademarks in complex scenes, but also deal with the problem of large differences in the size of different types of trademarks. Experiments on the Flickrlogos-32 dataset show that the proposed method outperform the original YOLOv3 in general.
Implementation of Cloud Monitoring System Based on Open Source Monitoring Solution

In this paper, a cloud monitoring system based on an open-source was designed and implemented by subdividing it into CPU, memory, storage, and network parts. Linux and KVM operating systems were used to implement the designed system, and Zabbix and Grafana were used as open-source monitoring solutions. Also, an open source-based monitoring system was implemented to facilitate scalability, such as upgrading and adding monitoring functions, and a smooth and flexible monitoring system compared to commercial products provided by Cloud Service Provider. To review the cloud monitoring system designed whether it works. It was implemented on the cloud, and it was confirmed that CPU utilization, memory utilization, volume, and network resources are generally monitored in real-time.
Analysis and Estimation of Sentence Speakability for English Pronunciation Evaluation

Conventional automatic English evaluation systems used only acoustic features extracted from an input speech. However, not only the speaker's pronunciation ability but also the difficulty of the sentences affect the pronunciation score. In this paper, we referred to the difficulty of sentences as “speakability.” Analysis of variance revealed that sentence speakability was effective in the evaluation as well as the speaker s pronunciation ability. We also showed that the number of phonemes in a sentence and word familiarity were useful as features to estimate sentence speakability. Finally, we carried out the multivariate regression analysis to estimate the sentence speakability score from these features, and the correlation coefficient was 0.41.
Characterising Proxy Usage in the Bitcoin Peer-to-Peer Network

In the public mind, Bitcoin has often been associated with censorship circumvention and evasion of surveillance measures, specifically in the context of monetary transactions. However, this perceived anonymity is a false sense of security as both on-chain transactions and the underlying message exchange in the peer-to-peer network are attack vectors for deanonymisation and monitoring, as shown in other research. Nonetheless, there has been an increase in Bitcoin usage not only for end-users but also in the context of cybercrime in the form of cryptojacking and ransomware. So there are a number of reasons why proxies might be used in the Bitcoin network, either as a privacy-preserving measure of end-users or as obfuscation in cybercrime.
Tree-based Keyword Search Algorithm over the Visual Paradigm’s Class Diagram XML to Abstracting Class Information

In software engineering, abstracting the class information from XML based design artifact is important to analyze the design phase. The challenge of this research is the information is embedded inner the XML data structure and the target data is more than one and stored in a non-consecutive XML element position. This paper proposes a search algorithm over the XML file using the tree-based keyword as a composition of information. The experimentation confirms that the proposed algorithm can be used to collect all information that lies on the XML file by using the tree-based keyword consistently. The proposed algorithm's performance shows the linear growth rate of the execution time that inline with the growth of the number of lines. The tree-based keyword searching algorithm is promising used in the analysis of XML based software artifact.
Guaranteeing Sound Reactions to Long-Tailed Changes: A Syntax-Directed Annotation Approach

To cope with the long-tailed changes, an annotation-based BPM approach has been proposed to adapts its behavior in a timely manner. It patches existing business process models rather than rebuilds models from scratch, which saves efforts and reacts to unforeseen changes quickly. However, the original annotation-based approach is at risk of improper annotations added that results in unexpected effects or leads to failure. To remedy this loophole, this paper proposes a syntax-directed annotation approach to guarantee sound reactions. we develop a scheme for designing domain specific languages based on abstract syntax trees and generating a syntax-directed editor automatically. As a result, all patched annotations on the process models are soundness guaranteed in terms of the domain specific language. Case studies demonstrate that proposed approach and tools can help domain experts to tackle long-tailed changes more easily guarantee the correct reactions.
Classifying and Understanding Tor Traffic Using Tree-Based Models

Over the past years the use of anonymization services has gained significant relevance as more users are interested in protecting their data and privacy on the internet. One of the most popular ways to achieve this result is Tor. The anonymity and untraceability that Tor provides, however, can also be used by ill-intentioned users who try to take advantage of bypassing security control and policies. The Cybersecurity and Infrastructure Security Agency (CISA) mentions two methods of recognizing Tor traffic in the enterprise: indicator- or behavior-based analysis. The first one uses log analysis and lists of Tor exit nodes to identify the suspicious activity while the latter inspects patterns in TCP and UDP ports, DNS queries and inspecting the payload of the packets. In this paper, we propose a different approach using white-box machine learning models such as decision trees and Random Forest. On the one hand, our classifier achieves accuracy levels above 95%. On the other hand, our approach is the first one to allow understanding the importance of each traffic feature in the classification. Our results demonstrate that the TCP window size, the frame size and time related traffic features can be used to identify Tor traffic. In this paper we will describe a Machine Learning methodology used to identify Tor network traffic utilizing decision trees C5.0 and Random Forest. We followed a white-box approach and accomplished accuracy of over 95% in the prediction in both models. We also present an analysis of the importance of the top predictor variables.
Developing Name Entity Recognition for Structured and Unstructured Text Formatting Dataset

Named-Entity Recognition (NER) is a task that extracts the entity information from dataset into several different entity classes. Most of current NER research train the NER model from structured data such as news and Wikipedia article. Whereas, there are several tasks that generate an unstructured dataset such as speech-to-text task. In this paper, we did NER research for unstructured text formatting dataset in Indonesian language using deep learning approaches including LSTM, Bidirectional LSTM (Bi-LSTM), GRU, Bidirectional GRU (Bi-GRU), and Convolutional Neural Network (CNN). We used NERGRIT CORPUS as our dataset and modified the dataset into four types of structured and unstructured datasets. Afterward, we run several experiments scenario by combining all types of data modification and the deep learning algorithms that we used and we obtain that the highest F - Score was obtained when using Bi-GRU for standard dataset, lowercase with punctuation dataset, lowercase without punctuation dataset, and lowercase and clean dataset equal to 71.04%, 70.61%, 68.12%, and 67.45%, respectively.
Preferential Experience Collection with Frequency based Intrinsic Reward for Deep Reinforcement Learning

Experience replay plays a key role in the success of deep reinforcement learning (DRL), where the experience collection is defined as the process of placing experience tuples into a replay buffer. Although this deterministic experience collection works well for the posterior replay, there is still a potential to improve the efficiency by using a preferential way. In this paper, we propose a preferential experience collection (PEC) method for experience replay in model-free DRL, where the reward is composed of extrinsic and intrinsic components. Since designing a good extrinsic reward is difficult especially in a complicated environment, this paper considers to establish a novel self-supervised intrinsic reward (IR) for model-free DRL. By looking into the frequency domain, the intrinsic reward is designed such that the agent can understand the behavior itself to some extent in addition to completing the task. Extensive experimental results demonstrate that our method can improve the efficiency and stability of the model-free DRL.
Does this persona represent me?

Personas are fictional representations of an end user based on data collected from a specific target audience. The creation of personas takes time, because part of the process is manual, which causes difficulties during the stages of data generation and analysis, if the data sample is large. The present work aims to investigate the results of the automatic generation of personas through a questionnaire and the Clustering method. Initially, a questionnaire was applied in order to outline the profile of students in the Computer Science and Software Engineering courses at the university Federal of Ceará. 130 responses were obtained from this applied questionnaire. The automatic generation of personas used the collected responses as a database, these data were applied to the Clustering method. From Clustering, using a specific tool for this purpose, it was possible to generate four personas automatically. In relation to the data generated from the personas, it was possible to identify some characteristics, such as: (a) course choice factor: the opportunity to work with new technologies, influence of family and friends and the possibility of living abroad; (b) factor of withdrawal from the course: lack of identification with the course, learning difficulties in the disciplines and finance; and, (c) area of interest: software engineering, human-computer interaction and computer graphics. A new questionnaire was applied in order to validate the personas generated in relation to credibility, empathy and similarity. Based on the results obtained, it was observed that two of the four personas generated achieved greater prominence in relation to the validation criteria, showing that the personas look like real people, students think like the personas and share similar interests.