Reviewerly/exHarmony huggingface dataset:
Deep learning based Layout Equivalence Detection in Compressed Domain and analysis of different Document Image Dataset: A Systematic Study

Use of electronic gadgets is increasing in this digital era as people often tend to use digital devices to perform there day to day activities. More and more fields such as government offices, banking sectors, educational institutions and so on use digital information for ease of storage and access. Today, the world is moving towards the paperless offices than using the physical paper documents. Paper documents once scanned get converted into document images. These document images have complex layouts which make it difficult to process. Processing these document images is a document image analysis problem known as DIA problem. It's been observed that the document image dataset is exponentially increasing which has simple to very complex structure leading to complex problems in processing. In order to find suitable solutions to these issues we intend to make use of a deep learning based approach for layout equivalence detection which can help to solve the retrieval problems and which may also be useful in the area of digital forensics based on the document layouts. Proposed approach follows two stages in which the first stage performs object detection using deep learning model followed by creation of bounding boxes for different entities which is further used to compare the layouts of the document images to predict the matching layouts for equivalence. Proposed model is a combination of object detection, bounding box creation and extraction of layouts which further uses Brute force matcher to perform equivalence detection. Here, we also present the state of the art document image datasets that is widely used by the researchers in the literature for solving DIA problems. Some of the publicly available datasets are considered for analyzing the results. The results show the robustness of the proposed approach.
Pedestrian Crossing Lights and Obstacles Detections for Visually Impaired Person

This research aims to develop a complete portable aid for visually impaired persons. It focuses on detecting the pedestrian traffic lights and obstacles in the way of a visually impaired person who crosses a pedestrian crossing. The system recognizes the pedestrian traffic lights in real-time with deep learning and provides signals to the visually impaired person to cross the pedestrian crossing in the form of audio output. It should work in both the day and night time and have good reliability. For the good reliability of the system, different sorts of images and video data have been collected, edited, and stored in a large amount of training data to get a good model.
Optimization of Substation Alarm Information Processing Based on BP Neural Network

When a fault occurs in the power grid, a large number of alarm messages will come out. In order to reduce the damage caused by power outages, the staff should find out the location and type of faults as soon as possible based on the alarm messages uploaded from the control center. However, we are currently facing problems in handling these alarm messages such as reliability of protection actions and circuit breaker tripping, correctness of received alarm messages, and the possibility of the existence of unreceived alarm messages. The fault tolerance of the BP neural network is studied in this paper, and the fault tolerance of the network corresponds to the size of the fuzzy zone formed by the test samples. The fault tolerance of the network is improved by eliminating the fuzzy zone, and a hierarchical causal rule reasoning network with a structure of four layers is established for each element in the station. An accessible path for the candidate cause of the fault is divided into two stages as follows: from the candidate cause to the protection operation and from the protection operation to the circuit breaker trip, which are assigned different credibility contribution rates. At the same time, the artificial bee colony algorithm is combined to identify the operation mode of the substation using its reasoning and judgment capacity and make the necessary corrections to get some output results of the neural network. The results of the examples suggest that the proposed evaluation method has a small workload of calculation and can identify the faulty components with high reliability and their causes, quickly and accurately.
Determinants of Professional Orientation of Future Psychologists on Correctional Support of Children with Psychophysical Disorders

This article is devoted to highlighting theoretical and methodological foundations of the study of professional orientation of future special psychologists on correctional support of children with mental and physical disorders. On the basis of the structural components of professional orientation of special psychologists developed by us, the main determinants of professional orientation of future specialists on correctional support of children with psychophysical developmental disorders were determined, specifically: in the study of the motivational-target component of professional orientation of the future specialists in the field of special psychology, we focused on the study of the dominant motives for choosing a profession; on formation of a humanistic orientation; on the meaningful life orientations of the individual; exploring the emotional and gnostic component of the professional orientation of future psychologists for correctional support of children with mental and physical disorders, represented by sensory and informational structures, we paid attention to formation and objectivity of understanding future professional activities, its requirements for the personality of a specialist; on the attitude to the future profession and to the process of mastering it; identification with the subjects of future professional activity; to the level of development of empathy, altruism, attraction and congruence; the regulatory component of the professional orientation of future special psychologists on correctional support of children with psychophysical disorders was studied taking into account the level of maturity of professional self-esteem and professional prognosis.
Covariance Matrix Evolutionary Preference-based Policy Search for Robot Confrontation

For robot confrontation task, reward derives only at the end, which makes the optimal policy learning very hard. Conventional deep reinforcement learning such as Deep Deterministic Policy Search (DDPG) performs poorly for sparse reward problems. To deal with this problem, in this paper, a novel approach, namely Covariance Matrix Evolutionary Preferencebased Policy Search (CMA-EPPS) is proposed. Specifically, the robot confrontation task is formulated as a preference-based task firstly. Then an evolutionary preference-based reinforcement learning policy search approach is developed. To improve the searching efficiency, covariance matrix adaptation is introduced, and to improve the optimization, a novel ranking method and a preference judging approach are also integrated. Finally, the proposed method is used in the robot confrontation environment and results show that CMA-EPPS can outperform the conventional methods such as DDPG.
A Two-stage Transition Correction Function for Adaptive Markov Matrix in IMM Algorithm

This paper proposes two probability correction functions to make adaptively the transition probability matrix(TPM). In a traditional interacting multiple model(IMM) estimator, TPM is usually considered a constant as initial values, so it is conveniently calculated by fixing prior information. However, inaccurate TPM can result in a large target state estimation error. To solve the problem, The IMM algorithm needs to have a time-varying transition probability so that the system model changes promptly according to the target movement. Therefore, a two-stage correction function is designed according to the period. The first phase is the accumulating transition probability correction function which increases the probability of the model matching the target movement and decreases others when the model jump does not occur. The second phase is the activating transition probability correction function which quickly updates the probability when the model jump occurs. By the performance comparison between the proposed adaptive IMM and the traditional IMM, the effect of probability correction functions is confirmed and the performance is improved.
CheapET-3: cost-efficient use of remote DNN models

On complex problems, state of the art prediction accuracy of Deep Neural Networks (DNN) can be achieved using very large-scale models, consisting of billions of parameters. Such models can only be run on dedicated servers, typically provided by a 3th party service, which leads to a substantial monetary cost for every prediction. We propose a new software architecture for client-side applications, where a small local DNN is used alongside a remote large-scale model, aiming to make easy predictions locally at negligible monetary cost, while still leveraging the benefits of a large model for challenging inputs. In a proof of concept we reduce prediction cost by up to 50% without negatively impacting system accuracy.
A simplified convergence theory for Byzantine resilient stochastic gradient descent

In distributed learning, a central server trains a model according to updates provided by nodes holding local data samples. In the presence of one or more malicious servers sending incorrect information (a Byzantine adversary), standard algorithms for model training such as stochastic gradient descent (SGD) fail to converge. In this paper, we present a simplified convergence theory for the generic Byzantine Resilient SGD method originally proposed by Blanchard et al. (2017) [3]. Compared to the existing analysis, we shown convergence to a stationary point in expectation under standard assumptions on the (possibly nonconvex) objective function and flexible assumptions on the stochastic gradients.
Sentiment Analysis on Public Transportation During Covid-19: An Exploratory Study

Public transportation is a backbone for cities. During Covid-19, public transportation became one of the sectors that did not stop due to the transport of goods and people. As a result, it is a critical issue for sustainable mobility. Due to improving the service quality of public transportation, passenger demands need to be investigated. This study presents sentiment analysis on public transportation as an exploratory study to take into account public demands. R programming is used to extract and analyze tweets about public transportation. Data and network analysis are conducted along with the sentiment analysis. The results show that there is a positive perception of public transportation despite the pandemic. In addition, wearing a mask, the Covid-19 test, and vaccination are seen as important issues. The study shed light on public opinions and perceptions about public transportation. Also, this work presents several significant results which might be critical to consider by decision-makers and practitioners.
3D Dynamic Spatiotemporal Atlas of the Vocal Tract during Consonant–Vowel Production from 2D Real Time MRI

In this work, we address the problem of creating a 3D dynamic atlas of the vocal tract that captures the dynamics of the articulators in all three dimensions in order to create a global speaker model independent of speaker-specific characteristics. The core steps of the proposed method are the temporal alignment of the real-time MR images acquired in several sagittal planes and their combination with adaptive kernel regression. As a preprocessing step, a reference space was created to be used in order to remove anatomical information of the speakers and keep only the variability in speech production for the construction of the atlas. The adaptive kernel regression makes the choice of atlas time points independently of the time points of the frames that are used as an input for the construction. The evaluation of this atlas construction method was made by mapping two new speakers to the atlas and by checking how similar the resulting mapped images are. The use of the atlas helps in reducing subject variability. The results show that the use of the proposed atlas can capture the dynamic behavior of the articulators and is able to generalize the speech production process by creating a universal-speaker reference space.
Detection of Online Hate in Social Media Platforms for Twitter Data: A Prefatory Step

Online social media (OSM) plays a major role in today’s life by providing people with a platform to share individual contents such as photos, ideas, opinions, and videos. However, at the same time, there are many negative carry-overs on social media users, which lead to uncontrolled and offensive communication called online hate. It has become a major concern for all online social media (OSM) users as it creates nothing but a negative influence on the users. Hence, developing a better generalized online hate classifier and network analyzer across domains is the challenge. This article highlights a novel 8-stage methodology that is proposed to develop an efficient generalized online hate classifier and network analyzer both which can help in overcoming the challenges. As a preliminary step toward 8-stage process proposed, the dataset related to women is collected from Twitter and analyzed to find out the frequent words used, number of retweets increased are negative tweets.
Design and Implementation of a Fast Convolution Algorithm for Embedded Platform

In recent years, deep learning has been gradually applied to the industry with great success. As the demand for the lightweight intelligent devices increases, the deployment of deep learning models on embedded platforms to meet users&#x0027; needs for real-time performance has become a trend in the development of intelligence. However, due to the pursuit of higher accuracy, existing deep learning frameworks are becoming richer in functionality and more complex in computation. A large amount of memory requirements and computational power demands make it challenging to deploy neural network computing frameworks on embedded platforms with limited resources and computational power. The WPOC algorithm is proposed and integrated into the Darknet framework to address real-time image processing based on the Winograd algorithm. Tested on the ZYNQ-7010 platform was passed. The results show that the WPOC algorithm proposed in this paper can effectively speed up image recognition by about six times under the VGG-16 model while ensuring the same accuracy rate.
Electrical Equipment Fingerprinting with Electrical Network Current Consumption

With the widespread use of electrical devices, device authentication and discrimination have received much attention. One of the possible device authentication methods is based on device fingerprints, such as software- or hardware-based characteristics. In this paper we propose a fingerprinting technique based on externally measured information, i.e. current consumption from the electrical network. The key insight is that hardware discrepancies naturally exist between same-model devices, making it feasible to identify small variations in the consumed current. We have successfully fingerprinted and identified five same-model electrical devices with 100% precision. Further research should be undertaken to investigate the current-consumption-based device fingerprinting and authentication.
Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN

Rock Paper Scissors is one of the most popular games in the world, because of their easy and simple way to play among young and elderly people. The point of this game is to do the draw or just to find out who loses or wins. The pandemic conditions made people unable to meet face-to-face and could only play this game virtually. To carry out this activity in a virtual way, this research facilitates a model in the form of image classification to distinguish the hand gestures s in the form of rock, paper, and scissors. This classification process utilizes the Convolutional Neural Network (CNN) method. This method is one type of artificial neural network in terms of image classification. CNN uses three stages, namely convolutional layer, pooling layer, and fully connected layer. The implementation of this method for hand gesture classification in the form of rock, scissors, and paper images in this study shows an increased average accuracy towards the previous study from 97.66% to 99%.
Statistical analysis and first-passage-time applications of a lognormal diffusion process with multi-sigmoidal logistic mean

We consider a lognormal diffusion process having a multisigmoidal logistic mean, useful to model the evolution of a population which reaches the maximum level of the growth after many stages. Referring to the problem of statistical inference, two procedures to find the maximum likelihood estimates of the unknown parameters are described. One is based on the resolution of the system of the critical points of the likelihood function, and the other is on the maximization of the likelihood function with the simulated annealing algorithm. A simulation study to validate the described strategies for finding the estimates is also presented, with a real application to epidemiological data. Special attention is also devoted to the first-passage-time problem of the considered diffusion process through a fixed boundary.
Discussion and Application of Blockchain Technology in Information Management of Internet of Things in Smart Lab

In today’s rapid development of higher education, the reform of higher applied undergraduate institutions and higher education has become an effective way to improve the quality of higher education personnel training and improved the quality of teaching. With the continuous expansion of the scale of university laboratories, the number of managers is also increasing, but the management of laboratories is still in the exploratory stage. The current laboratory information management system of Internet of Things has high degree of data heterogeneity, poor data interoperability, and difficult guarantee of data privacy, all of which lead to the difficulty in mining the data value of Internet of Things. The decentralized and zero-trust architecture of blockchain subverted the traditional centralized system architecture and has been applied in many aspects. Blockchain technology was a good addition to IOT to improve the privacy security, versatility, and reliability of the network. This thesis developed an IOT information management system for smart labs based on blockchain technology and the Internet of Things. The data processing efficiency, accuracy, and system throughput are compared with the original IOT laboratory information management system. The results showed that compared with the current laboratory IOT information management system, this system had higher speed, higher efficiency, and better performance in processing the information and data.
A Novel Approach for Feature Selection Using Artificial Neural Networks and Particle Swarm Optimization

Advancements in technology make the application domains to collect large amounts of data that is used for future decision making by applying various techniques such as classification, regression, etc. These techniques comprise machine learning algorithms. The running time complexity of these machine learning algorithms is determined by the number of instances and the total of all features or dimensions in the dataset. All the instances are generally required. But all the features in the dataset may not be related or useful in the machine learning task, for example, in classification. In such a case, the dimensionality of the dataset is reduced to optimize the running time complexity of the algorithms without compromising the accuracy. Reducing the dimensionality is achieved by extracting only those features which are more relevant. Hence feature extraction or dimensionality reduction plays a significant role in obtaining high accuracy of machine learning algorithms within the acceptable time complexity. The existing techniques for feature extraction are principal component analysis and by using correlation coefficients. But these techniques required more time complexity and some sort of manual decisions for selecting optimal features. To extract the most optimal features, a novel algorithm is proposed using an evolutionary optimization technique called particle swarm optimization (PSO). As a fitness function in PSO, an artificial neural network classifier pertaining to its efficient performance in classification tasks. The performance of the proposed algorithm is verified by five benchmark datasets from the UCI repository. The model is evaluated with different performance measures:accuracy, precision, recall, and the F-1 score. Research results have proven that algorithm proposed here reduces the number of features by improving the performance of the machine learning sub-system when compared to conventional algorithms.
Cyber Olympics: Game On!

There has been a rapid increase in cyberattacks by nation-states. This article looks at some global cyberpowers’ targets and motivations for such attacks.
Two Channel EOG Circuit Design and Implementation for Human Computer Interface

There are many factors that determine the quality of life of individuals whose movements are limited due to their special circumstances. One of the most important of these is the freedom to express their wishes and thoughts. Human-computer interface applications, on the other hand, stand out as assistive technology that can provide the freedom of communication of individuals with disabilities. In this study, a low-budget human computer interface solution is proposed to enable communication of private individuals who can move their eyes voluntarily. The system consists of a two-channel Electrooculogram (EOG) analog circuit, which is designed as a sensor-based and turned into a printed circuit.. With this circuit, EOG signals resulting from voluntary four-way eye movement and transaction confirmation movement were obtained and processed by signal processing methods. In addition, the human-computer interface has been completed with a personalized menu on the communication screen. The verification of the system has been tested with the tasks given to the subjects, who are the operators of the system, and it has been shown that high performance is obtained from the system.