Reviewerly/exHarmony huggingface dataset:
A Lightweight Mutual and Transitive Authentication Mechanism for Iot Network

IoT devices are typically authenticated directly by gateways present in the network. However, in a large and complex IoT systems like smart city or smart industry which consists of thousands of connected devices, it may not be always feasible to be directly connected to the gateway it may be connected to another device in the network. Therefore, already authenticated devices should facilitate the new device to get authenticated by the gateway. To address this issue, the existing protocols uses multiple authentication protocols based on different cryptography techniques, which are difficult to implement and manage in resource constrained IoT devices. In this paper, we propose a transitive device authentication protocol based on Chebyshev polynomial. The transitive authentication protocol utilizes the session key established in the mutual authentication between the intermediate device and gateway. Both the mutual authentication and transitive authentication protocols are relying on the same preregistration and authentication mechanism. To ensure the security of the proposed authentication protocol, a detailed security analysis is carried out, and the secure session of key establishment is verified using the BAN logic. Moreover, the proposed protocol is tested against crucial attacks in Scyther tool. These formal analysis and Scyther attack simulation show that the proposed protocol is capable of withstanding crucial attacks. Finally, to verify the efficiency, the protocol implementation is experimentally compared with other similar approaches. The results show that the proposed protocol offers better performance, providing significantly lower response time, handshake duration, memory utilization and energy consumption.
Exploiting Pseudo-Locality of Interchange Distance

The goal of this paper is to examine how to exploit pseudo-locality in order to transform tools derived for the Hamming distance in order to derive tools for the interchange distance, which is a pseudo-local metric. Intuitively, operators of pseudo-local string rearrangement distance measures have a bounded effect on the Hamming distance between the string prior to the operation and after it. Specifically, we demonstrate a way to exploit pseudo-locality of the interchange distance combined with additional techniques to derive:1. The first efficient approximate nearest-neighbour (ANN) data structure for the interchange distance, which is NP-hard to compute for general strings.2. The first linear time algorithm to compute approximate pattern matching with interchanges, which is a vast improvement from the O(nm) known algorithm.
Humanoid Robot Path Planning Using Memory-Based Gravity Search Algorithm and Enhanced Differential Evolution Approach in a Complex Environment

The present work focuses on an optimal path planning of single and multiple humanoid robots in a rugged terrain using a hybrid-based improved gravitational search algorithm (IGSA) tuned differentially perturbed velocity (DV) approach. The primary gravity search algorithm (GSA) suffers from the disadvantage of a lower convergence rate and getting trapped in local optimum conditions. The drawback is removed by using the hybrid IGSA-DV path planning approach, which improves the memory and velocity updating scheme. The algorithm is designed to minimize the overall path length of the humanoid, from source to goal, in the minimal time possible. The humanoids, during their locomotion, coordinate with each other to avoid collisions in their journey. The robots, primarily, make the decision based on the position of the various obstacles within the search space. The path smoothness is also considered to ensure stability and path optimization during the locomotion. The work further focussed on the energy efficiency of the different joints of the humanoid while walking on an even and uneven surface. The work is performed in real-world and simulation environments, and the results are then compared with the different existing, individual and hybrid techniques. The comparison of the above approach revealed that the IGSA-DV algorithm showed a better optimal outcome in terms of path length and time taken. Moreover, the deviation in simulation and experimental results was within the acceptable limits (less than 6%). Petri-Net approach was introduced along with the proposed technique to avoid confusion among the robots during path navigation.
Increasing Consumer Drone Safety Through an IoT-Based Custom Illumination System

Consumer-based drone applications have been increasing rapidly in recent years. Most of these applications, such as aerial surveillance, crop dusting, and real estate photography rely, on frameworks that are based on the Internet of Things (IoT). Although solutions have been proposed to security issues due to connectivity, physical safety issues have not been sufficiently discussed. To solve one such issue, the conspicuousness of drones during flight, we have proposed an illumination system that can adapt to different weather conditions using an IoT framework. To test the feasibility of our illumination system, we conducted two tests, a power consumption test, and a human-in-the-loop assessment. The power test showed different illumination configurations did affect power consumption. The human-in-the-loop assessment demonstrated the effect of illumination on distance perception and how sunlight levels affected the drone's preferred illumination color. As drones begin to operate in more crowded spaces, such as cities, the need to ensure the safety of the public will be paramount. Through this prototype, we aim to provide designers with guidance on how to consider illumination for safer drone operations. Future studies, we plan to explore using illumination to increase safety and congruence in future application areas, such as delivery.
9. Journalism by the dashboard light

Audience metrics have penetrated the newsroom. Web analytics can track real-time data on any digital news item and get information about audience appreciation and engagement. Audience metrics, in particular, are considered as a means to enhance journalistic quality as journalists can better respond to user desires. Although a greater focus on audience-centred journalism is a commendable development, it is questionable whether the metrics-driven focus is helpful in this respect. Audience desires cannot be captured in metrics alone. To prevent newsrooms from being blinded by the dashboard lights, we argue that engineers and ux-designers must build friction in order to stimulate responsible interactions.
Recommendation Algorithm using Weighted User Behavior Score-Based Similarity

One of the most widely used recommendation system approaches is neighborhood-based collaborative filtering. This approach uses the power of similarity between users to generate recommendations. Recently, the developed similarity model considers not only explicit rating scores but also implicit rating scores. The problem of this similarity model is to use similarity weighting based on the threshold value. The error rate for rating predictions is still needed to improve for a better recommendation. This study aims to develop a similarity model that considers similarity weighting by paying attention to the number of items rated by users to increase the recommendation performance. The proposed similarity model is called Weighted user Behavior score-based Similarity (WeBSim). Our experiment used the MovieLens 100k dataset to test the model performance. The results showed that the proposed similarity model outperforms the previous similarity (UPCF) by reducing the MAE and RMSE values by 0.0148 and 0.0123.
Virtual Simulation Learning Based on AR with Sawing Machine Design to Improve and Evaluate the Special Skill for Vocational Education in the COVID-19 Pandemic Era

This study aims to: (1) At the age of the COVID-19 pandemic, construct a virtual simulation learning innovation based on augmented reality in the form of a sawing machine to enhance the special skills of vocational students; (2) testing the feasibility of learning innovation based on virtual simulation based on documented reality in the form of a sawing machine; and (3) testing the effectiveness of virtual simulation learning innovation based on augmented reality in the form of a sawing machine. This research uses research and development. The research stages include product design, product testing, learning media expert testing, and effectiveness testing. The effectiveness test was carried out on vocational students at universities in Malang City. Among the key findings of this research are the following: (1) a virtual simulation learning innovation based on augmented reality in the form of a sawing machine developed that is feasible and attractive (percentage above 80<sup>&#x0025;</sup>); (2) in the age of the COVID-19 pandemic, a virtual simulation learning innovation based on augmented reality in the form of a sawing machine was developed and demonstrated to be effective in enhancing the special abilities of vocational students.; and (3) The study&#x0027;s digital learning media innovation products can be applied to the design of learning media in the COVID-19 pandemic era
Development of an intelligent recognition system for dynamic mid-air gesticulation of isolated alphanumeric keys

• Introducing detection + tracking approach for smaller gesture object (red marker) • Kalman filter is used to estimate the object positions during blurring/illumination. • Re-engineered self co-articulation removal model to work on alphanumeric keys. • Incorporated FIS to separate the gesticulated alphanumeric keys for the classifiers. • An analysis on different pre-trained networks on gesticulated character recognition. Detecting and recognizing naturally gesticulated characters in mid-air is difficult because of the complexity arising due to (i) gesticulation style; (ii) gesture’s pattern and size. This worsens further when the gesticulation speed is faster causing the gesture object to be blurred in some of the frames. To ensure that the trajectory formed is equivalent to the intended gesticulation and be robust to the impostor’s presence, we propose red marker-detection and tracking (RM-DT) approach. This uses channel subtraction, thresholding, compact (hand) criteria, and Kalman filter to detect and track the gesture object to form the gesture trajectory (accuracy of ∼96%; mAP – 0.9419). Unlike handwritten characters, the gesticulated trajectory incorporates the self co-articulated strokes that increases the rate of misclassification at the recognition stage. Hence, we separate the gestures into three groups and apply group-specific pre-processing models to remove them (accuracy of ∼95%). These trajectories were then clustered into two groups (based on the number of stroke segments and segments removed) using fuzzy inference system so to use two pre-trained InceptionV3 for recognition. An average accuracy of 96.81% (precision - 0.9597, recall - 0.9484, Fscore - 0.9550) and 98.95% (precision - 0.9767, recall - 0.9631, Fscore - 0.9666) across the two groups respectively. The overall accuracy of the proposed recognition model has a relative improvement of 9% over the existing state-of-the-art model for dynamic character gesture recognition.
Encrypted Packet Inspection Based on Oblivious Transfer

Deep packet inspection (DPI) is widely used in detecting abnormal traffic and suspicious activities in networks. With the growing popularity of secure hypertext transfer protocol (HyperText Transfer Protocol over Secure Socket Layer, HTTPS), inspecting the encrypted traffic is necessary. The traditional decryption-and-then-encryption method has the drawback of privacy leaking. Decrypting encrypted packets for inspection violates the confidentiality goal of HTTPS. Now, people are faced with a dilemma: choosing between the middlebox’s ability to perform detection functions and protecting the privacy of their communications. We propose OTEPI, a system that simultaneously provides both of those properties. The approach of OTEPI is to perform the deep packet inspection directly on the encrypted traffic. Unlike machine and deep learning methods that can only classify traffic, OTEPI is able to accurately identify which detection rule was matched by the encrypted packet. It can facilitate network managers to manage their networks at a finer granularity. OTEPI achieves the function through a new protocol and new encryption schemes. Compared with previous works, our approach achieves rule encryption with oblivious transfer (OT), which allows our work to achieve a better balance between communication traffic consumption and computational resource consumption. And our design of Oblivious Transfer and the use of Natural Language Processing tools make OTEPI outstanding in terms of computational consumption.
UAV Object Detection Model Based on Improved Ghost Module

Abstract UAVs with computer vision functions have been widely used in various fields. However, due to the limited memory and computing power of embedded devices, the real-time scene analysis of detection algorithm running on UAV platform is still a challenge. To face the challenge, in this paper, we based on the YOLOV3 detection model by changing the convolution method and using fewer parameters to generate more feature maps. We evaluate our detector on the VisDrone2020 dataset. Compared with the original YOLOV3, we achieved impressive results, including a reduction of 76.38% of the parameter size, 77.95% of floating-point operations, and a considerable accuracy rate and faster inference speed.
Super-resolution image reconstruction network based on dual-channel pixel attention mechanism

Abstract In the field of image processing, image super-resolution reconstruction technology based on deep learning is becoming more and more mature. Relying on stacking convolutional layers to expand the depth of the reconstruction network can improve the similarity of the reconstructed image, but as the depth of the convolutional layer continues increase, the learning rate of the reconstruction network will also become lower. In this paper, the dual-channel pixel attention mechanism PA combined with the ESPCN network model can not only retains the high reconstruction rate of ESPCN, but also improves the image reconstruction effect. The experimental results show that the network model proposed in this paper is better than SRCNN, FSRCNN, ESPCN for single image reconstruction, and the peak signal-to-noise ratio (PSNR) and structural similarity ratio (SSIM) are significantly improved.
Skeleton Action Recognition Based on Double Residual Convolutional Neural Network

Abstract Recently, methods for action recognition using bone point information have gained much attention and research. However, considering the problem, this paper presents a model of skeletal action recognition based on a dual-residual convolutional neural network. The 3D bone data was used as network input to replace the original network structure with ResNet18 network. With action coherence involved, long and short memory network (LSTM) is used to obtain time features for skeletal movement information flow, further improve the fusion strategy, add static attention mechanism to obtain the most important features, and further extract features. With this model, we achieved 85.3% accuracy on the X-Sub of the NTU RGB+D data set, with higher accuracy compared to the original and other networks.
Research on Sensitive Image Detection Service Based on Deep Learning Framework

In recent years, image detection services based on cloud computing deep learning have emerged at the historic moment, but due to the influence of network instability, bandwidth restrictions and many other factors, there may be a large response delay, which will seriously affect the user experience. How to allocate large-scale services to limited nodes, increase user satisfaction, and achieve the load balance, this has become a difficult problem to be solved at present. In this paper, the simulation environment is configured based on cloudsim, and the simulation experiments of standard particle swarm optimization algorithm and improved algorithm are carried out to simulate the scheduling strategy of sensitive image detection service suitable for the deep learning framework of this subject.
Intelligent Audit Question Answering System based on Knowledge Graph and Semantic Similarity

Audit work needs to refer to a large number of law and institutional documents, and the business process is intricate. Finding the answer to audit question based on the knowledge graph can improve the work efficiency observably. This paper has proposed a knowledge base for the electric power audit task. In detail, this project collects law texts related to auditing, and has designed schema and specific rules for them to extract triples. These triples are stored in the Neo4j graph database as Knowledge Graph. In addition, this paper makes templates to generate a question answering dataset about electric power audits. According to the audit knowledge graph, a character-level convolutional neural network is trained and an intelligent audit question answering system is built based on the semantic similarity. The effectiveness of the system is evaluated by experiments.
Distribution Network Anomaly Detection Algorithm Based on VAE

With the booming development of economy and technology, China&#x0027;s electric industry has gradually realized intelligent, has developed into a comprehensive network including computer network, power network, information network. The current power information monitoring technology is mainly aimed at the power generation, transmission and transformation stage, and the lack of effective power information detection means in the power distribution stage. Therefore, a distribution network anomaly detection algorithm based on variational auto-encoder is proposed to solve the problem of anomaly detection of distribution terminal data. The input time series power load data is compressed and reconstructed, and the anomaly degree of samples is detected by reconstruction error. Experimental results show that the proposed algorithm has high detection rate and accuracy, as well as high robustness.
Blind visual quality assessment of light field images based on distortion maps

Light Field (LF) cameras capture spatial and angular information of a scene, generating a high-dimensional data that brings several challenges to compression, transmission, and reconstruction algorithms. One research area that has been attracting a lot of attention is the design of Light Field images quality assessment (LF-IQA) methods. In this paper, we propose a No-Reference (NR) LF-IQA method that is based on reference-free distortion maps. With this goal, we first generate a synthetically distorted dataset of 2D images. Then, we compute SSIM distortion maps of these images and use these maps as ground error maps. We train a GAN architecture using these SSIM distortion maps as quality labels. This trained model is used to generate reference-free distortion maps of sub-aperture images of LF contents. Finally, the quality prediction is obtained performing the following steps: 1 ) perform a non-linear dimensionality reduction with a isometric mapping of the generated distortion maps to obtain the LFI feature vectors and 2 ) perform a regression using a Random Forest Regressor (RFR) algorithm to obtain the LF quality estimates. Results show that the proposed method is robust and accurate, outperforming several state-of-the-art LF-IQA methods.
Rotational Differential-Linear Attack on Chaskey

Rotational/Rotational-XOR differential-linear attack is a kind of powerful attack on ARX primitives. In this paper, we analyse the message authentication code algorithm—Chaskey—by using rotational differential-linear attack and using partitioning technique for key recovery. We presentthe results of distinguishing attack under weak-key and related-key settingswith correlation <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mo>−</mo> <mn>55.636</mn> </mrow> </msup> </math> for 9-round Chaskey. This is for the first time rotational differential-linear distinguisher has been used to analyse Chaskey. It is concluded that Chaskey can resist against rotational differential-linear attack.
An Improved Data-Driven Decision Feedback Receiver via Deep Unfolding

The proposed decision feedback receiver (DFR) is an end-to-end data-driven iterative receiver, and the performance gain is achieved from iterations. However, the mismatch problem between the training set and test set exists in the DFR training, and thus performance degradation, slow convergence speed, and oscillation are introduced. On the other hand, deep unfolding using parameter sharing is a practical method to reduce the model parameter number and improve the training efficiency, but the problem whether the parameter sharing will cause performance degradation is rarely considered. In this work, we generally discuss and analyze these two problems, and solution to solve the problem or conditions that the problem no longer exists is then introduced. We give the improvements to address the mismatch problem in the DFR, and thus we propose an improved-DFR via deep unfolding. The improved-DFRs with and without parameter sharing, namely, DFR-I and DFR-IS, are both developed with low computation complexity and model complexity and can be executed by parallel processing. Besides, the practical training tricks and performance analysis including computation complexity and model complexity are given. In the experiments, the improved-DFRs outperform the DFR in various scenarios, in terms of convergence speed and symbol error rate. The simulation results also show that the DFR-IS is easier to train, and the slight performance loss can be reduced by increasing model complexity, in comparison to DFR-I.
Lateral shearing interferometry with continuously variable shear ratio

Lateral shearing interferometry with different shear ratio has different measurement sensitivity, which corresponds to different measurement accuracy. To image the wavefront of an unknown system, high precision measurement accuracy is particularly important. Here a quadriwave lateral shearing interferometry (QWLSI) with continuous adjustable shear ratio is proposed, in which the light splitting grating is placed in the front of the first condenser lens rather than in late position for the traditional QWLSI. In this case, the shear ratio is proportional to the ratio of the defocusing amount to the focal length of the imaging lens. The newly developed QWLSI with continuously variable shear ratio, not only can measure the system aberration, but also calibrate the spatial resolution of the measured wavefront. Software GLAD simulation and experimental results on phase imaging show that the proposed scheme can not only realize variable-shear-ratio interferogram but also has a good robustness, which is well suitable for quantitative phase imaging.
A method for dynamic target simulation of non-coplanar multimode radar

During the hardware-in-the-loop simulation of multimode compound guidance, it is common for the receiving antenna center to deviate from the rotation center of the turntable. In this regard, this paper proposes a method for dynamic target simulation of non-coplanar multimode radar. The corrected position of the target simulation at any receiving antenna center position and any target theoretical position is derived through the spatial geometric relationship. Aiming at the situation where the target position after correction is outside the original triad of antenna array, the target localization vector control method of the triad of antenna array at any observation position is proposed. Using this method as a criterion for pros and cons, the particle swarm optimization algorithm can be used to simulate targets at any location. The error between the expected angles and the corrected angles is within 0.02° according to the digital simulation. This accuracy can satisfy the dynamic target simulation requirement in the hardware-in-the-loop simulation.