Reviewerly/exHarmony huggingface dataset:
A band-shaped Mura detection method based on unsupervised deep learning

Mura is a phenomenon in which the displays have various uneven display defects. The band-shaped Mura has the characteristics of irregular shape and different sizes. And the new shapes and sizes of Mura may appear at any time during the inspection process. Therefore, traditional image algorithms are difficult to detect the band-shaped Mura anomaly. In response to the above problems, this paper proposes the Res-unetGAN network, which is an unsupervised anomaly detection method based on generative adversarial network. We design resnet50 as the encoding network of the generator to obtain the latent feature vectors. To improve the quality of reconstructed samples, we combine the skipconnection structure into the generator to guide the decoder. The discriminator is a convolutional neural network based on the Depthwise Separable Convolution. The purpose is to distinguish between normal samples and reconstructed samples, and form a game process with the generator. The network only needs normal screen samples during the training process. In the test, since the Mura sample has not been trained, the reconstruction error score of the Mura sample will be higher. After repeated experiments on the band-shaped Mura data set, the highest auc of 0.995 was obtained, which is better than several models for comparison.
Chromatic Dispersion in Subcarrier Wave Quantum Cryptography

We report on the chromatic dispersion role in subcarrier wave quantum key distribution system. We built a mathematical model of the change in multi-mode weak coherent phase-coded states in a fiber channel under the influence of chromatic dispersion and estimated the asymptotic secure key rate. The absence of any compensation will limit the maximum channel length to 53 km. We propose a method to mitigate dispersion effect which significantly increases the system’s reach. All theory shown is experimentally confirmed.
Noise Floor Estimation Based on Deep CNNs

This paper proposed a new method for noise estimation based on deep learning. We treat the wideband power spectrum as a one-dimensional (1-D) gray image and regard the noise floor estimation problem as a curve regression task. We design an end-to-end deep learning model based on convolutional neural networks (CNNs) to accomplish the task. By using sufficient numbers of simulation noise floor labeled spectra samples to train the model, experimental results show that our model can effectively regress the noise floor of the wideband power spectra. Comparing to the nonlinear recursive smoothing filter method, our method not only can be suitable for the single narrow carrier signal noise floor estimation but also gain good results when there exist multiple carriers in the wideband power spectrum.
Mask-Ranking Network for Semi-supervised Video Object Segmentation

Video object segmentation is the fundamental problem of video analysis and many methods based on mask propagation and matching have been proposed in recent years. However, the two strategies are highly dependent on the last mask or the fixed mask given in the first frame and hence cannot adapt well to high deformation and rapid motion of objects. In this paper, we proposed a novel architecture named Mask-Ranking Network (MRNet), which takes advantage of both the propagation-based method and the matching-based method, to address the above problem. Specifically, in order to make better use of the long-term previous masks, we propose a novel propagation mechanism to make the network comprehensively consider the previous information. Under a unified encoder-decoder framework, we track the pixel-wise similarity of the object activation area in a long-term manner and explore the correlation between frames. In contrast to propagation-based only or matching-based only techniques, our method reduces the accumulation of errors in the propagation process and effectively uses the long-term previous frame information. In the video object segmentation task, MRNet can better handle the deformation of the objects, and make the segmentation result more accurate. We validate the effectiveness of the proposed method on the DAVIS 2016 and DAVIS 2017 dataset. Experiment results show that our method achieve state-of-the-art performance without using online fine-tuning and is robust to long-term propagation.
Mitigating Risk of IoT Sensing Data Error Using Platform as a Data Handler

Sensing node principally is only responsible for sending data to the server that later on will forward the data to the user. Through this mechanism, the user will receive the data from the sensing node based on the value sent without any data verification at the server-side. The data in this case could be invalid. Thus, error handling as the main problem of the research is needed. By ensuring that this problem is worth objectivity raising, then through the Risk Management Process Model, it is certain that this problem will cause an adverse threat. After identification, 6 arising risk were found, with the level of control at threat”. Furthermore, 7 Mitigation Actions (MA) were designed and grouped into 3 Mitigation Phases (MP). Mitigation plan was realized into a platform with several system blocks, including 2 middleware blocks giving data error handling service directly. Moreover, to ensure that the proposed platform has covered all existing risks, service guarantee agreement was made in the form of a Service Level Agreement (SLA).The test results showed that both SLA 1 and SLA 2 succeed in guaranteeing no more risk arising. The test results showed the security level value is 6/ 6=1, which means all of the risks are ”handled” status and succeed in scope of ”eliminate”. Eventually, when the proposed platform is adopted directly to the organizations that need it, it is expected that it can be the answer to the problem of error in data sensing.
An Intelligent Computer Vision for Children Affected with Cerebral Palsy

This article is to improve communication with the children affected with cerebral palsy by using a computer vision. cerebral palsy is a permanent movements disorder that appears in childhood. It affects their movements, sensation, and speaking so the children differ from normal children. The technology can improve communication between the children and parents by using an open cv python programming and convolutional neural network(CNN). It detects the facial expression and body pattern of the children to give accurate results of the emotion or needs of the children. then it intimates the alert message to the parents through the mobile application.
Improved PULSE: (Photo Upsampling via Latent Space Exploration of Generative Models) Using Anti-spoofing System

Abstract Image super-resolution is typically explained as converting a low-resolution (LR) image into a high resolution (HR) output. However, previous approaches could be blurry and unrealistic, especially for regions with many details. This paper took advantage of Latent Space Exploration from PULSE and a facial liveliness detection algorithm to receive a more accurate and rational outcome. (Related code can be found on https://github.com/cisgroupb/PULSE with Anti Spoofing-Detection)
Improving Multi-Agent Cooperation Using Directed Exploration

In this work, we are addressing the problem of fully cooperative multi-agent system (MASs) with the same common goal for all agents. Coordination question is the main focus in such systems: how to ensure that the agents' own decisions contribute to the group's jointly optimal decisions? To solve this, a new multi-agent reinforcement learning algorithm, named TM LRVS Qlearning, is introduced and tested. The usefulness of this new method is shown using a simulated hunting game.
Time-Optimized Software Implementation of the Present Cipher on Python Language

This paper shows the development of the PRESENT cipher using Python 3 language, this cipher uses 64-bit data and 80-bits or 128-bit keys.The main objetive is optimizing the execution time of the algorithm in order to be used along with different kinds of embedded systems such as microcontrollers, SoCs (System on Chip) or even programable logic devices like FPGA (Field Programable Gate Array) for IoT (Internet of Things) and/or smart sensor networks applications.First, some bit-wise operations which are commonly used in cryptographic algorithms were implemented and tested for defining which must be used in the final implementation.After, three different approaches of implementing the complete PRESENT cipher on Python were proposed, the third version reached a time reduction from 25.3% to 45.3% compared with the other two versions.These time reduction percents depend on the kind of operation (cipher or decipher) and the width of the key (80-bit or 128-bit).Finally, the algorithm implementation obtained shown that it is able to be used on the applications previously mentioned as well as it can be used on embeded systems which can run a Python interpreter.
An Artificial Bee Colony Inspired Density-Based Approach for Clustering with New Index Measure

This article suggests a clustering approach that is inspired by the random searching of the Artificial Bee Colony optimization. It considers the density distribution of the objects which gives an insight into identifying the cluster structures. The unknown density distribution has been approximated by using Kernel Density Estimation, it eventually helps in identifying the thresholds that discriminate the core objects from the noise and intercluster density transition. We have introduced a new index measure that serves the purpose of cluster evaluation. The superiority of the proposed approach has been drawn from the experimental analysis over different data sets.
Solving Hard Problems by Protein Folding?

Inspired by the NP-hardness of string folding problems modeling the natural process of protein folding, we discuss the idea of solving instances of NP-hard problems (e.g., string folding problems) of moderate size by letting artificially assembled proteins to fold. The accuracy with which one can combinatorially model the protein folding process, e.g., by string folding, as well as the precision with which one could experimentally estimate the energy of folded artificial proteins are crucial issues.
Towards a PGD-Based Computational Vademecum for Robot Path Planning

The present paper describes the use of the technique known as PGD-Vademecum in potential-based path planning for mobile robots. The basic idea of the method is to obtain a Vademecum with all the possible robot paths from any start to any goal derived from a harmonic potential field in a predefined map, including any possible dynamic obstacle positions. The PGD is a numerical technique that offers three important advantages over other methods. First, the ability to calculate all the possible Poisson equation solutions for all start, goal and dynamic obstacle combinations in a map, ensuring that the resulting potential field does not have local minima. Second, the PGD-Vademecum can be expressed as a sum of uncoupled multiplied terms: the geometric map and the start and goal configurations. Consequently, the harmonic potential field for any start and goal robot positions can be reconstructed very fast, in an almost negligible computational time, allowing real-time path planning. Third, just a few uncoupled parameters are required to reconstruct the potential field with a minimum discretization error. The abilities of this technique are demonstrated by means of simulation results.
La educación en contexto de pandemia por COVID-19: Implicaciones de la enseñanza en línea en el desarrollo de habilidades psicosociales de docentes angoleños, argentinos y brasileños

The COVID-19 pandemic impacted people and socioeconomic sectors around the world, creating a context of uncertainties and challenges. Education, as well as other sectors, has been developing its role through technologies and teachers have been adapting to the challenge of carrying out their work remotely. However, after more than five months of online teaching work, many obstacles begin to be overcome and adaptation is occurring more and more successfully, thus contributing to the development of psychosocial skills. This study is a pilot test for which the Questionnaire on Teaching Psychosocial Skills (CHPD) has been developed with the aim of knowing the psychosocial skills of teachers, developed throughout these months of online work. The questionnaire was administered to 50 secondary school teachers from Angola, Argentina and Brazil. The descriptive method was used, with a mixed approach. For the analysis of quantitative data, systematic observation and content analysis were used for qualitative data. The results showed the difficulties of teachers to adapt to the work remotely, but also the skills developed over the more than five months of online work and showed that the CHPD is an effective instrument to measure teacher psychosocial skills.
Robust Variational Learning for Multiclass Kernel Models With Stein Refinement

Kernel-based models have a strong generalization ability, but most, including SVM, are vulnerable to the curse of kernelization. Moreover, their predictive performance is sensitive to hyperparameter tuning, which demands high computational resources. These problems render kernel methods problematic when dealing with large-scale datasets. To this end, we first formulate the optimization problem in a kernel-based learning setting as a posterior inference problem, and then develop a rich family of Recurrent Neural Network-based variational inference techniques. Unlike existing literature, which stops at the variational distribution and uses it as the surrogate for the true posterior distribution, here we further leverage Stein Variational Gradient Descent to further bring the variational distribution closer to the true posterior, we refer to this step as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stein Refinement</i> . Putting these altogether, we arrive at a robust and efficient variational learning method for multiclass kernel machines with extremely accurate approximation. Moreover, our formulation enables efficient learning of kernel parameters and hyperparameters which robustifies the proposed method against data uncertainties. The extensive experiments show that without tuning any parameter on modest quantities of data our method obtains comparable accuracy to LIBSVM, a well-known implementation of SVM, and outperforms other baselines, while being able to seamlessly scale with large-scale datasets.
PSYCONALYSIS LITERATURE CRITICS: MAHDI CHARACTERS IN JENNY & MAHDI MULIADI GF’S SHORT STORY

In general, this study aims to describe the character and inner conflict of Mahdi's character in the Jenny & Mahdi short story by Muliadi GF. Based on the problem above, this study aims as follows to describe the character of the characters in the Jenny & Mahdi short story by Muliadi GF; and describe the inner conflict with the short story character Jenny & Mahdi by Muliadi GF. The method used is the method of literary criticism in the form of text analysis (literary works) because only with analysis of any literary work can the meaning be explained. The characters of Mahdi in this short story include: a person who never gives up, a person who is closed and doesn't talk much, and anger and curses. The inner conflict of Mahdi's character in this short story covers inner conflict because of his closed attitude and solitude and inner conflict due to obsession. Keywords: character; conflict; critic
Task Distribution Method for Index Classification based on Color Segmentation in Remote Sensing

Digital processing of remotely sensed image data has been great importance in recent times. This research work discusses task distribution method in parallel image processing and load balancing under the circumstance of multi-tasks and multi-processors in remote sensing. Task distribution method can speed up computation and improve efficiency and perform larger computations which are not possible on single processor system. The tasks are distributed based on the segmentation of color and the Support Vector Machine (SVM) is used to classify the indices of the input image and intends to design and improve the color segmentation based task distribution method for index classification using machine learning. In the system, the RGB satellite image is used as an input image and the output is the four indices of forest, building, road and land The results of the system are more accurate and less time consumption than non-distributed computing methods. It is implemented in MATLAB platform with parallel computation toolbox because the system can solve computationally and data-intensive tasks using multicore processors and clusters of computer.
Single image dehazing algorithm using generative adversarial network based on feature pyramid network

This paper proposes a single image dehazing algorithm using generative adversarial network (GAN) based on feature pyramid network (FPN). This method is an end-to-end image dehazing method, avoiding the physical model dependence. The generator uses MobileNet-V2 as the backbone network, and uses the FPN structure to improve the feature utilization rate of the image, combined with the discriminator formed by the convolutional neural network to form GAN that can improve the training stability and convergence of the generator. The model uses a lightweight MobileNet-V2 network, and the FPN structure also enables multiple-scale feature maps to be obtained while avoiding the use of direct scaling, thus reducing the computational power and memory requirements and allowing the model to operate with limited computational resources. We used the RESIDE training set to train our proposed model and conducted extensive experiments on the test set. The experimental results show that the algorithm has satisfactory results in terms of quality and speed.
Image Retrieval with Data Augmentation of Sentence Labels Based on Paraphrasing

Text-based image retrieval is a fundamental study in the field of information retrieval. Recent text-based image retrieval methods employ deep neural networks (here-inafter referred to as deep neural TBIR) to retrieve a desired image from a sentence query and achieve the state-of-the-art performance in TBIR. To improve the retrieval performance of the deep neural TBIR method further, it is essential to prepare diverse sentence labels in training data. However, it takes a lot of effort to prepare diverse sentence labels in training data. To address this problem, we propose a novel deep neural TBIR method with data augmentation of the sentence labels in training data. Experimental results show the effectiveness of the proposed method.
Building Banyuasin Village, Bangka Regency as green and agro-tourism village that sustainable using information technology

Abstract Tourism is currently one of the state’s income to support the economy. Therefore, almost all countries develop their regions so that they can become destinations for tourists. Indonesia is one of the countries that encourages and supports the development of tourism to the village, so that each village designs so that its village can become a tourist destination. Now, every village in Indonesia have been race to developing their village into green and tourism village in order to improve the welfare of the village. Banyuasin village is one of the villages in Bangka Regency which has agro-tourism potential because of many agricultural and plantation potentials that can be used as agro-tourism destinations and also development to become an environment-based village. Banyuasin village also has a plan to become a green village by doing good waste and waste management by using information technology. To support Banyuasin village plan for achieve the goal, the use of information technology and building an information system that helps to develop the Banyuasin village in the future. This information system will design and process data related to the potential of Banyuasin village to become a Green and Agro-tourism village as well as to process the data to help Banyuasin Village.
Sound Reactive Bio-Inspired Snake Robot Simulation

We present a hardware and software framework in which we use the direction of the sound source to interact with the simulation of a snake robot. We present a gamification idea (similar to hide and seek) of how one can use the direction of the sound and develop interactive simulations in robotics and especially use the bio-inspired idea of a snake's reactive locomotion to sound. We use multiple microphones and calculate the direction of sound coming from the sound source in near real-time and make the simulation respond to it. Since a biological snake moves away from a sound source when it senses vibrations, we bio-mimic this behavior in a simulated snake robot. This idea can be used for developing games that are reactive to multiple people interacting with a computer, based on sound direction input. This is a novel interface and first of its kind presented in this paper.
A Proficient Deep Learning Approach to Classify the Usual Military Signs by CNN with Own Dataset

Everyday, around the world crimes, like kidnapping or forced to do something to enemy’s command, are happening. General people are being the main victim in most cases. Hostage people are usually rescued by military or special force sometimes. The best way to build communication between hostage and military is by using the basic sign language of that military or special force. In this research, we analyzed 2400 images for 24 different basic signs what they use in their real mission. For this analysis, we classified their basic signs by convolutional neural network (CNN) algorithm. This research will help general people to take decision on hostage circumstances so that they can easily communicate with the military who have gone there to rescue them. We used multiple convolutional layers and get 92.50% accuracy. Using our model in any real system, a novice member of the military or special force can learn and validate his sign.
Cognitive Cybernetics in the Foresight of Globalitarianism

This paper presents the results of the research conducted with the help of cognitive cybernetics about the "mass" factor from the theory of totalitarianism.According to the expert system model, "big data" analysis sought to discover knowledge for assessing the future status of digital social connectivity.Originally developed models and methods of cognitive and computer research and processing of "eminent text" with the help of "convolution" from the theoretical background of many works about the past totalitarianism, recognize the same characteristics of emerging globalitarianism.Using search correlation, algorithms have confirmed a suspicion that intelligent interactive technologies impact the changes of the human psychophysical structure, through the digital social network of globalitarianism.Applying new intelligent interactive technologies without being familiar with their deeper impact, could plausibly make people accept them as part of themselves.People's digital obsession with the internet in a "global village", is increasingly similar to blind obedience, of the sympathizers and followers, gathered around past totalitarianism's grand leaders.There is a visible correlation between such technological-integration engagement, and the loss of social intelligence, leading to a conclusion that the future interactions between people and intelligent technologies will turn the history into the implosion of current events.History today is already pretty accelerated, due to its own mass digital integration and interaction, turning it into an illusion.There is a danger that globalitarianism, as an elusive end of the history, will escape cyclical times, due to unavoidable repetitiveness.