Reviewerly/exHarmony huggingface dataset:
Loners Stand Out. Identification of Anomalous Subsequences Based on Group Performance

Time series analysis is a part of data mining and nowadays an important field of research due to the increasing amount of data that is recorded sequentially by various systems. Especially the identification of anomalous subsequences arouses great interest, since a manual search for errors or malfunctions is not possible in most cases. Often outliers are defined as points or sequences that deviate significantly from the course of one or multiple time series, yet there are also applications where the trend rather than the exact course of time series is relevant. In that case, there is an approach of clustering the time series per time point and analyzing their cluster transitions over time. Sequences that change their cluster members suddenly or often, indicate an anomaly. In 2019, a novel approach for the detection of these transition-based outliers was introduced [19]. Now, we present an algorithm called DACT (Detecting Anomalies based on Cluster Transitions) that is able to identify outlier sequences of the same type. It is a simple approach that stands out due to different results, although a similar type of anomalies is targeted. In the evaluation, we examine and discuss the differences. Our experiments show, that the results are competitive and reasonable.
Future Moment Assessment for Action Query

In this paper, we aim to tackle the task of Assessing Future Moment of an Action of Interest (AFM-AI). The goal of this task is to assess if an action of interest will happen or not as well as the starting moment of the action. We aim to assess starting moments at any time-horizon of the future. To this end, we tackle the regression task of the starting moments as a generation task using a Deterministic Residual Guided Variational Regression Module (DR-VRM), which is built on a Variational Regression Module (VRM) and a deterministic residual network. The VRM takes the uncertainty into account and is capable of generating diverse predictions for the starting moment. The deterministic network encourages the VRM to learn from deterministic residual information in order to generate more precise predictions for moment assessment. Experimental results on three datasets clearly show that the proposed method is capable of generating both diverse and precise predictions of starting moments for query actions.
Data center network acquisition symptom algorithm based on Hamiltonian Cycle

In the network, fault diagnosis includes two major phases: first the test result (symptom) is obtained through the test model; and second, the faulty node is determined by applying a diagnosis algorithm to the symptoms. In this paper, we mainly study the first stage, and an efficient algorithm based on Hamiltonian cycle---ASHC (acquiring symptom based on Hamiltonian cycle) is proposed, which mainly sends test data packets via Hamilton cycle to test each server and record the test results, then we use simple comparison models for test results to obtain test symptoms. The advantage of this algorithm is that the central server does not need to communicate with each server directly, so as to save system resources.
Durable Implementation of Multi-cloud Storage with Assured Integrity for Sensitive Information

Cloud computing has gained an excellent response across several industries due to the agility and elasticity it offers. With the increase in the usage of cloud computing in both businesses and research industries, there was a significant ascent in threats and vulnerabilities. The cloud since, its inception, has a layered architecture with security deployed at each layer. Risks like access ambiguity, lack of central governance, data loss, data leakage, and data privacy are rising day by day. A multi-cloud strategy is to mitigate these risks and vulnerabilities. Even then, issues of data loss, privacy persist. This paper presents a methodology that reduces these limitations with the design and implementation of multi-cloud architecture and shuffling algorithm. The application developed provides security to the data deployed on the public cloud as well as maintains data reliability. This paper addresses the issues of data loss and theft. This design-pattern focuses on micro-services that also require reliability and availability of data.
FACTORIZATION LENGTH DISTRIBUTION FOR AFFINE SEMIGROUPS III: MODULAR EQUIDISTRIBUTION FOR NUMERICAL SEMIGROUPS WITH ARBITRARILY MANY GENERATORS

Abstract For numerical semigroups with a specified list of (not necessarily minimal) generators, we describe the asymptotic distribution of factorization lengths with respect to an arbitrary modulus. In particular, we prove that the factorization lengths are equidistributed across all congruence classes that are not trivially ruled out by modular considerations.
Ruggedizing LTE Security Using Hybridization of AES and RSA to Provide Double Layer Security

Achieving better security for the transmission of data over cellular networks has always being a hard nut to crack. Security of long-term evolution (LTE) network is prime concern because of existence of malicious attackers. The security of network can be enhanced by utilizing cryptography. However, each cryptographic algorithm has its own pros and cons. So, hybridization (merging two or more algorithms) is one of the solutions to achieve better security. In this paper, double layer protection is implemented in order to ruggedize the security mechanism of LTE networks. The proposed algorithm utilizes better speed of AES and robust security of Rivest–Shamir–Adleman (RSA) together to achieve a hybrid system with improved performance compared to the traditional algorithm. The cryptanalysis has been intricated by increasing the number of keys to six. Further, the time required for encryption of keys is less when compared to decryption making the network more rugged and secure. In order to further enhance the speed, the concept of parallel processing is utilized. Then, proposed framework is analysed based on various parameters, and it is experimentally concluded that it provides approximately 22.7% increment in acceleration when compared to encryption rate and about 22.3% increment in acceleration when compared to decryption rate. 10% enhancement in percentage of avalanche effect has been achieved thereby increasing confusion in ciphertext and reducing chances of decoding. 5.39% increment in PSNR values of the proposed algorithm has been achieved when compared to existing algorithm.
Cluster-Based Ensemble Using Distributed Clustering Approach for Large Categorical Data

The modern data analytics approaches suffer through the various challenges when dealing with large datasets, and thus, there is qualitative and quantitative degradation in the decision-making process. There is a need of an efficient analytical system that can handle on large and diverse datasets with increased accuracy in data analysis. Here, a prototype model of a cluster-based ensemble system is proposed to run on commodity machines using hierarchical approach. The commodity machines create the cluster-based ensemble in a distributed environment using a popular data mining algorithm. The experimentation proves that hierarchical approach helps to decrease time for data mining process, in turn boosts the speed of decision-making and the cluster-based ensemble improved the accuracy.
Pre-verification of Data in Electronic Trade Blockchain Platform

Blockchain technology has several advantages, including security, transparency, and stability. It can reduce data management cost and be applied to important electronic document related services. However, in the perspective of the subject, in order to apply the blockchain technology to electronic document-related services form, it is necessary to review the integrity of the electronic documents to verify that a stable transaction is possible. In addition, in the perspective of the consumer, because electronic documents contain content related to their important personal information or contracts, electronic documents should be proved to be stable in the blockchain trading platform. Therefore, this study aims to present an architecture that can verify the integrity of the blockchain-based electronic document platform by presenting a pre-validation method for electronic documents. Furthermore, by establishing an actual pilot system based on this architecture, a system where customers can reliably understand their transactions and contracts was made.
On the automorphism group of a binary form associated with algebraic trigonometric quantities

Let F(x,y) be a binary form of degree at least three and non-zero discriminant. In this article we compute the automorphism group Aut F for four families of binary forms. The first two families that we are interested in are homogenizations of minimal polynomials of 2cos⁡(2πn) and 2sin⁡(2πn), which we denote by Ψn(x,y) and Πn(x,y), respectively. The remaining two forms that we consider are homogenizations of Chebyshev polynomials of first and second kinds, denoted Tn(x,y) and Un(x,y), respectively. For a video summary of this paper, please visit https://youtu.be/1Ge-cm4ilek.
Stain-Refinement and Boundary-Enhancement Weight Maps for Multi-organ Nuclei Segmentation

Nuclear detection and segmentation in pathological images is a crucial prerequisite for finding and forecast of numerous ailments. With the advancement of deep learning, high precision automatic segmentation of multi-organ nuclei pathological images is possible. The segmentation results require not only accurate foreground prediction but also careful annotation of every individual object. There are still several remaining challenges in multi-organ nuclei segmentation. Firstly, blurred boundaries and inconsistent staining make pixel-wise segmentation difficult to generate occlusive object masks. Secondly, the background noise will be retained or even enhanced after preprocessing. Thirdly, the differences in size, shape, and intensity of different organs make it harder to separate touching nuclei. Two novel weight maps of loss function are proposed, which make full use of structural information provided by raw image and its corresponding annotations, in order to supervise the network learning more available features. The Stain Refinement Weight Map focuses on the structural difference between Hematoxylin channel and Eosin channel, which has been neglected in existing methods, to highlight potential noise pixels. The Boundary-Enhancement Weight Map leverages the new boundary annotation of each individual object that has emerged in the recent dataset to help the network better divide cell clusters. We test our method on Multi-Organ Nuclei Segmentation Dataset and show that our proposed method has high accuracy in nuclei detection and can separate touching nuclei effectively in the segmentation task. Contribution-The main contribution of this paper is to propose two pre-defined novel weight maps to help the network extract better features.
Enhancing Neural Network Explainability with Variational Autoencoders

Machine intelligence has been used to tackle increasingly complex problems and deep learning solutions are at the forefront of tackling these problems. In general, these architectures have a great number of parameters that are methodically updated in training. The vast number and complexity of deep neural networks makes it very difficult to decipher the inner workings of the neurons and layers that make up the network. This paper posits that trustworthiness and trust in autonomous systems are increased through eXplainable Artificial Intelligence (XAI) and presents a method that enhances the explainability and understanding of a neural network decision. We leverage variational autoencoders to produce human interpretable features from complex data sets. We show that the explainable features can then be used for machine learning applications. Explainability inspires trust in autonomous systems that use deep learning, which is necessary for safety critical systems.
3D real-time path planning of UAVs in dynamic environments in the presence of uncertainty

Unmanned Aerial Vehicles (UAVs) are being integrated into all spheres of life varying in a wide range of applications from military to civil applications. In such applications, UAVs are expected to operate safely in the presence of uncertainties present in the dynamic environment and the UAV itself. Based on literature different uncertainty sources are identified, quantified and modelled using bounding shapes. The UAV model, path planner parameters and four different complexity scenarios with different moving shapes are defined in view of real UAVs. For analysis uncertainty is varied between 2% and 20% for UAV position and obstacle position and orientation. Results show that both types of uncertainty deteriorate path planning performance of both A* and RRT algorithms for all scenarios considered especially for RRT. RRT results in faster and shorter paths with approximately the same success rate (\> 95%) as A* for simple scenarios. For complex scenarios A* performs better. This work shows that 3D real-time path planning in different obstacle density, moving obstacle environments in the presence of uncertainty is possible.
MuRF-Net: Multi-Receptive Field Pillars for 3D Object Detection from Point Cloud

In this paper, we propose a point cloud based 3D object detection framework that accounts for both contextual and local information by leveraging multi-receptive field pillars, named as MuRF-Net. Recently, common pipelines can be divided into a voxel-based feature encoder and an object detector. During the feature encoding steps, contextual information is neglected, which is critical for the 3D object detection task. Thus, the encoded features are not suitable to input to the subsequent object detector. To address this challenge, we propose the MuRF-Net with a multi-receptive field voxelization mechanism to capture both contextual and local information. After the voxelization, the voxelized points (pillars) are processed by a feature encoder, and a channel-wise feature reconfiguration module is proposed to combine the features with different receptive fields using a lateral enhanced fusion network. In addition, to handle the increase of memory and computational cost brought by multi-receptive field voxelization, a dynamic voxel encoder is applied taking advantage of the sparseness of the point cloud. Experiments on the KITTI benchmark for both 3D object and Bird's Eye View (BEV) detection tasks on car class are conducted and MuRF-Net achieved the state-of-the-art results compared with other voxel-based methods. Besides, the MuRF-Net can achieve nearly real-time speed with 20Hz.
Fast copula variational inference

Mean-field variational inference, built on fully factorisations, can be efficiently solved; however, it ignores the dependencies between latent variables, resulting in lower performance. To address this, the copula variational inference (CVI) method is proposed by using the well-established copulas to effectively capture posterior dependencies, leading to better approximations. However, it suffers from a computational issue, where the optimisation for big models with massive latent variables is quite time-consuming. This is mainly caused by the expensive sampling when forming noisy Monte Carlo gradients in CVI. For CVI speedup, in this paper we propose a novel fast CVI (abbr. FCVI). In FCVI, we derive the gradient of CVI objective by an expectation of the mean-field factorisation. Therefore, we can achieve a much efficient sampling from the D-dimensional mean-field factorisation, enabling to reduce the sampling complexity from O(D2) to O(D). To evaluate FCVI, we compare it against baseline methods on modelling performance and runtime. Experimental results demonstrate that FCVI is on a par with CVI, but runs much faster.
Attention Mechanism Balances Semantic Representation and Syntactic Representation

Semantic information and syntactic information are two essential dimensions of language. In recent years, most researches focus on extracting semantic information or how to extract useful information from syntactic dependency tree. As the semantic representations are closely related to syntactic ones, how to effectively integrate semantic and syntactic information is still a subject worth studying. We propose attention mechanism based on graph convolutional networks. The model integrates syntactic dependence information into semantic features and automatically achieve a balance between semantic information and syntactic information. We conducted experiments on the task of sentence-level relation extraction, and the results show that our model performs better than some previous methods.
Comparison of Tree Based Classifications and Neural Network Based Classification

In this paper, we empirically analyze and compare the performance of neural network-based classification (MLP) and decision tree based classifications (CART and Random Tree) on data sets with banking and medical purpose information. We included structural parameters for distinguishing the classification methods. We also introduced up sampling and down sampling along with feature selection and found a more detailed analysis of the Precision, Recall, F1 score, Area under the Curve, Test and Train accuracies which are necessary in order to judge the performance of the learning and classification method of all the models. However, we identified some limitations involved with these sampling techniques during the research such as loss of vital data and overfitting outcomes.
Obstacle Detector and Qibla Finder for Visually Impaired Muslim Community

Vision impairment remains a serious global health concern in poor and developing countries. Indonesia has the highest blindness rate in the Southeast Asian region. The majority of the roughly 3.0 million visually impaired individuals have Islamic faith. A Salah means "worship", is an obligatory religious duty for every Muslim which is observed 5 times a day and performed in a whole series of movements. It requires them to face the direction of the Qibla that is oriented towards the Kaaba in the city of Mecca that connects the believers. In this research, a portable, lightweight, and low-cost assistive device was placed in a traditional Muslim headwear Peci, that will help them not only to avoid head collisions but also to determine the correct Qibla prayer direction. The module integrated obstacle detection, direction finder, and notification system. First, the ultrasonic sensor will be embedded in the front of the Peci for obstacle detection. Second, a digital compass and global positioning system receiver will be utilized to determine the coordinates of the person and to calculate the direction of the Qibla. Third, selection control between the obstacle detector and the Qibla finder functions will be done through the use of switches. Fourth, notification is done via a vibration motor. Fifth, a Blynk mobile application will be developed to provide family members with the real-time location of the visually impaired loved one.
A Novel Approach for Acquiring Training and Software Security Requirements

Security is an important software quality attribute albeit, as a nonfunctional requirement, often overlooked. Although several approaches for security requirements engineering exist, it is not clear how to adapt security requirements to software end users. This poster aims to fill in this gap by developing a novel approach for acquiring security requirements by leveraging end user analysis and a security body of knowledge. To achieve this aim, we divide security requirements into two categories. Software security requirements are those that need to be technically implemented in the developed software. Training security requirements are those that aim to provide the necessary training to the end users that need it. The proposed approach may help security experts in security requirements engineering tailored to the characteristics of end users.
Augmented Reality for 3D House Design Visualization from Floorplan Image

Smartphones currently support the augmented reality feature, capable of displaying virtual objects into the real world environment at a specific location designated with a marker. This research develops an augmented reality application on a smartphone to visualize a 3D house design. The floorplan image captured by the smartphone camera is designated as a marker and then sent to the server. The server-side application, built using deep learning and integer programming techniques, detects corner positions on the image and produces a vector containing 2D coordinates. The client application on the smartphone uses the 2D coordinates to draw a 3D house model using Unity 3D. The model is then added over the floor plan image marker using Vuforia. Based on the test results, the 3D house models can be drawn according to the provided floorplan with moderate accuracy. The augmented reality application offers an alternative approach to examine a floorplan so that a user can better understand and engage with the design.
An Improved Algorithm for Bert

Natural language processing (NLP) has been widely applied in machine translation, speech recognition and other fields. Many elegant results have been proposed such as text classification, sentiment analysis. In recent years, the Bert model has made a great breakthrough in many NLP tasks. Afterwards, using transformer as a feature extractor has been achieving good performance in many scenarios. However, there are too many model parameters in the Bert model. In order to solve this problem, we propose an improved algorithm for the Bert model, which shares the first 11 layers' parameters. The proposed method could largely reduce the number of parameters in the model. We also conduct experiments to evaluate the algorithm's performance; the results also validate that the improved algorithm for the Bert model could achieve good results.
A Cooperative Framework for Autonomous Landings of Quadrotors using Vision on a Moving UGV

Unmanned ground vehicles (UGVs) have become popular for deploying and recharging quadrotors in various persistent applications. In order to accurately land the UAV on the UGV, efficient landing controllers must be designed. Further, a single UGV may need to service several quadrotors and hence the UGV and UAVs need to coordinate with each other for landing and take-off. In this paper, we present a State Dependent Riccati Equation (SDRE)-based landing controller with vision in the loop and a cooperative scheduling scheme for sequential landing of the UAV on the UGV with active interaction with the vehicles. Simulations results are presented to show the efficacy of the landing and scheduling for different trajectories.