Reviewerly/exHarmony huggingface dataset:
WITHDRAWN: High performance research implementations with third party cloud platforms and services

Cloud computing is one of the evolving areas where distributed services are requested without the physical networks at the customer end. The physical tools of cloud computing are mounted at remote locations and implemented. This thesis focuses on deployment trends for researchers and professionals of real time cloud systems and platforms, in the area of cloud computing and associated technology. This research work is focusing on the integration of third party cloud platforms and services for the high performance computing in assorted domains. The integration of widely used cloud platforms are done so that the research implementations can be done effectively on assorted parameters.
A Coupling Path Analysis Method for High Power Microwave Interaction With Electronic Systems

In order to analyze the electromagnetic coupling path of real complex electronic system in high power microwave more conveniently and effectively, based on the electromagnetic topology theory, a signal flow graph drawing and simplification method based on coupling port is proposed, and the assignment principle of coupling path are given. The new method can satisfy the construction of signal flow graph in high power microwave environment with multiple interference sources and different bandwidths. Furthermore, aiming at the bidirectional coupling problem of signal flow graph and the shortest path calculation problem under different limit encountered in practical engineering application, this letter proposes an improved Floyd algorithm combined with double-sweep algorithm, which can solve the deficiency of traditional Dijkstra algorithm and Ford algorithm in solving the above engineering problems. Finally, the coupling path analysis method under the action of multiple interference sources is briefly discussed. The method proposed in this letter is more suitable for practical engineering, and it's the base of the further electromagnetic protection work.
Multifeature fusion action recognition based on key frames

Summary As an important technology in computer vision, video‐based human action recognition has a great commercial value, which has attracted extensive attention in the field of computer vision and pattern recognition in both academia and industry. To date, there are a wide variety of applications of human action recognition, such as surveillance, robotics, health care, video searching, and human–computer interaction. However, there are many challenges involved in human action recognition in videos, such as cluttered backgrounds, occlusions, viewpoint variation, execution rate, and camera motion. However, data redundancy and single feature were largely limited the accuracy of human action recognition. In this article, adopting the key frame extraction and multifeature fusion techniques, a novel action recognition method was proposed, which can improve the recognition accuracy. The main works are as follows: 1) in order to solve the problem of data redundancy, a key frame extraction method based on node contribution weighting is proposed to extract video key frames; 2) different kinds of information flows are extracted from the obtained key frame sequences, and different convolutional neural networks are used to obtain corresponding classification results and merge, so as to better complement the information in different flows. Lastly, the experimental results show that our method improves the accuracy of action recognition.
A Multipole Approach Toward On-Chip Metal Routing for Reduced EM Side-Channel Leakage

With the proliferation of smart interconnected devices, the importance of securing computational data has increased manifold. While computational security layers like cryptographic algorithms protect against software-domain vulnerabilities, they cannot prevent leakage of electromagnetic (EM) fields from its parent device, hence compromising physical layer security. Most proposed methods of reducing EM side-channel leakage involve incorporating complex blocks into the design of an integrated circuit (IC). In this letter, we take an entirely novel stance, by borrowing fundamental concepts from multipole EM field decay and adapting the same for ICs by proposing a multipole routing approach. By building proof-of-concept scaled-up routing in PCBs, we demonstrate that unlike conventional dipole-style routing, metal layers incorporating higher-order multipoles result in a much faster-decaying side-channel leakage. Through detailed measurements, we infer guidelines toward optimizing the order of multipole needed to minimize side-channel leakage, thus enabling convenient implementation of physical layer security.
Exploring Gaze-Based Prediction Strategies for Preference Detection in Dynamic Interface Elements

Digitization is currently infiltrating all daily processes, forcing casual computer users to become acquainted with unfamiliar tools. In order to avoid overstraining these users, simplified interfaces that are reduced to the functionality and content which are relevant to the individual user are imperative. Gaze-contingent systems thus monitor viewing behavior during natural system interactions to predict relevant interface elements. The prediction performance is highly dependent on the underlying features and algorithm, especially when the interface consist of dynamic elements such as videos. In this paper, we conduct two studies with a total of 233 subjects in which we record the viewers' gaze while watching videos. We then compare the quality of preference predictions for video elements of majority voting to the performance of machine learning. Our results indicate that (1) majority voting can predict preferences with an accuracy of up to 73% (66%) for two (four) elements, (2) machine learning improves the performance to 82% (74%), (3) prediction accuracy depends on the strength of the user's preference for an element, and (4) we can rank preferences for individual elements.
Smart home: Keeping privacy based on Air‐Padding

With the rapid development of the IoT, smart home plays an increasingly important role in daily life. At the same time, privacy issues have gradually aroused the concern of researchers. Past research proves that there are privacy risks when people use smart home devices and some protection methods are proposed to protect people's privacy. However, attackers can still infer people's behaviour and activities through analysing the wireless traffic of smart home devices. Herein, an attack method under complicated WiFi scenarios is demonstrated, including different locations, different buildings and different networks. In addition, a new protection method, Air-Padding, is developed, which can change the traffic patterns of smart home devices through injecting constructed packets into the link between devices and routers. The results prove that Air-Padding can prevent people's behaviour and activities from being analysed and inferred.
Automatic Image Colorization Using GANs

Automatic image colorization as a process has been studied extensively over the past 10 years with importance given to its many applications in grayscale image colorization, aged/degraded image restoration etc. In our project, we aim to generalize this process using a Generative Adversarial Network (GAN) that takes fixed size black and white images as input and obtains corresponding coloured images of the same size as output, to demonstrate GAN working and superiority. We use a special type of GAN called Conditional Generative Adversarial Network which takes a specific type of input as opposed to vectors from random probability distributions, and we primarily work on the CIFAR-10 dataset. Our goal was to build a successful CGAN model using limited resources that utilizes Google Colaboratory, Strided Convolutions and Batch Normalization to colorize images. Our resulting model is essentially a base model that beginners and students can implement and embellish to further GAN understanding and techniques.
A PSO-Based Sanitization Process with Multi-thresholds Model

Earlier, many PPDM algorithms have been proposed to conceal sensitive items in a database in order to disclose sensitive itemsets. All prior techniques, however, ignored a crucial problem in setting minimum support thresholds. Thus, a new concept of minimal support for solving this issue is proposed in this paper. In compliance with a given threshold function, the proposed approach would set a tighter threshold for an object containing several items. Experimental results are then evaluated to show the performance of the traditional Greedy PPDM approach, GA-based PPDM approaches, and the proposed PSO-based algorithm with the new flexible and minimal support function.
Configurable Hardware Architecture of Multidimensional Convolution Coprocessor

We propose a configurable coprocessor for the convolutional neural network (CNN) that suit various models of CNN. It can operate 2D standard convolution, 2D depthwise separable convolution, 3D convolution, and a fully connected layer. The proposed processing cluster consists of 72 processing units (PUs) of half-precision floating-point to assist the main processor in embedded systems. The experimental results on Artix-7 FPGA revealed that our design has 12.16 GOPs per cluster. Moreover, this architecture was designed to be scalable for the systems with higher performance.
Comparison of the Deep Learning Methods Applied on Human Eye Detection

For the fatigue driving detection of a driver wearing a mask, the traditional fatigue driving detection method cannot effectively detect the face. The characteristics of the mouth area are disappeared due to the mask's occlusion. Therefore, the extraction of fatigue features in the eye area becomes very important. The accuracy of the eye area detection will directly affect the performance of the fatigue driving detection algorithm. At present, YOLOv3 and Faster-RCNN are both excellent models in the field of target detection. Therefore, this article uses the same data set and sets the same training parameters during training. Under a unified evaluation standard, the YOLOv3 model and the Faster-RCNN model are evaluated. Experimental results show that YOLOv3 has a better effect on human eye detection under the same conditions.
Spiral-based Manta Ray Foraging Optimization to Optimize PID Control of a Flexible Manipulator

This paper presents a Spiral-based Manta Ray Foraging Algorithm (SMRFO). It is an improvement of Manta Ray Foraging Algorithm (MRFO). The original MRFO has a competitive performance in terms of its accuracy in locating an optimal solution. Its performance can be improved further provided the balanced exploration and exploitation strategies throughout a search operation are improved. A modification in the Somersault phase of the MRFO is proposed. A spiral strategy is incorporated into the Somersault phase of the MRFO. This is to guide all agents toward the best agent in spiral-based trajectory in every iteration. The spiral strategy also offers a dynamic step size scheme for all search agents during the operation. The proposed algorithm is tested on a set of benchmark functions that consist of various fitness landscapes. In terms of solving an engineering application, the proposed algorithm is applied to optimize a PID controller for a flexible manipulator system. Result of the accuracy performance test on benchmark functions shows that the proposed algorithm outperforms the original MRFO significantly. In solving the engineering problem, both SMRFO and MRFO optimize the PID control adequately good. The SMRFO-PID control tracks the bang-bang test input better than the MRFO-PID. It confirms the superiority of the SMRFO over the MRFO.
Learning Off-By-One Mistakes: An Empirical Study

Mistakes in binary conditions are a source of error in many software systems. They happen when developers use, e.g., `<;' or `>' instead of `<;=' or `>='. These boundary mistakes are hard to find and impose manual, labor-intensive work for software developers. While previous research has been proposing solutions to identify errors in boundary conditions, the problem remains open. In this paper, we explore the effectiveness of deep learning models in learning and predicting mistakes in boundary conditions. We train different models on approximately 1.6M examples with faults in different boundary conditions. We achieve a precision of 85% and a recall of 84% on a balanced dataset, but lower numbers in an imbalanced dataset. We also perform tests on 41 real-world boundary condition bugs found from GitHub, where the model shows only a modest performance. Finally, we test the model on a large-scale Java code base from Adyen, our industrial partner. The model reported 36 buggy methods, but none of them were confirmed by developers.
Research on path planning of mobile robot based on improved Deep Q Network

Abstract In order to solve the problem of slow convergence and low learning efficiency when mobile robots use ordinary DQN algorithm to plan path in an unknown environment with insufficient prior knowledge, an improved DQN algorithm is proposed. A heuristic reward function is designed to realize continuous reward and solve the problem of slow convergence of DQN algorithm caused by reward sparsity. An adaptive exploration strategy is proposed to solve the problem of balance between exploration and utilization in reinforcement learning process, and improve the efficiency of exploration. Experiments show that the improved DQN algorithm not only has high learning efficiency and fast convergence, but also the planned path is optimal.
Extraction of jet engine parameters from micro‐Doppler signatures using continuous wavelet transform

Micro-Doppler signatures in jet engine modulated (JEM) radar signals are used to extract jet engine features for automatic target recognition. Herein, a novel algorithm based on the continuous wavelet transform (CWT) is used to determine the spool rate and the number of blades of a jet engine. The return signal is represented at multiple scales to form a scalogram for time-frequency analysis. Further, the energy of the scalogram across various scales is computed and salient local maxima in the energy plot are extracted. The signals are analysed at the corresponding scales of salient local maxima and are used to extract spool rate and chopping frequency. The algorithm is applied to the simulated JEM signals affected by noise and investigated using Coiflet and complex Morlet wavelets. In the presence of noise, the proposed algorithm using both the wavelets has shown better performance when compared with the modified Hilbert Huang Transform (HHT) algorithm.
Bridging Task Expressions and Search Queries

People engage in search episodes as they have a task or a problematic situation. Often this task is not clearly expressed by the information seeker, nor directly supported by the search system. People also list their tasks using tools such as to-do applications, and while many of these could be search tasks, there is a lack of that recognition or a possible bridge to a search system. In the work reported here, we aim to create that bridge by analyzing data on both sides. In task management, we examined 1,000 to-do tasks annotated by human assessors for their appropriateness for a search engine and created a simple process to learn that classification. In search, we analyzed millions of queries in a search engine log to understand how often queries represent tasks that people express in to-do lists. Our results show that (1) we can accurately predict which of the to-do tasks are appropriate as search queries; and (2) such tasks do indeed show up in search engines as a substantial segment. Together, these findings outline an opportunity to link explicitly expressed tasks to search queries and vice versa. This has implications for both task completion and query understanding.
A novel three layer particle swarm optimization for feature selection

Feature selection (FS) is a vital data preprocessing task which aims at selecting a small subset of features while maintaining a high level of classification accuracy. FS is a challenging optimization problem due to the large search space and the existence of local optimal solutions. Particle swarm optimization (PSO) is a promising technique in selecting optimal feature subset due to its rapid convergence speed and global search ability. But PSO suffers from stagnation or premature convergence in complex FS problems. In this paper, a novel three layer PSO (TLPSO) is proposed for solving FS problem. In the TLPSO, the particles in the swarm are divided into three layers according to their evolution status and particles in different layers are treated differently to fully investigate their potential. Instead of learning from those historical best positions, the TLPSO uses a random learning exemplar selection strategy to enrich the searching behavior of the swarm and enhance the population diversity. Further, a local search operator based on the Gaussian distribution is performed on the elite particles to improve the exploitation ability. Therefore, TLPSO is able to keep a balance between population diversity and convergence speed. Extensive comparisons with seven state-of-the-art meta-heuristic based FS methods are conducted on 18 datasets. The experimental results demonstrate the competitive and reliable performance of TLPSO in terms of improving the classification accuracy and reducing the number of features.
Assisted Human-Robot-Interaction for Industrial Assembly

Human-robot collaboration is increasingly applied to industrial assembly sequences due to the growing need for flexibility in manufacturing. Assistant systems are able help to support shared assembly sequences to facilitate collaboration. This contribution shows a workplace installation of a collaborative robot (Cobot) and a spatial augmented reality (SAR) assistant system applied to an assembly use-case. We demonstrate a methodology for the distribution of the assembly sequence between the worker, the Cobot, and the SAR.
Application of Fuzzy Comprehensive Method and Analytic Hierarchy Process in the Evaluation of Network Security Level Protection Research

Abstract The data center is the country’s key information infrastructure and an important cornerstone for the development of informatization. Only by ensuring the security of the data center can the security of important data or sensitive information of the information system be guaranteed. Data center network security level protection evaluation is an important technique and means to check whether the data center is safe. At present, the traditional methods of graded protection evaluation mainly have the problems of differences in understanding of safety indicators, ambiguity of judgments, uncertainty of judgment intervals, and unreasonable division of evaluation indicator weights. In view of the above problems, this paper proposes a method for evaluating the protection of data center network security levels based on fuzzy synthesis and AHP. This method classifies and divides the evaluation indicators in Isobao 2.0 into specific weights, determines the evaluation interval, and achieves quantitative and standardization., Objective grade protection evaluation method. Finally, the method is used to realize the evaluation of the network security level protection of a data center, and an objective and fair evaluation conclusion is given.
30-day Hospital Readmission Prediction using MIMIC Data

Patient readmission to the hospital within 30 days or 365 days is a challenging problem for hospitals as they get penalized and in many cases the Center of Medicaid and Medicare (CMS) will not reimburse the hospitals for the costs associated with these readmissions. Although readmission prediction is a common problem in healthcare and has been addressed by the researchers in the machine learning community, it remains a hard problem to solve. The goal of the project proposed in this paper is to build a predictive model for 30-day readmission based on the Medical Information Mart for Intensive Care (MIMIC III) dataset, which contains admissions for intensive care unit (ICU) patients. We used ICD9 embedding's, chart events and demographics as features to train multiple classifiers including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and Multi-Layer Perceptron (MLP). Best model, Random Forest, achieved 0.65 accuracy and 0.66 Area Under the Curve (AUC).
Exploring Personal Knowledge Extraction from Conversations with CHARM

Incorporating users' personal facts enhances the quality of many downstream services. Automated extraction of such personal knowledge has recently received considerable attention. However, often the operation of extraction models is not exposed to the user, making predictions inexplicable. In this work we present a web demonstration platform showcasing a recent personal knowledge extraction model, CHARM, which provides information on how the prediction was made and which data was decisive for it. Our demonstration explores two potential sources of input data: conversational transcripts and social media submissions.
Secure Communication Using Steganography in IoT Environment

IoT is an emerging technology in modern world of communication. As the usage of IoT devices is increasing in day to day life, the secure data communication in IoT environment is the major challenge. Especially, small sized Single-Board Computers (SBCs) or Microcontrollers devices are widely used to transfer data with another in IoT. Due to the less processing power and storage capabilities, the data acquired from these devices must be transferred very securely in order to avoid some ethical issues. There are many cryptography approaches are applied to transfer data between IoT devices, but there are obvious chances to suspect encrypted messages by eavesdroppers. To add more secure data transfer, steganography mechanism is used to avoid the chances of suspicion as another layer of security. Based on the capabilities of IoT devices, low complexity images are used to hide the data with different hiding algorithms. In this research study, the secret data is encoded through QR code and embedded in low complexity cover images by applying image to image hiding fashion. The encoded image is sent to the receiving device via the network. The receiving device extracts the QR code from image using secret key then decoded the original data. The performance measure of the system is evaluated by the image quality parameters mainly Peak Signal to Noise Ratio (PSNR), Normalized Coefficient (NC) and Security with maintaining the quality of contemporary IoT system. Thus, the proposed method hides the precious information within an image using the properties of QR code and sending it without any suspicion to attacker and competes with the existing methods in terms of providing more secure communication between Microcontroller devices in IoT environment.