Reviewerly/exHarmony huggingface dataset:
Increasing Predictive Accuracy of Neuro-Fuzzy Using Quartiles to Initialize the Membership Function

Neuro-Fuzzy is one of the techniques commonly used in classification as it has been proven satisfactory in predicting results. The neuro-fuzzy system is divided into 3 layers: the first layer is the fuzzification process, the special hidden layer is bipolar transform, and the output layer is the learning process. This research proposes the alternatives to the initialization of membership function (MF) for the Neuro-fuzzy system that maintains the properties of direct calculation and rule-based for classification. In the fuzzification phase, define membership functions in accordance with the characteristics of the data using five summarized values such as min, 1st quartile (Q1), median, 3rd quartile (Q3) and max to determine the initial position of MF to convert input data to fuzzy sets or linguistics variable. The Gaussian is used as MF and transforms members into bipolar in a special hidden layer to be sent to the final part of the artificial neural network. The 12 data sets with various classes and features are used in the experiment and give satisfactory results.
Lossless Image Compression Algorithm Based on Long Short-term Memory Neural Network

People have relatively higher requirements for image storage in some specific fields, such as high-resolution cultural relic data image, medical image, infrared remote sensing image, high-precision astronomical observation image. There cannot be any pixel loss in the storage process, so the image can only be compressed by lossless compression. In this paper, a lossless image compression algorithm based on the neural network of long short-term memory (LSTM) is proposed: a LSTM model predictor based on attention mechanism is built by utilizing the memory characteristic of cyclic neural network. The previous pixel value of the image was taken as the input of the model, then the predicted pixel was obtained through the cyclic neural network, and finally the calculated difference between these values was encoded by the mixed run-length encoding and Golomb-Rice encoding. Compared with the traditional predictive lossless image compression algorithm, this algorithm proposed here comprehensively considers the correlation between more pixels and encoded pixels. The experimental results show that compared with the lossless image compression algorithms JPEG-LS and CALIC, the proposed algorithm improves the compression rate by 25% and 12% respectively.
Parallax-Based Color Correction in Image Stitching

As an effective image preprocessing method, color correction is widely used in the field of image stitching. However, due to the changeable visual environment, parallax often occurs in mosaic images. So far, there are few algorithms dealing with color correction under parallax situation. And most color correction methods produce poor results in images with parallax. This paper introduces a color correction algorithm which can also be applied in the case of parallax in image mosaic. We use VFC algorithm, color hue information and iterative calculation to obtain better color correction results. Compared with existing algorithms, it has the wider range of applications. The experiments show that our method has faster time efficiency and better results.
Voice-Activity and Overlapped Speech Detection Using x-Vectors

The x-vectors are features extracted from speech signals using pretrained deep neural networks, such that they discriminate well among different speakers. Their main application lies in speaker identification and verification. This manuscript studies, which other properties are encoded in x-vectors. The focus lies on distinguishing between speech signals/noise and utterances of a single speaker versus overlapped-speech. We attempt to show that the x-vector network is capable to extract multi-purpose features, which can be used by several simple back-end classifiers. This means a common feature extracting front-end for the tasks of voice-activity/overlapped speech detection and speaker identification. Compared to the alternative strategy, that is training of independent classifiers including feature extracting layers for each of the tasks, the common front-end saves computational time during both training and test phase.
Learning-Driven Cloud Resource Provision Policy for Content Providers With Competitor

The cloud resource provision policy of a content provider in the presence of competitors on globally distributed cloud platforms plays a significant role in maximizing its profit. However, developing an optimal resource provision policy is quite challenging, due to the difficulty to capture the competition relationship between two competitive CPs and to obtain the budget of the competitors which is usually kept private. To solve this problem, in this article, we propose a learning-driven cloud resource provision policy for a CP with competitors. We formulate the competition between the CPs as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lottery Colonel Blotto</i> game in which the payoff of each region is positively related to the resource advantage achieved by the CP, formulate the budget allocation problem as a Markov decision process, and obtain the sub-optimal resource provision policy by reinforcement learning and deep reinforcement learning-based algorithms. We also prove the convergence of the sub-optimal solution. Finally, we validate our proposed method using real-world CPs statistics. The results show that the budget information is critical for a CP to make policy decisions, and it is better for CPs with smaller budget to focus their budget resources in regions with higher values.
Proof of Stake for Blockchain Based Distributed Intrusion Detecting System

One of the most important component of each corporate network is an intrusion detecting system. But it is a problem to find a mechanism for setting a trust between the nodes in a big distributed system. A blockchain can be used as such kind of mechanism, but most of working blockchains use cases related to cryptocurrencies. So there is no suitable consensus protocol for a permissionless blockchain, which can be used as a part of IDS. In this paper we propose a few modifications to proof of stake consensus protocol to adopt it for using with a blockchain based IDS. Also an agent based model was built to simulate a staking process for our modified consensus protocol. Modeling result will help us to evaluate performance of a blockchain based IDS in a normal state and in some critical situations and to find the most suitable consensus protocol parameters depending on a network size.
Double-Blinded Finder: A Two-Side Privacy-Preserving Approach for Finding Missing Children

Posting photos of suspected missing children on the street and posting them to social networks can help find missing children, but posting them to the Internet without protection may create privacy issues. In order to solve this problem, we propose Double-Blinded Finder, which is mainly used for low-dimensional multi-attribute matching of children’s face and blind face to find missing children and has high efficiency. To obtain enough knowledge for representing child faces, we build the Labeled Child Face in the Wild dataset, which contains 60K Internet images with 6K unique identities. We use this dataset to train a multitasking deep learning facial model, using a 128-dimensional feature vector and age and gender attributes to describe the child’s face. Due to the use of the face descriptor generation key, facial photos from the social network and facial information from the parents of the missing child are encrypted. In addition, we devise inner-production encryption to run blind face matching in the public cloud. In this manner, Double-Blinded Finder can provide efficient face matching while protecting the privacies of both sides: 1) Suspicious missing children in order to avoid human rights violations, 2) The real missing child is to retain the second victim. Experiments show that our system can achieve the actual performance of children’s face matching, and privacy leakage is negligible.
Recurrent Restricted Boltzmann Machine for Chaotic Time-series Prediction

How to extract effective information from large-scale time-series for prediction has become a hot topic in dynamic modeling. Benefiting from powerful unsupervised feature learning ability, restricted Boltzmann machine (RBM) has exhibited fabulous results in time-series feature extraction, and is more adaptive to input data than many traditional time-series prediction models. However, for the application of chaotic time-series prediction, RBM lacks a unique mechanism to capture time-series information. In addition, RBM only provides a feature extraction mechanism for dynamic modeling and cannot perform the task of regression prediction alone. In view of aforementioned problems, we propose a recurrent restricted Boltzmann machine (RRBM) to capture dynamic information and perform regression prediction by introducing a recurrent structure of leaky integral reservoir. This recurrent structure not only can remedy the dynamic characteristics, but also has a short-term historical information memory, which is more suitable for time-series applications. On this basis, a cross-layer connection is established between the feature layer of RBM and output layer of RRBM to achieve feature reuse and compensate for the missing information in the recurrent process. Experiments show that RRBM has smaller prediction error and higher information utilization.
An Online Evaluation Method Based on Entropy-Topsis Radar Interference Effect

In view of the real-time effect of radar interference, an online evaluation method based on entropy-topsis's radar interference effect is proposed, which is based on the comparison of the threat level of the radar target of some kind of interference strategy, and the effectiveness of the radar interference effect is judged. This method is proved to be feasible by the problem of typical pattern.
Convolutional Neural Networks for Real-Time Eye Tracking in Interactive Applications

Face alignment and eye tracking for interactive applications should be performed with very low latency or users will notice the delay. In this chapter, a face alignment method for real-time applications is introduced featuring a convolutional neural network architecture for face and pose alignment. The performance of the novel method is compared to a face alignment algorithm included in the freely available OpenFace toolkit, which also focuses on real-time applications. The approach exceeds OpenFace's performance on both our own and the 300W test sets in terms of accuracy and robustness but requires significant parallel processing power, currently provided by the GPU. For the eye tracking application, stereo cameras are used as input to determine the position of a user's eyes in three-dimensional space. It does not require synchronized recordings, which may contain redundant information, and instead prefers staggered recordings, which maximize the number of possible model updates.
An Improved Quantum Identity Authentication Protocol for Multi-party Secure Communication

Quantum secure identity authentication is a gripping part in the field of quantum communication which has an obvious superiority to makes the identity authentication system more secure. There are many existing multi-party quantum authentication protocols whereas not impeccable, some potential insecurity for some multi-party communication in those protocols can lead to be eavesdropped. Therefore, this paper aims to proposed an improved multi-party quantum identity authentication for secure communication scheme, by coalescing two advancing methods from Hong et al. [23] and Xiong et al. [24] and introduces a simple protocol that incorporates quantum identity authentication technique into multi-party communication medley with Greenberger-Horne-Zeilinger states as well as photons in Bell entangled states can be safely measured by legitimate parties. This protocol literally not only smooth over the insecure problems but also easy to realize. With this improved protocol, the authentication method can defend many attacks in the real world. In addition, effective protection against various attacks like eavesdropping or entangled-and-measure attack can also be achieved via this improved protocol.
A Comparative Analysis of Hybrid Encryption Technique for Images in the Cloud Environment

Cloud heralds a new era of computing. Cloud provides flexible, cost effective service. Cloud service is also referred as “On Demand Service” or “Pay as you go Service”. Mobile cloud computing is emerging branch of cloud computing which delivers services to mobile devices. As the smart phone usage has tremendously increased, the multimedia information such as pictures and video that is downloaded/uploaded from the cloud has also seen the rise. As our personal/sensitive information is with the third party, however, a significant question is, can we trust the cloud? There are many traditional security approaches are available to secure the data exchange between the users and the media cloud. But still there are instances of security breaches. This paper addresses the issues of security. Here we are proposing a hybrid encryption technique to secure the images. The scheme uses Elliptic Curve Cryptography to generate the secret key, which in turn used for DES and AES algorithms.
Adaptive LAMDA applied to identify and regulate a process with variable dead time

In this paper, an adaptive intelligent controller based on the fuzzy algorithm called LAMDA (Learning Algorithm for Multivariable Data Analysis) is presented in order to identify and regulate a process with variable dead time. The original algorithm has been used for supervised and unsupervised learning, whose main field of application is the identification of functional states of the systems. In this work a modification of LAMDA has been implemented which is capable of online learning using hybrid techniques. The proposal consists of two stages: training stage to learn about the unknown plant in order to establish initial parameters to the controller, and a second phase, called application, in which the control strategy is updated using online learning. The proposed method is tested in the control objective of regulation of a process with variable dead time, to analyze the viability of its utilization in these types of systems in which their dynamics are variable and unknown.
Implementing Gesture Recognition in a Sign Language Learning Application

Artificial Intelligence (AI) has become increasingly prevalent in contemporary times. It has a wide variety of application areas which can almost replicate tasks that humans would normally perform. Many companies that are using this form of technology are making efficiencies by replacing humans with AI agents. However, researchers are still making efforts to find ways to enhance artificial intelligence to be more `human-like'. Gesture recognition is a form of human computer interaction in which AI has the potential to improve. Similar to humans, AI has the ability to `see' and recognise gestures. Sign language is a language that a small proportion of the human population know and use. However, it is slowly gaining popularity and more resources are being provided in order to learn the language. Some people tend to go for in-person classes whereas others tend to go online or use applications to self-learn. This research discovers the success of technology such as gesture recognition to assist in learning sign language. The main research aim is to determine whether gesture recognition can assist self-learners in learning the language. The research has explored the use of Convolutional Neural Networks (CNN) to detect shapes that represent sign language form. The research demonstrated different accuracies based on a small sample size of 10 participants using three different types of datasets: non pre-processed, skin mask, and Sobel filtered images. The CNN model trained with the skin mask dataset was overall the most suitable model in identifying gestures from images; however, the CNN model trained with the non pre-processed dataset was slightly more accurate in recognising the American Sign Language (ASL) gestures in realtime. All CNN models demonstrated accuracy levels above 70% proving that the CNN has the ability to recognise sign language gestures.
Additional Feature Layers from Ordered Aggregations for Deep Neural Networks

In the last years we have seen huge advancements in the area of Machine Learning, specially with the use of Deep Neural Networks. One of the most relevant examples is in image classification, where convolutional neural networks have shown to be a vital tool, hard to replace with any other techniques. Although aggregation functions, such as OWA operators, have been previously used on top of neural networks, usually to aggregate the outputs of different networks or systems (ensembles), in this paper we propose and explore a new way of using OWA aggregations in deep learning. We implement OWA aggregations as a new layer inside a convolutional neural network. These layers are used to learn additional order-based information from the feature maps of a certain layer, and then the newly generated information is used as a complement input for the following layers. We carry out several tests introducing the new layer in a VGG13-based reference network and show that this layer introduces new knowledge into the network without substantially increasing training times.
Customer Detection and Tracking By Deep Learning and Kalman Filter Algorithms

Multiple people's tracking and detecting are essential and challenging tasks for monitoring vision, particularly in public places like shopping centers. We introduced a technical proposal based on the detection algorithm and tracking algorithm. In this investigation, we applied YOLOv3 as a detection algorithm and to raise the performance and velocity Kalman Filter was used as a tracking algorithm. We additionally validated the performance of the proposed detection method “YOLOv3” by performed a statistical analysis of detecting errors MSE method. The experimental results on a series of tests show that the efficiency of the proposed method is quite beneficial, and is able to detect people not only in closed space but in open space, especially when they are closer to each other as well.
Vision-based correspondence using relaxation algorithms for particle tracking velocimetry

Abstract A new particle tracking algorithm is derived based on consolidated methods, with the aim of overcoming the current limits encountered with high particle density flows. The proposed method consists of an integration of the relaxation algorithm based on matching probabilities into vision-based features association concepts. Hybridization with PIV pre-analysis is suggested to help with the estimation of parameters. A dual calculation strategy is also developed in order to reduce the amount of spurious vectors. Simulation tests using synthetically generated images are carried out to evaluate the sensitivity of the proposed method to the particle image density, the background noise and the nature of the flow. Three flow configurations with a growing degree of complexity are successively considered: a 2D flow over a moving wall, a steady 2D Lamb–Oseen vortex ring, and a 3D unsteady homogeneous isotropic turbulence. The ability of the new tracking algorithm to provide better matching performances with high reliability than conventional techniques, out of a dense particle image field, is demonstrated.
Development and validation of the lane-keeping controller using a similarity-type fuzzy reasoning method

This paper proposes the Similarity-type Fuzzy Reasoning method based on the calculated overlap coefficient of sensor rules and sensor input data for a lane-keeping controller. Fuzzy control reasoned by a membership function and human-adjusted rules. Those are required to improve the control performance. In this paper, instead of using the membership function, the inference result was calculated by the similarity of the rules and the input data. We try to implement the lanekeeping controller using the similarity-type fuzzy reasoning method, and this method has been succeeded to develop new advance two-stage fuzzy control.
Designing Transit Agency Job Descriptions for Optimal Roles: An Analytical Text-Mining Approach

In an effort to help the transit industry prepare better job descriptions for staff positions, this study looks at various job descriptions posted by the human resources department of a transit agency. The goal of the study was to establish a baseline for developing the job descriptions for each staff position. The implemented method identified the major keywords and concepts within the job descriptions. By detecting the keywords and keyword frequency within the job descriptions, the split of responsibilities between each position is visualized. The study then performed a causality analysis to identify the patterns between ranking within the organization and the corresponding keywords. This nested analysis has been rarely utilized in the literature, and the hierarchical organization is the best-matched algorithm for applying these methods to create meaningful patterns. The methodology is applied to a case study, a transportation sub-department within a county government department in Texas housing nine administrative, managerial, or supervisory roles. The results of this analysis help transform the data into knowledge for decision-makers and transit agencies. Comparison of the analysis outcomes highlights complementary staff positions (to facilitate effective teaming and project management) and/or overlap in duties and responsibilities that may present an opportunity for increased efficiency.
EMG based Signal to Control Home Appliances by Partially Paralyzed People

The paralytic society or a community feels that they are unable to cope along with their fellow beings who are not accompanied by such illness. This feeling of being differently-abled has frustrated the paralytic society to a high extent. Our project's main aim is to shine a light on this community to show that they are also equally productive and can live independently in a society of others who are normal. The EMG signal of 7 subjects (normal people and people with paralytic illness) were included in this study. The circuit includes C coding, the computer language platform for coding into Arduino UNO for the EMG RKI2401 module. A preset threshold is set for switching `on' and `off ' of the bulb. The EMG signal acquired from the module is of analog in reality and has been converted into a digital signal with the help of Arduino software. The EMG circuit consists of a two-relay circuit that acts as an “ON” and “OFF” switch. The developed circuit worked well for control of bulb using EMG module RKI2401.