Reviewerly/exHarmony huggingface dataset:
Development of Security Clustering Process for Big Data in Cloud

Big data is a large volume of data that demands effective cost and novel forms of data to process for enhanced security imminent innovation, assessment creation, and optimization process. Cloud computing provides a consistent, fault lenient, and extensible background to the big data disseminated managing systems. Cloud computing is emerged that has a popular computing model of system of servers hosted over the Internet for the store, manage, and process the data. It includes several advantageous characteristics of cloud storage are easy to access, scalability, resilience, cost efficiency, and reliability of the data. Since every organization is moving its data to the cloud and uses the storage services provided by the cloud. However, threats to cloud security such as data loss, data breaches, insecure application programming interface, account or service traffic hijacking, denial of service, malicious insiders, shared technologies and dangers, insufficient diligence are moving the security of the data. So, it is essential to protect the information against denial of services, unauthorized access or modification of data. The proposed method provides the vital security for different environment to store and access the information file in big data cloud. A numerous levels of security services such as authorization, authentication, data integrity, and confidentiality are ensure by applying the proposed method; therefore, only the genuine users are allowable to store and access the data through this method. The proposed method has multiple levels of security such as authorization by the Cloud Service Provider, encryption of the data using different cryptographic algorithms though uploading the data to the cloud, and usage of one time password for decrypting the data while downloading it from the big data cloud.
An Experimental Study on 3D Person Localization in Traffic Scenes

This paper presents an experimental study on 3D person localization (i.e. pedestrians, cyclists) in traffic scenes, using monocular vision and LiDAR data. We first analyze the detection performance of two top-ranking methods (PointPillars and AVOD) on the KITTI benchmark, with respect to varying Intersection over Union (IoU) settings and the underlying parameters of 3D bounding box location, extent and orientation. Given that the KITTI dataset contains relatively few 3D person instances, we also consider the new EuroCity Persons 2.5D (ECP2.5D) dataset, which is one order of magnitude larger. We perform domain transfer experiments between the KITTI and ECP2.5D datasets, to examine how these datasets generalize with respect to each other.
Bearings-only multitarget tracking based onRao-Blackwellized particle CPHD filter

Following Mahler’s framework forinformation fusion, this paper develops a implementationof cardinalized probability hypothesis density (CPHD)filter for bearings-only multitarget tracking.Rao-Blackwellized method is introduced in the CPHDfiltering framework for mixed linear/nonlinear state spacemodels. The sequential Monte Carlo (SMC) method is usedto predict and estimate the nonlinear state of targets.Kalman filter (KF) is adopted to estimate the linear stateswith the information embedded in the estimated nonlinearstates. The multitarget state estimates are extracted byutilizing the kernel density estimation (KDE) theory andmean-shift algorithm to enhance tracking performance.Moreover, the computational load of the filter is analyzedby introducing equivalent flop measure. Finally, theperformance of the proposed Rao-Blackwellized particleCPHD filter is evaluated through a challengingbearings-only multitarget tracking simulation experiment.
Book Review of Corpus Stylistics: Theory and Practice

There has been a surge of interest in applying quantitative approaches to stylistics in recent decades. These researches can be roughly divided into two branches, i.e., quantitative or computationa...
2<sup>n</sup>+1-valued SSS-Net: Uniform Shift, Channel Sparseness, and Channel Shuffle

Convolutional neural networks (CNNs) are primarily a cascaded set of pattern recognition filters, which are trained by big data. It enables us to solve complex problems of computer vision applications. A conventional CNN requires numerous parameters (weights) and computations. In this study, we propose SSS-Net using uniform channel shift, weight sparseness, and channel shuffle operations. We prove that a conventional k × k kernel convolution can be divided into k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of channel shift operations and a point-wise (1 × 1) convolution. We develop a 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> +1-valued quantization with zero weight sparseness technique. We investigate a weight distribution for a post-training CNN, and almost all weights are close to zero. We eliminate such small weights to reduce the model size and non-zero weights are efficiently quantized by 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -valued one. We show an algorithm for a uniform shift with quantization. Since a uniform shift operation requires no multiplications, the amount of computation becomes zero. We train SSS (Shift, Sparseness, and Shuffle)-Net by ImageNet 2012 benchmark image. Compared with existing CNN models, our SSS-Net is a smaller model size and MAC operations, while it has considerable recognition accuracy.
Distortion Compensation in Structured Lighting Systems

The article discusses a modification of the structured lighting method. The difference between the proposed method and classical optical methods for measuring the profile of objects using structured illumination is that the phase of the projected images is demodulated taking into account the change in the intensity of the set of sinusoidal patterns projected onto the object when phase shifts of a known value are introduced. This improves the quality of the measurements. An experimental verification of the proposed method has been carried out.
Learning the Structure of the Tree and Tree Augmented Naive Bayesian from Incomplete and Imbalanced Data

In many fields, such as decision, diagnosis, or predication, the Bayesian network formalism is becoming increasingly common, that especially thanks particularly to its inferential capabilities, even when data is incomplete and imbalanced. In this paper, we present an approach to learn the structure of Chow-Liu and Tree Augmented Naïve Bayes (TAN) from incomplete and imbalanced datasets.
No-attachment head tracking method of robotic transcranial magnetic stimulation based on vision sensor

A vision-based head tracking method for robotic transcranial magnetic stimulation (TMS), requiring no attachments to patient, is proposed to track the movements of patient's head during treatment. First, a facial landmark detection method based on Constrained Local Neural Field (CLNF) algorithm is used. Second, a method of head location and pose estimation is presented and a filter for image restoration is used for a higher accuracy. Finally, calibration approaches are proposed based on the coordinates of the robot arm and camera and real-time motion tracking system. Experiment results indicate that the accuracy and real-time performance of this head tracking method satisfy the requirements of TMS treatment.
PruneFaceDet

Face detection is the basic step of many face-analysis tasks. In practice, face detectors usually run on mobile devices with limited memory and computing resources. Therefore, it is important to keep the face detectors lightweight. To this end, current methods usually focus on directly design lightweight detectors. Nevertheless, the resource consumption of the lightweight detectors could be further suppressed. In this paper, we propose to apply the network pruning method to the lightweight face detection network, which can further reduce the face detector's parameters and floating point operations (FLOPs). To identify the channels of less importance, we perform the network training with sparsity regularization on channel scaling factors of each layer. Then, we remove the connections and the corresponding weights with the near-zero scaling factors after the sparsity training. We apply the proposed pruning pipeline on a state-of-the-art face detection method, EagleEye [5], and get a shrunken EalgeEye model which has a reduced number of computing operations and parameters. The shrunken model could achieve comparable accuracy as the unpruned model. By using the proposed method, the EagleEye face detector achieve 57.2% reduction of parameter size with 2% accuracy loss on WiderFace dataset.
Implementation of Multiple Model: Simulation and Experimentation for Future State Prediction on Different Classes of Small Unmanned Aerial Systems

In this paper, we present the implementation of a multiple model system to generate future position estimates and enable safe deployment of small unmanned aerial systems (sUAS) in the national airspace (NAS). The system was developed in the ROS-Gazebo physics engine environment. The proposed system was developed for multi-rotor and fixed wing simulations. The algorithm was also tested in simulations to track multiple sUAS simultaneously. The algorithm has been verified using the 3DR experimental quadcopter platform. The multiple model system is executed on the ground control station (GCS) and does not interfere with the vehicle in flight. The multiple model only requires current position information from the vehicle to predict future state of the vehicle. The multiple model incorporates two motion models, viz. the constant velocity (CV) model and the constant acceleration (CA) model. These two models are capable of tracking the expected dynamics of commercial sUAS in the national airspace. These motion models were tuned by incorporating kino-dynamic constraints of sUAS. The structure of the multiple model and information of the sub-modules are presented in detail. Further, the Gazebo simulations for multi-rotor, fixed wing and multi vehicle are presented. Hardware results for a single multi-rotor are included to show the practical application of the proposed system.
Recursive RNN Based Shift Representation Learning for Dynamic User-Item Interaction Prediction

Accurately predicting user-item interactions is critically important in many real applications including recommender systems and user behavior analysis in social networks. One limitation of most existing approaches is that they use the sparse user-item interaction relationships directly, but ignore the second order user-user and item-item relationships. Another limitation is that they generally embed users and items into different embedding spaces in a static way, but cannot capture the dynamic and evolving dependency between users and items and embed them into a unified latent space. In this paper, we aim to learn dynamic embedding vector trajectories rather than static embedding vectors for users and items simultaneously. A Recursive RNN based Shift embedding method called RRNN-S is proposed to learn the continuously evolving embeddings of users and items for more accurately predicting their future interactions. Specifically, we first propose to quantize the user-user and item-item relationships from the original user-item interaction graph, which can be used as auxiliary information to enrich the sparse user-item interaction graph. A recursive RNN is proposed to iteratively and mutually learn the dynamic user and item embeddings in the same latent space based on their historical interactions. A shift embedding module is next proposed to predict the future user embedding. To predict the item which a user will interact with, we innovatively output the item embedding instead of the pairwise interaction probability between users and items, which is much more efficient. Through extensive experiments on two real-world datasets, we demonstrate that RRNN-S achieves superior performance by comparison with several state-of-the-art baseline models.
A Real-Time Learning-Based Super-Resolution System on FPGA

This paper proposes a real-time super-resolution (SR) system. The proposed system performs a fast SR algorithm that generates a high-resolution image from a low-resolution image using direct regression functions with an up-scaling factor of 2. This algorithm contained two processes: feature learning and SR image prediction. The feature learning stage is performed offline, in which several regression functions were trained. The SR image prediction stage is implemented on the proposed system to generate high-resolution image patches. The system implemented on a Xilinx Virtex 7 field-programmable gate array achieves output resolution of [Formula: see text] (UHD) at 85 fps and 700Mpixels/s throughput. Structure similarity (SSIM) is measured for image quality. Experimental results show that the proposed system provides high image quality for real-time applications. And the proposed system possesses high scalability for resolution.
Anomaly Detection in ICS based on Data-history Analysis

Data of industrial control systems (ICS) are increasingly subject to cyber attacks which should be detected by approaches such as anomaly detection before they can take effect. However, examples such as Stuxnet, Industroyer or Triton show that, despite all the precautions taken, it is still possible to overcome anomaly detection systems and cause damage. Similarly, damage can be made by intentional malicious and unintentional changes by employees in programming or configuration of ICS components. An example is an employee who unintentionally manipulates a machine's configuration to a higher temperature limit than it should have. The potential consequence would be that the machine overheats and breaks. The aim of the project MADISA (Machine Learning for Attack Detection Using Data of Industrial Control Systems) is to identify such anomalies in the data of ICS by examining the data-sets and creating a machine learning system (MLS) based on heuristics over meta-data, configurations and code content. For this purpose, this poster provides a structured analysis of real-world projects from a German automobile manufacturer which lead to first attributes in this unexplored approach for creating heuristics to anomaly detection of historic data in ICS.
Efficient vCPU Utilization for Reducing Energy Consumption in Cloud Data Centers

As the demand for cloud computing services continues to grow, the requirement for expanding cloud data centers also increases. One main issue facing this growth is the huge amount of energy consumed by the cloud data centers. The massive energy consumption expenses considered the main problem for cloud service providers. Recent reports revealed that the electricity expenses of Information and communications technology or (ICT) devices occupy 42% per month of the total budgets. Nevertheless, the continuous increase in energy consumption has become the main challenging subject. Due to this reason, researches have proposed many techniques and approaches (such as virtual machine VM consolidation, Voltage and Frequency Scaling, VM migration policies, etc.) for addressing this issue. This paper presents a study to evaluate the influence of controlling virtual machine central process unit (vCPU) on energy consumption of Cloud data center. CloudSim simulator is used to apply the dynamic voltage and frequency scaling (DVFS) technique with the proposed approach while processing different types of general purpose cloud computing applications (video streaming, file compression process, and video games). Results indicate that about 13% of data center energy was saved compared with the base DVFS system. This saving percentage result was a better percentage comparing with other results obtained from previous work.
Generation of all-to-all connections in a two-dimensional qubit array with two-body interactions

All-to-all connections are required in general quantum annealing machines to solve various combinatorial optimization problems. The Lechner, Hauke, and Zoller (LHZ) method, which is used to realize the all-to-all connections, requires many-body interactions in locally connected qubits. Because most of the qubit interactions are two-body interactions, Lechner also proposed the construction of each four-body interaction by six controlled-NOT (CNOT) gates between two qubits. However, it is difficult to construct many CNOT gates. Herein, we show more concrete sequences to produce four-body and three-body interactions based on a two-dimensional solid-state qubit system. We show that the number of operations needed to construct the many-body interactions can be reduced using appropriate pulse sequences. These findings will help reduce quantum computation costs for solving combinatorial problems.
Detecting Struct Member-Related Memory Leaks Using Error Code Analysis in Linux Kernel

Struct member-related memory leak can become a serious problem. Linux kernel is not an exception. According to our study of Linux Kernel patches, 54.6% of all memory leak-related patches within the last two years were related to the leak of struct members. This occurs when a struct is freed before freeing its dynamically allocated struct members. Detecting these bugs in large-scale software requires to reduce analysis cost for scalability and effectively collect the state of a struct and its members.In this paper, we present a simple static-analysis approach to detect struct member-related memory leak in the Linux Kernel. Our analysis first collects alloc/free information by conducting a path-insensitive analysis. To efficiently conduct inter-procedural analysis, we introduce error-code analysis, which is an optimization to efficiently pass back the alloc/free information by focusing on the return value of callee and its use in the caller. When detecting a struct free, we scan through the collected information to detect any member that remains unfreed, and generate warnings to them. We evaluated our method by analyzing the Linux Kernel 5.3-rc4, and found two new bugs. Both of the bugs were reviewed and confirmed by Linux Kernel developers.
Combining Natural Language Annotation with Transformer for Thai Sentence Classification

Sentence classification is one part of the NLP tasks that have a wide range of applications including question answering, sentiment analysis, and much more. The Challenge of sentence classification is more ambiguous because they might have not enough contextual information. While this is not a simple task, it is becoming increasingly important to many applications. In this study, we propose to classify sentences to a wh-question category based on combined Natural Language Processing annotation (Part-of-Speech tags and Named Entity Recognition) and attention transformer mechanism. The experimental results on both interrogative sentences and simple sentence datasets showed that the proposed model improves the performance of sentence classification to the wh-question category with promising accuracy.
Extending the Whale optimization Algorithm with Chaotic Local Search

The Whale optimization Algorithm (WOA) is a recently introduced metaheuristic that has shown a promising performance in solving optimization problems. To further improve the search performance of WOA, the Chaotic Local Search (CLS) mechanism is incorporated into WOA. Ten different chaotic maps are investigated to give insights into the influence of CLS in the WOA. The goal is to compare the performance of the Whale optimization Algorithm with Chaotic Local Search (WOACLS) with the standard WOA and the Chaotic WOA (CWOA) by applying them to twenty well-known benchmark functions. Results show that the performance of WOACLS depends on both, the characteristics of the embedded chaotic map and the properties of the search landscape of the optimization problems considered.
Capacity Aware Active Monitoring Load Balancing Principle for Private Cloud

Virtual machines (VMs) are the basic compute elements in cloud computing. There are load balancing principles associated with a job scheduler assigns the requests to these computing elements. Deploying an effective load balancing principle enhances better performance that ultimately achieves users’ satisfaction at the high level. Assigning an equal requests load appropriate to the capacity of the VMs will be a fair principle that can be the objective of any load balancing principle. Active monitoring load balancing principle assigns the requests to a server based on the pre-computed threshold limit. This paper presents a technique for assessing the capacity of the VMs based on a common attribute. This work measures each VM’s processing ability as a percentage using the statistical method called Z-score. A threshold is quantified and the requests are proportioned based on this threshold value. Each server is then assigned with the proportioned requests. Suitable experiments were conducted Requests Assignment Simulator (RAS), a customized cloud simulator. The results prove that the performance of the proposed principle is comparatively better than a few load balancing principles. Areas of future extension of this work were also identified.
An Application Suite: Effectiveness in Tracking and Monitoring of Skill Training Programs

Conducting and monitoring skill training programs for rural population always involves a huge amount of administrative tasks and tracking processes. To ensure the effectiveness and efficiency of the program it is mandatory to implement a proper tracking and monitoring system. In this paper, we present a software application suite for monitoring and tracking of the PMKVY (Pradhan Mantri Kaushal Vikas Yojana) training programs in the rural settlements of India conducted by an NGO. We discuss the common challenges and the design considerations that were used while designing and implementing the application. Sentiment analysis was applied on the feedback received through the mobile application and it was observed that 66% of the feedback were of positive polarity. Also, the word frequency analysis on the same data revealed that the feedback was mostly about the students and that too on a daily basis. With these observations, the potential to apply NLP in the future is also discussed.
A Tracking Algorithm with Dynamic Response Fusion Mechanism

In order to improve the accuracy of the traditional correlation filtering tracking algorithm (staple) in the light changing scene, we introduce the dynamic fusion method to overcome the influence of illumination on the tracking accuracy, and dynamically highlights the target candidate probability area. According to the object movement characteristics in a short time, we use a mask to removes some redundant points in the search area. Compared with other common tracking algorithms on OTB-100 target tracking data set, the average success rate and tracking accuracy are better than other algorithms.
Context as knowledge

It has been argued that common ground information is unsuited to the role that contexts play in the theory of indexical and demonstrative reference. This paper explores an alternative view that identifies shared information with what is common knowledge among the participants. We argue this view of shared information avoids the problems for the common ground approach concerning reference while preserving its advantages in accounting for communication.