Reviewerly/exHarmony huggingface dataset:
IT Applications

IT revolution is the largest one which is transforming the world completely. These applications are providing new ways of managing the organizations. Study was conducted on the employees of Natural Disaster Management Authority (NDMA) of Pakistan. Electronic gadgets like Internet, mobile phones, cloud computing, unmanned vehicles are some application of IT which have positively impacted the efficiency of organizations against the natural crisis like earth quake, flood and viral diseases etc. Study has revealed that these application has improved the management to quickly respond against crisis. Moreover performance of organization found improved. Outcomes of the study show that through internet and social media platforms, information spread fastly which help to minimize the effect of crisis. Communication devices like cell phones help public to communicate timely to deliver information to the rescue departments. Now a days disaster managements departments are collaborating and sharing experiences with each other by using IT applications.
Kernel-Based Autoencoders for Large-Scale Representation Learning

A primary challenge in kernel-based representation learning comes from the massive data and the excess noise feature. To breakthrough this challenge, this paper investigates a deep stacked autoencoder framework, named improved kernelized pseudoinverse learning autoencoders (IKPILAE), which extracts representation information from each building blocks. The IKPILAE consists of two core modules. The first module is used to extract random features from large-scale training data by the approximate kernel method. The second module is a typical pseudoinverse learning algorithm. To diminish the tendency of overfitting in neural networks, a weight decay regularization term is added to the loss function to learn a more generalized representation. Through numerical experiments on benchmark dataset, we demonstrate that IKPILAE outperforms state-of-the-art methods in the research of kernel-based representation learning.
Development and Comparison of Different Implementations of Fuzzy Logic for Physical Capability Assessment in Knee Rehabilitation

Since its beginning, artificial intelligence (AI) had as a strategy to mimic human cognition. In the area of physical rehabilitation, studies include AI to process, estimate and classify the level of physical activity to improve professional-patient relationship. This work aimed at developing and comparing Sugeno (FLS) and Mamdani (FLM) Fuzzy Logic (FL) implementations. These implementations have as main objective to assist the physiotherapist in making a decision to allow the patient to return to daily activities safely and in the shortest possible time. The implemented systems are composed of fuzzy rules (if—then) and four inputs of a range of motion, extension and flexion; pain intensity; and muscle strength; to generate an output on the physical capability of the knee. The qualitative requirements of the systems took into account the processing time, precision, and reliability of the responses. Practical results suggest that Sugeno’s method is the one more reliable regarding the level of membership function. However, both systems agreed on the values reported in six hypothetical clinical cases and the resulting capability concepts. The experiment consisted of three physical therapists, who responded to the same six clinical cases and their responses were compared with the conceptual outputs of the FLM and FLS systems. The level of agreement was high in two of the six cases and low in the other four. It is believed that if a larger number of professionals has access to this innovative method of assessment in the rehabilitation area, the higher agreement level may be reached. With this pupose, several clinical tests can be added as training inputs in order to improve the proposed method.
Efficient Data Verification Systems for Privacy Networks

To overcome the problem with aggregated raw data, privacy preservation is the best answer. For privacy measures and other concerns, it delivers full throttle security for data. The essential reason for data security is that single transactions will not be permitted and recently utilised customers to communicate information securely. This study presents and compares various verification strategies based on the crypt arithmetic methodology for various set-valued data. It primarily checks for privacy risks in the sharing of details and information between the publisher, admin, and customers. There are various ways of preventing privacy violations, including the PPCDP technique for strong data that is non-trivial to implement. The authors used the Java Tomcat server, HTML, and JavaScript to develop a web application. We can automatically stop the person who is attempting to inject the vulnerability code using the technique, and all of this information is kept in the database.
Multi-machine Joint Video Tracking Mechanism and Its Application for Substation Safety Protection

With the improvement of my country's industrialization level and the development of network information technology, automation technology has deeply served various industrial fields. Therefore, this paper uses the five-frame difference method in the inter-frame difference method in combination with the actual operation requirements of the substation, which is more traditional. The standard three-frame difference can better eliminate the hole; then the moving target is tracked, and the mean-shift-based cam-shift image tracking technology is used. Finally, through MATLAB under the WINDOWS system, the program operation verification and analysis of the video data of the substation is carried out. The results show that the method in this paper can better meet the requirements of the video monitoring of the substation.
Performance Analysis and Improvement of Deep Q Network in Pommerman Agent Based on Compound Training Method

With the rapid development of the game industry, various intelligent agents are created and then applied in video games. Many of these kinds of agents are trained with reinforcement learning methods as well as deep-learning methods. However, under any circumstances, the agent-based on deep learning method requires a relatively high cost in computation resources. To this end, we propose a compound method with a relatively low cost to improve the training effect of pommerman agents. We separate the training into two stages. The first stage focuses on training the robot for survival with imitation learning methods. The second stage focuses on athletics by utilizing Deep Q-Network and reinforcement learning. Experiments show that the agent-based two-stage compound training method can win 4% more games than other agents in actual pommerman games. Meanwhile, judging from the 5-times increase in average game length before and after pre-training, the training process becomes more efficient and effective applying compound training methods.
Unsupervised PulseNet: Automated Pruning of Convolutional Neural Networks by K-Means Clustering

Convolutional Neural Networks (CNNs) achieve state-of-the-art results in many application areas, including image classification. For some applications it would be useful but impractical to deploy them on mobile devices with limited memory and power. A currently active area of research is the compression of deep networks while maintaining accuracy, with the aim of reducing memory usage, energy consumption and processing time. Several network compression methods have been proposed and have achieved good results, but they usually require the specification of parameters and are computationally expensive. We propose a new fast automated method called Unsupervised PulseNet that uses unsupervised k-means clustering to detect clusters of similar filters, and nodes in fully-connected layers, and prunes those that are redundant. We evaluate it on the CIFAR10, CIFAR100 and Tiny-Imagenet datasets using Alexnet, VGG16 and a 2-layer CNN called CifarNet suggested by the Tensorflow group. Compared to other methods in the literature we achieve the greatest compression, in shorter times, and with negligible loss in classification accuracy. In particular, we reduced Alexnet down to less than 0.7% of its original size, while not losing more than 2% classification accuracy.
The Observability in Unobservable Systems

In this paper, we introduce the concept of observability of selected state variables for systems that may not be fully observable. For their estimation, we introduce and exemplify a deep filter, which is a neural network specifically designed for the estimation of selected state variables without computing the trajectory of the entire system. The observability definition is quantitative rather than a yes or no answer so that one can compare the level of observability between different sensor locations.
Wall-climbing Robot Path Planning for Cylindrical Storage Tank Inspection Based on Modified A-star Algorithm

Wall-climbing robots have been widely developed in the application of storage tank inspection. One of the significant issues in robot tank inspection is how to determine an optimal path for the robot from an initial position to the destination. A-star algorithm is a first search algorithm commonly used in robotics path planning, whereas it may suffer from some drawbacks, for example, there are some redundant points could be removed from the generated path and some sharp turns need to be smoother for the robot to track with. As a result, a modified A-star algorithm is proposed in this paper to optimize the generated path. The simulation results show that the number of way points is decreased and the sharp turns in the path are removed. Therefore, this modified algorithm can provide a more efficient path planning with smoother curves and shorter operation time compared with normal method, without losing the optimization.
Vehicle as a diagnostic space: action recognition while driving a car

A person spends a significant portion of time driving a vehicle. This time serves several applications, such as unobtrusive health monitoring with sensors that are mounted inside the car. Such a car can perform regular medical checkups or other tasks such as drunk driver detection. For such tasks, driver behavior monitoring is essential. Several approaches utilize data from different modalities and sensors. Video-based recognition is used increasingly and usually combined with deep learning. In this work, we propose an end-to-end transfer learning approach using temporal pyramidal networks (TPN’s) on top of a ResNet-50 backbone that is pre-trained on the Kinetics400 dataset. We further perform a comparative analysis with the inflated 3D ConvNet network (I3D). We aim to boost training efficiency while improving accuracy as compared to previous work. The extracted videos from the DriveAct dataset have been captured from a single near-infrared (NIR) camera mounted on the rear-view mirror. Using these videos for training and evaluation, we achieve the best validation accuracy of 75.74%. This work has several potentials to be extended, generalizing to a multi-camera setup and combining multi-modal data to increase accuracy significantly. It further serves as a baseline for in-car health monitoring.
Search for Image Steganographic Policy With Adversary and Auxiliary Constrained Distance Measure

Steganography is to conceal the presence of secret communication.Steganalyser based on rich models and deep learning achieves state-of-the-art performance.However, due to the difficulty of capturing the unknown distribution of the high-dimensional cover, it is challenging to design a steganographic scheme from the view of traditional and deep learning-based steganography to defeat the steganalyser.In this paper, we propose a scheme to search the steganographic policy from scratch with the help of auxiliary constrained distance measure and the adversary.The auxiliary distance measure is provided via similarity evaluation and cover estimator.The response space of distance measures is constrained to provide better performance.Similarity evaluation establishes the distance measure in cover space.The cover estimator is to predict the potential distribution of the cover.Thus it could provide the distance measure in distribution space.The adversary model is to simulate the role of the steganalyser.The steganographic policy is searched from playing the adversarial game against an adversary with the relaxation of constrained response space of auxiliary distance measure.Ultimately, within the searched steganographic policy, the stego objects could be generated by the STC coder.Cover estimator, adversary, and steganographic policy are parameterized via neural networks.Experiments demonstrate that our scheme could achieve an effective modification policy and has competitive security performance compared with traditional and state-of-the-art deep learning-based steganographic methods.
A New Performance Indicator for Binary Classificationgraphical Abstract (Optional)

A new indicator is introduced to measure the performance in binary classification as a single numeric value.Most of the existing indicators target only the performance of the positive class only by calculating sensitivity, precision, and F1 score. Accuracy and related average measures target both classes but are tuned to be effective only for datasets with balanced classes. ROC, PR, and other charts presentations can also reveal a good overview but cannot the ability to address the classifier performance for both classes in one chart being dependent on the class prevalence in the evaluation dataset.The newly introduced performance indicator is successful in addressing the importance of both classes and is applicable for both datasets with balanced and imbalanced class ratio distribution.In addition, some of the most promising indicators reveal values in a range between -1 and 1, while the newly introduced one is normalized in the range between 0 and 1, where 1 indicates a perfect classifier and 0 is the worst (perverse).The new evaluation indicator can easily compare the performance of different classifiers and effectively distinguish the differences from other classifiers, including bad, random guessing, or perverse classifiers.It follows the expected performance trends and reveals the largest range of values compared to others.
A Study of Update Request Comments in Stack Overflow Answer Posts

Comments play an important role in updating Stack Overflow (SO) posts. They are used to point out a problem (e.g., obsolete answer and buggy code) in a SO answer or ask for more details about a proposed answer. We refer to this type of comment as update request comments (URCs), which may trigger an update to the answer post and thus improve its quality. In this study, we manually analyze a set of 384 sampled SO answer posts and their associated 1,221 comments to investigate the prevalence of URCs and how URCs are addressed. We find that around half of the analyzed comments are URCs. While 55.3% of URCs are addressed within 24 hours, 36.5% remain unaddressed after a year. Moreover, the comment votes could not differentiate URCs from non-URCs. Thus many URCs might not be aware by the users who can address the issue. As a first step to enhance the awareness of URCs, we investigate the feasibility of URC detection by proposing a set of features extracted from different aspects of SO comments and using them to build supervised classifiers that can automatically identify URCs with the accuracy of 89.3% and AUC of 0.95, on average.
A Deep learning based approach for Social Distance Monitoring

The COVID-19 pandemic has hit the world at large claiming large number of lives till date leaving us with no solution except maintaining social distancing or washing hands regularly, wearing masks and staying at homes. Social distancing is one of the key aspects to prevent spreading of this virus. It means more of maintaining suitable distance between each other. Artificial intelligence has been used widely for a large number of purposes and as such is one of the key tools used here for implementing this project. The proposed system identifies people who are not suitable distance apart by using object detection and calculating the Euclidian distance between two people. This system would be beneficial to the authorities for alerting people if the situation is serious.
Semi-Supervised Nuclei Detection in Histopathology Images via Location-Aware Adversarial Image Reconstruction

Nuclei detection is a fundamental task for numerous downstream analysis of histopathology images. Usually, it requires a large number of labeled images for fully supervised nuclei detection to achieve optimal performance. However, the process of collecting sufficient and high-quality ground truth labels is extremely labor intensive. To alleviate this problem, in this paper, a novel semi-supervised learning framework is proposed for nuclei detection, which optimizes the detection network with the involvement of unlabeled image reconstruction. Specifically, we reconstruct unlabeled images from their detection maps representing detailed information about individual location of candidate nucleus, which will aid in regularizing the training process of the detection network by encouraging spatial consistency between original and reconstructed images. Moreover, to further facilitate image reconstruction, we adopt an adversarial learning scheme using image and instance level discriminators for the classification of original and reconstructed images t. In this way, the capability of the detection network is successfully enhanced by taking advantage of both labeled and unlabeled images, thus leading to more accurate nuclei detection results. Extensive experiments show that we compare favorably with previous studies in various settings, which highlights the effectiveness of our proposed framework.
Design of Manifestation of Engineering Micro Lectures Based on Virtual Reality Technology

research-article Share on Design of Manifestation of Engineering Micro Lectures Based on Virtual Reality Technology Authors: Jie Zhang NCO Academy, Space Engineering University, China NCO Academy, Space Engineering University, ChinaView Profile , Jie Zhao NCO Academy, Space Engineering University, China NCO Academy, Space Engineering University, ChinaView Profile , Meng Li NCO Academy, Space Engineering University, China NCO Academy, Space Engineering University, ChinaView Profile Authors Info & Claims ICETM'21: 2021 4th International Conference on Education Technology ManagementDecember 2021 Pages 31–36https://doi.org/10.1145/3510309.3510315Online:14 March 2022Publication History 0citation5DownloadsMetricsTotal Citations0Total Downloads5Last 12 Months5Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Distributed Differentially Private Ranking Aggregation

Ranking aggregation is commonly adopted in cooperative decision-making to assist combining multiple rankings into a single representative. To protect the actual ranking of each individual, some privacy-preserving strategies, such as differential privacy, are often used. This, however, does not consider the scenario where the curator, who collects all rankings from individuals, is untrustworthy. This paper proposed a mechanism to solve the above issue using the distribute differential privacy framework. The proposed mechanism collects locally differential private rankings from individuals, then randomly permutes pairwise rankings using a shuffle model to further amplify the privacy protection. The final representative is produced by hierarchical rank aggregation. The mechanism was theoretically analysed and experimentally compared against existing methods, and demonstrated competitive results in both the output accuracy and privacy protection.
Estimation for a Feedback System With a Desired Final State and Intermittent Stochastic Inputs

The system considered in this article operates with a feedback that is characterized by a gain and a desired final state (DFS), which is the main parameter of interest in this article. The system is, however, subjected intermittently to stochastic inputs according to a Markov process. Since the system operates in two modes—under the feedback to the DFS and under a stochastic input—an interacting multiple model estimator is designed to handle the unknown DFS to be estimated (mode <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M_{1}$</tex-math></inline-formula> ) and the random inputs (mode <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M_{2}$</tex-math></inline-formula> ). Simulation results explore several scenarios and investigate the degree of observability of this stochastic problem
Research on intelligent home care system based on Internet of Things

Due to the increasing aging of the world population, the industries related to the elderly are also developing rapidly. In the face of the huge number of elderly population, how to provide the elderly with a sense of security and care has become a key concern of the country. With the support of the Internet of Things technology, this paper is designed to provide the elderly with a better quality of home care through the design of intelligent home care system and control system, while reducing the pressure of children to support the elderly and creating a healthier and happier life for the elderly. To create a more healthy and happy life for the elderly in their old age.
AINNS: All-Inclusive Neural Network Scheduling Via Accelerator Formalization

Driven by the rapid development of accelerators and diverse efficiency requirements of the naturally heterogeneous neural network computation, recent years have seen increased heterogeneity in neural network accelerator systems in terms of network structures, accelerator dataflows and implementations. However, existing research fails to schedule and map the heterogeneous neural networks on heterogeneous accelerators efficiently. They rely on clumpy exhaustive search or complicated ad hoc mapping approaches due to the semantic gap between the networks and accelerators. This paper proposes a systematic method to transform various accelerators into standard parameterized containers of the neural network loops, which builds a direct connection between the computation and the underlying hardware resources. This enables us to match the neural networks with accelerators based on their essential characteristics (e.g., reuse opportunities and bandwidth requirements) without diving into the detailed architectures. To this end, we propose AINNS, an all-inclusive neural network scheduler, that automatically schedules and maps the NN computation on heterogeneous accelerators with just one universal algorithm. Our experimental results show the proposed AINNS not only performs well in the traditional neural network acceleration but also improves the system throughput and energy efficiency by 1.8x and 1.7x respectively in the most challenging heterogeneous acceleration system.