Reviewerly/exHarmony huggingface dataset:
Anatomizing Hybrid Biometric Modality to Identify Tissue Loss and Pitted Keratolysis using BOPVIC Algorithm in Humans

Information blend implies the trade-off of confirmation presented by various sources of information to make a decision. With respect to biometrics, evidence compromise expects an essential part in updating the affirmation precision of human confirmation systems and is insinuated as multibiometric. Multibiometric systems join the information presented by various biometric sensors, estimations, tests, units, or characteristics. Besides overhauling planning with execution, these systems are depended upon to additionally foster people consideration, predict spoofing, and give variation to non-basic inability to biometric applications. Thinning is the important step in preprocessing phase of fingerprint and iris recognition System. This paper dissects the importance to analyze the pixel variations and the nerve abnormalities observed in the fused modality using bag of pixel volatility image classification (BOPVIC)algorithm. The thinning algorithms such as Zhang- Suen’s and Guo-Hall’s are incorporated in order to analyze the correlative loss of tissues, along with conceivably identifying the lap in the compression/ repression of eye ridges that could be a consequent to pitted keratolysis.
Express Service Composition Optimization Based on Improved Ant Colony Algorithm

Express delivery is a complex service activity of cross regional and multi links, it could be subdivided into four major links, including express picking-up, processing, transportation, and delivery, while each subdivision service often has a variety of alternative candidate service modes. It is difficult to choose the optimizing service composition scheme in courier service pool and meet customers’ individualized and diversified needs. To address this issue, this paper constructs an express service portfolio model based on QoS (Quality of service). Moreover, aiming at the characteristics of the traditional ant colony algorithm with slow convergence speed and falling into local optimality, this paper improves the algorithm by improving the pheromone update mechanism, adding the roulette selection mechanism, and adding the single-loop optimal path table. Finally, a case study is simulated to verify the improved ant colony algorithm by MATLAB platform. The results show that compared with the traditional algorithm, the improved ant colony algorithm is accurate in solving the model, and has faster convergence speed, more stable output results, and better performance. The significant contributions of this paper are flexibly establishing an optimized package of express delivery services and designing its solution algorithm, and will help express enterprises onto the right choices to increase the customers’ satisfaction.
Efficiency improvement techniques for private intersection-sum protocol using Bloom filter

Abstract A private set intersection protocol is one of the secure multi-party computation protocols, and allows participants to compute the intersection of their sets without revealing them to each other. Ion et al. proposed the private intersection-sum protocol (PI-Sum). The PI-Sum is one of the two-party private set intersection protocol. In the PI-Sum, two parties (say Alice and Bob) have the private sets A and B . Moreover, Bob additionaly has a rational integer associated with each element of B . The PI-Sum allows Bob to obtain the sum of the rational integers associated with the elements of $$A \cap B$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:mo>∩</mml:mo> <mml:mi>B</mml:mi> </mml:mrow> </mml:math> . This paper proposes the efficiency improvement techniques for the PI-Sum. The proposed techniques are based on Bloom filters which are probabilistic data structures. More precisely, this paper proposes three protocols which are modifications of the PI-Sum. The proposed protocols are more efficient than the PI-Sum.
MPC Based Path Planning for Wheeled Mobile Robots in Environments with Varying Slip

The paper aims to address the challenges involved in the control design of Wheeled Mobile Robots (WMR) under the conditions of slip. The work is done as part of investigating the possibilities of using advancements in Machine Learning techniques in WMR with the integration of modern control strategies. The research presents a dynamic model of the robot including four slip parameters and wheel torques as input. The navigation in different environments can be affected by these slip variables resulting from disturbances including terrain dynamics. A model predictive control (MPC) is used to manipulate the torque variables according to the path and variation in both longitudinal and lateral slip values. By proposing the torque control, the work can be extended to Ground Robots with different payloads. The model can follow the reference path with linear and angular velocity constraints. It is also identified that the proposed MPC can adapt to the slip variations which are observed from the change in linear velocities and accelerations under different test values of slip variables. The slip parameters can be identified in real-time with the help of machine learning techniques and can provide a reference to the MPC model proposed. In the future, Artificial Intelligence (AI) can be integrated with control system design to address the multi-terrain navigation challenges.
How Much Do Synthetic Datasets Matter in Handwritten Text Recognition?

This paper explores synthetic image generators in dataset preparation to train models that allow human handwritten character recognition. We examined the most popular deep neural network architectures and presented a method based on autoencoder architecture and a schematic character generator. As a comparative model, we used a classifier trained on the whole NIST set of handwritten letters from the Latin alphabet. Our experiments showed that the 80% synthetic images in the training dataset achieved very high model accuracy, almost the same level as the 100% handwritten images in the training dataset. Our results prove that we can reduce the costs of creating, gathering, and describing human handwritten datasets five times over – with only a 5% loss in accuracy. Our method appears to be beneficial for a part of the training process and avoids unnecessary manual annotation work.
State of the Art - Smart Doorbell Systems

In this paper Smart Doorbell systems are analyzed to break up modern doorbells into generations, of which the Amazon Ring series shines for those who are open to Amazon infrastructure, and the Eufy Video Doorbell remains viable for those who prefer their data to be more local.The paper also presents a new proposed Smart Doorbell system. The new proposed system allows a video classification network to run on edge devices to alert the homeowner of any significant events happening in front of their home without any other input, such as the button being pressed. The key aspect of the new proposed system is the creation of a doorbell dataset that can provide researchers with a set of videos that is large enough to properly train a network that is ready to be implemented on a user’s doorstep. The paper also identifies advances that could be realized with a proper dataset available to train a video CNN, such as real time event notifications and automatic video sorting.
A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.
Arabic Part Of Speech (POS) Tagging Analysis using HMM Trigram method on Al-Qur’an Ayah Sentences

Part Of Speech (POS) tagging is part of Natural Language Processing to determine correctly the label in a sentence from the given input. Different POS tagging techniques in some literatures have been developed for English text, and few for Arabic texts. This problem uses a method based on the second hidden Markov model, which is looking for to two words to the past or better known as the HMM Trigram method. The main problems of POS tagging are Out Of Vocabulary (OOV) and word ambiguity. This study discusses POS tagging using HMM Trigram method on Al-Qur'an text data. The dataset is divided into three categories of data derived from quran corpus consists of 150 simple perfect sentences, 50 sentences with more than one S/P/O/K and 50 selected verses of the Qur'an. The data experiment was carried out using a cross-validation technique, namely k-fold cross validation. The data is classified into two, namely training data and test data. Training data is used to find emissions and transitions probabilities, while data testing uses the Viterbi algorithm. The experimental results achieved an average accuracy of 86% for simple datasets, 60% for medium datasets, and 38% for the complete paragraph datasets.
An Answer Set Programming Based Framework for High-Utility Pattern Mining Extended with Facets and Advanced Utility Functions

In the context of pattern mining, the utility of a pattern can be described as a preference ordering over a choice set; it can be actually assessed from very different perspectives and at different abstraction levels. However, while the topic of High-Utility Pattern Mining (HUPM) has been widely studied, the basic assumption is that each item in a knowledge base is associated with one, static utility. In this paper we introduce, among others, the notion of facets for items, which allows to cope with this limitation and, moreover, we show how a more structured representation of available information, coupled with facets defined also for higher abstraction levels, paves the way to new opportunities for HUPM. In particular, the proposed framework allows to introduce some new advanced classes of utility functions in the detection process, whose relevance is also experimentally evaluated. A real use case on paper reviews is exploited to analyze the potentiality of the proposed framework in knowledge creation and discovery. Given the wide variety of analytical scenarios that can be envisioned in this new setting, we take full advantage of the capabilities of Answer Set Programming and its extensions for a fast encoding and testing of the framework.
NTU-DensePose: A New Benchmark for Dense Pose Action Recognition

Skeleton-based action recognition has recently gained a lot of attention in computer vision. The previous skeleton-based datasets used sparse poses to represent the human body, which always leads to a large loss of human body detail information. Therefore, the previous skeleton-based methods generally performed worse than the image-based methods. In this paper, we propose a dense-pose-based action recognition dataset NTU-DensePose. This dataset automatically annotates 37,060 video samples with two dense poses, IUV equidistant annotation and IUV equivalent annotation. Each dense pose annotation contains more than 240 keypoints per instance. So the dense-pose-based action recognition method can capture more subtle details and predict human action more accurately than the previous skeleton-based methods. To the best of our knowledge, NTU-DensePose is the first dense-pose-based action recognition dataset.
A New Air and Ground Target Classification Method Based on Doppler Information

When an airborne early warning radar is detecting air targets, ground targets, especially freeway targets often can be detected and accordingly become interference plots and tracks. Hence, air and ground target classification must be performed to enhance track quality of air targets. In this paper, a new air and ground target classification method is proposed based on Doppler information of targets. First, the Doppler features of freeway target echo are analyzed on the condition of multi-channel clutter and space-time adaptive processing (STAP) based on traffic flow simulation. Then, an air and ground target classification rule is proposed according to the Doppler distribution difference in frequency domain. The simulation results show that both higher recognition probability of air targets and that of air target can be acquired simultaneously in the classification rule.
Research on Tag Method for Joint Extraction of Domain-oriented Entity and Relation

Corpus tagging is an important basic work of named entity recognition and relation extraction based on deep learning, and it is also a core task in natural language processing. Aiming at the problems of error propagation, information loss and entity redundancy in the pipeline method, this paper proposes a text annotation method "E+R+BIES" for joint extraction of domain entity relations based on the characteristics of specific domain corpus. The entity relationship extraction problem is transformed into a sequence labeling problem. The entity and its relationship information are included in a labeling process, the triples are directly modeled, and the BERT-BiLSTM+CRF end-to-end model is used for label prediction. Experiments show that the joint extraction effect of domain entity relations based on this annotation method and the BERT-BiLSTM+CRF model is better than the general frontier model, in which the accuracy rate is increased by 5 to 7 percentage points, the recall rate is increased by 9 to 11 percentage points, and the F1 value is increased by 8 ~9 percentage points, reaching 90.60%.
Fast ECDH Key Exchange Using Twisted Edwards Curves with an Efficiently Computable Endomorphism

It is widely accepted that public-key cryptosystems play a major role in the security arena of the Internet of Things (IoT), but they need to be implemented efficiently to not deplete the scarce resources of battery-operated devices such as wireless sensor nodes. This paper describes a highly-optimized software implementation of scalar multiplication for Elliptic Curve Diffie-Hellman (ECDH) key exchange on resource-limited IoT devices that achieves fast execution times along with reasonably small code size and RAM consumption. Our software uses a special class of elliptic curves, namely twisted Edwards curves with an efficiently computable endomorphism similar to that of the socalled Gallant-Lambert-Vanstone (GLV) curves. This allows us to combine the main advantage of the GLV model, which is an efficiently-computable endomorphism to speed up variable-base scalar multiplication, with the fast and complete addition rules of the (twisted) Edwards model. We implemented variable-base scalar multiplication for static ECDH on two such curves, one over a 159-bit and the second over a 207-bit pseudo-Mersenne prime field, respectively, and evaluated their execution time on a 16-bit MSP430F1611 processor. The arithmetic operations in the prime field do not contain operand-dependent conditional statements (in particular no “if-then-else” clauses) and also the scalar multiplication follows a fixed execution path for a given (static) scalar. A variable-base scalar multiplication on curves over the 159 and 207-bit field takes about 2.63 and 4.84 million clock cycles, respectively, on an MSP430F1611 processor. These results compare favorably with the Montgomery ladder on the equivalent Montgomery curves, which is almost 50% slower.
ITGNN

In e-commerce scenario, the goal of a session-based recommendation system(SBRS) is to predict next item clicks of users, while the session is anonymous in the process. Graph Neural Network(GNN) has aroused widespread interest in the SBRS system. However, there are two important drawbacks of applying GNN for this task. First, classic GNN approaches rarely consider the time interval between the user clicking on two items as in real-world environment. This time interval is very important for accurately recommending the next user's interest. Secondly, for inter-session information, most GNN-based models consider n-hop neighbor nodes of a single node, which introduces more noise in the learn global information phase.In this paper, we propose a Item Transition Attentive Graph Neural Network model(ITGNN). This model learns how time interval and global frequency of item transition affect a local session. Extensive experiments conducted on two E-commerce datasets demonstrate the model to be more accurate in recommendation task.
Motor teaching based on inducing human posture changes

It is considered that getting a sense of motor posture leads more efficient learning. In this study, we propose a motor teaching method that changes the trainee’s posture to improve motor performance. The desired posture is calculated based on the human joint torque estimated by measured force and torque acting on the environments during motion. The effectiveness of this teaching method is investigated by applying it to pedaling of the bicycle. By simulations, it is confirmed that applying this method to the pedaling force of beginner cyclists improves motor performance. Additionally, we conduct preliminary experiments, in which we deal with arm motions analogous to pedaling of pedaling of the bicycle. By experiments, it is confirmed that the proposed method increases the performance efficiency of human motion.
Robust Multiview 3D Pose Estimation Using Time of Flight Cameras

In this work, we introduce a robust multi-view 3D pose estimation method which leverages infra-red intensity image and the depth information from the ToF camera. We use the intensity image as an input to a CNN based 2D key point detector from multiple view ports and forward project them to calculate the 3D joint position. If a keypoint is missing in one of the views we benefit from the depth information from the ToF camera. This results in a robust system which is robust against material reflectivity and occlusion. Our experiments show that the system is able to deliver 3D joint positions with a maximum positional error of only 4 cm independent of the material reflectivity. We also compare our technique with different 3D key point estimation techniques. We compare our system to a state-of-the-art CNN based multi-view depth based pose estimation technique to lift 2D key points from a single image to 3D pose. The maximum euclidean distance from the ground truth and its deviation for each system are evaluated against VICON, a marker based motion capture system.
The Design of Modern English and American Literature Appreciation System Based on Computer Network

With the advent of the information age, many new science and technology have been created. Computer network has been chased by people because of its express information, high accuracy of information and no longer the expression of information. In the teaching of British and American literature, the teaching mode based on computer network can change the mode teaching mode that people can only rely on the text, so that the teaching can develop to multi-mode. Students in multimodal teaching can also change the single way to acquire knowledge and then can learn knowledge, accumulate knowledge, use knowledge and update knowledge from a variety of methods. At the same time, this paper also discusses that the multimodal English and American literature teaching based on computer network can lead to the loss of cultural loss.
Energy Efficient Dynamic Task Offloading for Blockchain-enabled Virtual Wireless Networks

The application of the blockchain technology in various fields has continued to grow exponentially. Besides, task offloading schemes are essential in the lessening of the quantity of consumed energy. These schemes are also needed for workload rate of systems during the transfer of tasks to the execution phase in the cloud or edge. Based on the need for a dynamic task offloading and allocation of resources for virtual wireless networks (VWNs), we have proposed an energy efficient dynamic task offloading scheme in VWNs that leverages the blockchain technology. The proposed approach chooses the online optimal computing place either on the mobile cloud computing (MCC) server or the mobile edge computing (MEC) server with the goal of reducing task response time and energy consumption. Communication and computation costs incurred by different kinds of applications are controlled by the Lyapunov optimization technique. Computing location for each task is chosen adaptively during the optimization without requiring extensive system information. Energy efficient approach achieves better offloading decisions with lower computational complexity as compared with traditional offloading techniques.
A Semantic Network Analysis of Research Trends on Media Education of Elementary School Students

In the era of the Fourth Industrial Revolution, one of the core competencies required of students is media literacy. In particular, elementary school students’ media education is important because elementary school students have high adaptability and capacity. In this regard, this study aims to examine the discourse about media education in Korean society for effective media education in the era of smart media. The purpose of this study is to find out the research trend of media education for elementary school students. For this study, online big data collected over the past 5 years has been used on Internet portal sites. Keywords used in this study are ‘elementary School Students media education’ and ‘elementary School Students media literacy.’ In the last 5 years, 2755 KB of data was collected through the data mining process from the big data retrieved. In this study, a semantic network analysis method was used. Based on these research results, elementary school students are using a lot of smartphones or other digital devices, and it seems that there are many media education approaches, especially for English education and coding education. It is the time that more effort is needed for better education utilizing media in media friendly environment of elementary school.