Reviewerly/exHarmony huggingface dataset:
Deep Active Imitation Learning in FIFA Free-Kicks Player Platforms Based on Raw Image and Object Detection State Representations

This paper investigates the use of the Deep Active Imitation (DAI) learning strategy in single and multi-agents player platforms for FIFA game. DAI is a supervised Deep Learning training strategy in which previously trained agents evolve by mimicking human experts' behavior through active learning. Such learning strategy has only been validated in single-agent scenarios where the environment is perceived through raw images. FIFA, in spite of being a successful soccer simulation video game representing a very realistic and challenging case study, has been relatively poorly explored in the context of Machine Learning research. Then, the main objective of the present work is to investigate the efficacy of DAI to cope with single and multi-agents FIFA platforms in which the environment is represented either by Object Detection Techniques (ODT) or by raw images. The agents were evaluated in terms of the rate of goals obtained in the free-kicks scenarios provided by FIFA. The results show that DAI performs well on both the single and multi-agent based platforms. Further, the raw image based technique proved to be better than the ODT based method to represent the environment.
Identification of QR Code using Kalman Filter Algorithm

In this paper, we describe the development of application that support for tracking QR codes. The problem in this case, arises when scanning the QR code there is a lack of accuracy when the position of yawing as an axis (x), surging around the axis (z) and pitching as axis (y). Apart from the position of QR code, when the application is tracking it, the state of the room also affects the accuracy of scanning process. Therefore, we use the Kalman Filter (KF) algorithm to increase the accuracy of tracking process. The results show that the prediction and correction steps of the KF provide better accuracy of the QR code in the application.
Design of Configurable Digital Spike Filtering Circuits in 130 nm CMOS Process

In this paper, configurable digital spike filtering circuits are explored for removing unwanted noise pulses from the signals. The width of the spike to be suppressed from the signal is configurable. The circuits identify and remove both positive and negative spike pulses accurately. The presented circuits are implemented in 130 nm CMOS process. The asynchronous circuits are implemented in fully custom and fully synthesizable techniques which occupy an area of 252 × 323 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 127 × 127 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and consumes 419 nW and 226 nW of power respectively from 1.2 V supply. The presented synchronous spike filtering circuit is fully synthesizable and it needs only 348 gate counts for its implementation with an area of 68 × 68 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . It draws only 83 nA current form supply for its full operation. The circuits are measured extensively and experimental results verify the accuracy of the proposed spike filtering circuits.
Marine Biological Image Recognition Based on Deep Convolutional Neural Network

Marine image recognition is a basic research of underwater robots. Because the marine images are weak, variable lighting, low contrast and blurring, marine image recognition is a difficult task. In the traditional marine image recognition methods, feature extraction depends on manual guidance and prior knowledge. And as the amount of data increases, the efficiency is drastically reduced. So, a marine biological identification method based on deep convolutional neural network is proposed. The marine biological image recognition based on deep convolutional neural network (MB-DCNN) has few structural parameters, the accuracy rate reaches 85.9%.
Variational Policy Chaining for Lifelong Reinforcement Learning

With increasing applications of reinforcement learning in real life problems, it is becoming essential that agents are able to update their knowledge continually. Lifelong learning approaches aim to enable agents to retain the knowledge they learn and to selectively transfer knowledge to new tasks. Recent techniques for lifelong reinforcement learning have shown great success in getting an agent to generalise over several tasks. However, scalability becomes an issue when agents learn numerous tasks, as each task's information must be remembered. To address this issue, this paper proposes the approach of Variational Policy Chaining (VPC) which enables a reinforcement learning agent to generalise effectively in a scalable manner when presented with continuous task updates, without storing multiple historic experiences. VPC uses Kullback-Leibler divergence based method to isolate the most common pieces of knowledge, and condenses the important knowledge into a single policy chain. We evaluate VPC in a GridWorld environment and compare it to vanilla policy gradient methods, showing that VPC's ability to reuse knowledge from previously encountered tasks reduces learning time in new tasks by up to 50%.
End-to-End Protocols and Performance Metrics For Unconstrained Face Recognition

Face recognition algorithms have received substantial attention over the past decade resulting in significant performance improvements. Arguably, improvement can be attributed to the wide spread availability of large face training sets, GPU computing to train state-of-the-art deep learning algorithms, and curation of challenging test sets that continue to push the state-of-the-art. Traditionally, protocol design and algorithm evaluation have primarily focused on measuring performance of specific stages of the biometric pipeline (e.g., face detection, feature extraction, or recognition) and do not capture errors that may propagate from face input to identification output in an end-to-end (E2E) manner. In this paper, we address this problem by expanding upon the novel open-set E2E identification protocols created for the IARPA Janus program. In particular, we describe in detail the joint detection, tracking, clustering, and recognition protocols, introduce novel E2E performance metrics, and provide rigorous evaluation using the IARPA Janus Benchmark C (IJB-C) and S (IJB-S) datasets.
When space is built of light, matter and sound

In Polytopes, we find many examples of spatial sound architectures composed of different figures and transformed sound constellations, exchanged and combined on an ongoing basis. The mobility of these figures and sound is present in its structure, location and spatial position. The opposition and fusion of sound elements, a constant, it creates a moving space by imposing a multidirectional and multidimensional listening.
Pengaruh Metode Partisipatori terhadap Kemampuan Menulis Teks Deskripsi

This study aims to examine the effect of participatory methods on the ability to write descriptive text for grade VII students of SMP Negeri 1 Sukabumi City in 2018/2019 Academic Year. The main subjects in this study were students of class VII G, amounting to 36 students. The purpose of this research is to know the ability to write description text before and after using participatory methods, and determine the effect of participatory methods on the ability to write descriptive text. The research method used in this study is a quantitative method using a one group pretest-posttest design research design. The results of the study were obtained when the pretest average value of students is 40 while the posttest average value of 65.41. This has increased after researchers used participatory methods. The results of the calculation of each test were carried out using the t-test and it was significant to pretest and posttest so that the results obtained were tcount (12.29)&gt; ttable. These results indicate a difference that occurs between the pretest and posttest. It can be concluded that participatory learning methods affect the ability to write description texts for grade VII students of SMP Negeri 1 Sukabumi.&#x0D; Keywords: Writing, Description Text, participatory
Research on Heuristic Mutated Particle Swarm Optimization Algorithm

In order to solve the problem of thrust distribution in dynamic positioning, based on the particle swarm algorithm, take the thruster system consisting of azimuth thrusters and lateral thruster as the research object. Considering the interference, maximum capacity, force and angular rate, singularity of the thruster system, a power distribution optimization model is established with the goal of optimal power and minimum wear. In the particle swarm optimization algorithm, the pseudo-inverse algorithm is introduced as a heuristic search strategy, and a mutated particle swarm optimization algorithm is improved. A heuristic mutated particle swarm optimization algorithm (HMPSO) is proposed. The HMPSO algorithm can effectively reduce energy consumption and thruster wear via simulation and ship model experiment.
Computing Classical-Quantum Channel Capacity Using Blahut–Arimoto Type Algorithm: A Theoretical and Numerical Analysis

Based on Arimoto's work in 1972, we propose an iterative algorithm for computing the capacity of a discrete memoryless classical-quantum channel with a finite input alphabet and a finite dimensional output, which we call the Blahut-Arimoto algorithm for classical-quantum channel, and an input cost constraint is considered. We show that, to reach ε accuracy, the iteration complexity of the algorithm is upper bounded by log n log ε ε where n is the size of the input alphabet. In particular, when the output state { ρ x } x ∈ X is linearly independent in complex matrix space, the algorithm has a geometric convergence. We also show that the algorithm reaches an ε accurate solution with a complexity of O ( m 3 log n log ε ε ) , and O ( m 3 log ε log ( 1 - δ ) ε D ( p * | | p N 0 ) ) in the special case, where m is the output dimension, D ( p * | | p N 0 ) is the relative entropy of two distributions, and δ is a positive number. Numerical experiments were performed and an approximate solution for the binary two-dimensional case was analysed.
Preprocessing Improvement in Mime Speech Recognition based on Surface Electromyogram

By extracting electrical signals on the surface of the speech-related muscles, which are, surface electromyography signals, information of the brain can be traced back to achieve silent speech recognition. Preprocessing is a crucial step for silent speech recognition and subsequent phases. In preprocessing of mime speech signals, baseline drift and active segment detection play an important role, but efforts in the direction are very limited. In this paper, the quadratic variation reduction separation baseline drift was introduced. On this basis, the three-parameter double threshold method based on autocorrelation function, band standard deviation, and short-time energy were used to detect the active segment of the surface electromyography signals, and good results were obtained. Finally, by extracting features, silent speech recognition was classified by the random forest algorithm. Experiments showed that the method described in this paper obtains 85% correctness in the data set of 9 words.
Analysis of Different cases of Blurs in Images Using the Concept of Key points

Photos are a means to keep our beautiful memories. Although we have high-resolution cameras nowadays, blurs in images is a common phenomenon. Blurriness can be of various reasons, as they may be caused due to defocus or can be due to focusing on specific objects. So the types of blurs in images are analyzed. Blur analysis becomes vital when they are being enhanced. During image enhancement, enhancing highly sharp or highly blurred image region produce obnoxious results. Firstly, types of blurs, mainly motion blur and defocus blurs are analyzed. Then key points of images are detected. Matching of key points is done for a visual analysis as well as a match metric value is assessed to detect the deviation of interest points from the original image. Also, a blur percentage is calculated as well as a histogram metric is evaluated.
Twins' Identification using Single-Shot Detector

Auto-tagging identical twin siblings on social media is a challenging problem. In computer vision, such problems are called fine-grained visual-recognition challenges (FGVC). Though CNNs have been popular in detection problems, there is tremendous scope for improvement in FGVC. In this paper, we implement a CNN-based model for detecting the celebrity `Olsen' twins. We first created an annotated dataset of 490 public images of the twins, `Mary-Kate' and `Ashley'. We observed that in only a few images the differences between the twins were clearly noticeable. We called these images `critical images' as they would be vital for improving the detection accuracy. However, they were significantly outnumbered by images in which the differences were not clearly visible. This resulted in the smoothening of distinguishing features during training. We used a simple, yet effective approach to address this problem. We split our dataset into two sets - `images with critical features' and `images without critical features'. We first fine-tuned our model using the non-critical images. We then fine-tuned only the last layer using the critical images. By doing this, we were able to preserve the most distinguishing features for the fully connected layer. The detection architecture used was Single-Shot multibox Detector (SSD) with a pretrained VGGNet base-network. SSD provides multi-scale feature learning. Our results show that by splitting the training data, we get a mAP score of 0.4563 for Mary and 0.3861 for Ashley, which is a significant improvement over training without splitting. Since the split sets are smaller in size, the training time is also reduced. We conducted our experiments on NVIDIA Tesla T4 GPU in Google Colab.
A Novel Approach on Error Detection and Correction using Feedforward Artificial Neural Networks

According to a Forbes survey around two and a half quintillion bytes of data is being created on the internet each day. With such a rate the amount of data to be structured is going to double in a time span of nearly one and a half years. It is not only the amount of data but the number of systems that communicate the data with each other increasing the complexity even further. With such a huge combination of data exchange possible, there is a high chance of erroneous data communication between communication channels. Though there are some hardware technologies like optical fiber which reduces such errors the cost for such systems is very high and impractical to implement on a full hundred percent scale. Moreover, with the current technology of error correction and reduction, the system is not immune to a large number of changes in the communication channel. If the number of bytes changes either in the data word or the appended code word the whole algorithm remains vulnerable. Hence in this paper, artificial neural networks are used to reduce the errors and make the system immune to a high number of changes in the data word or even the code word. This is achieved using artificial neural networks as an encryptor and doing overfitting of the data on the bytes. Since composite functions are created for every hidden layer of the neural system, therefore the change if a higher number of bytes does not affect the neural network output considerably and this forms the crux of this paper.
Visual Engagement and Historical Accuracy in a Production for a Video Mapping Projection

The current article documents the development of an audiovisual production, projected with video mapping technique at Antigos Paços do Concelho, on May 2019, titled "Viana do Castelo: Uma Viagem no Tempo", showcasing the local history and cultural heritage of Viana do Castelo. The audiovisual production involved the project's researchers, specialized in the areas of animation, illustration, multimedia and computer graphics. To ensure the historical accuracy, the contents were considered and validated by the history and education team, and then developed and adapted into a visual-based narrative. Usually, the most known use of a video mapping technique is for visual special effects, without a communication intent of historical facts. Interested in that unexplored potential, the present project innovates by developing an audiovisual production, to be projected using the video mapping technique to achieve the main goal of communicating and diffusing historical knowledge. By showing the problems, the limitations, the methods and the solutions employed, this article explores how the project team adapted the contents using a video mapping technique, through a narrative-based production.
An Hour of Gingerbread Fun or a Lesson?

The article presents the experience of cultural and educational activities conducted with children at the ART-Radouga Creative Development Center under the Association of Russian Culture in Strasbourg (France). Teaching the Russian language and familiarizing yourself with the original Russian culture in a multilingual environment required teachers working abroad to search for new original methodological approaches. The training sessions described by the author based on the use of traditional technology for making gingerbread for Russian folk and applied crafts help to successfully solve many educational problems: turn a child-listener into an active and active participant in the development of the Russian language and literature, motivate to study the folklore heritage and decorative and applied art of Russia, traditions of book illustration, to develop creatively, putting into practice their own projects of «gingerbread theme». The published material can be interesting and useful for all practicing educators.
Improved CRT Image Steganography based on Edge Areas and Spread Embedding

Chinese Remainder Theorem (CRT) is one of the spatial domain methods that is more implemented in the data hiding method watermarking. CRT is used to improve security and imperceptibility in the watermarking method. CRT is rarely studied in studies that discuss steganographic images. Steganography research focuses more on increasing imperceptibility, embedded payload, and message security, so methods like LSB are still popular to be developed to date. CRT and LSB have some similarities such as default payload capacity and both are methods in the spatial domain which can produce good imperceptibility quality of stego image. But CRT is very superior in terms of security, so CRT is also widely used in cryptographic algorithms. Some ways to increase imperceptibility in image steganography are edge detection and spread spectrum embedding. This research proposes a combination of edge detection techniques and spread-spectrum embedding based on the CRT method to produce imperceptibility and safe image steganography method. Based on the test results it is proven that the combination of the proposed methods can increase imperceptibility of CRT-based steganography based on SSIM metric.
News Data Analysis from Facebook Through MongoDB and Hive

The research aims at doing a comparative study for news data analysis through MongoDB and Hive. The news posts or feeds are gathered from the official Facebook page of The Times of India with the help of Facebook Graph API. The data is stored in a NoSQL database, MongoDB and the Hive data warehouse of Hadoop ecosystem. For better data handling and quick query processing, the data is sharded (partitioned) in MongoDB as well as partitioned in Hive using the date field as the sharding key and the partitioning key in the respective databases. A comparative study is done between the two platforms by running various queries on the data and comparing the execution time. The queries aim to search the news posts on the basis of date ranges, particular keywords in the heading or through any other field like maximum laugh reactions. The study was done on a single machine system for both MongoDB and Hive. The results indicate less execution time for all queries with MongoDB as compared to Hive.
An Adaptive Mission Planning Method for UUV Combat Operations Under Uncertain Conditions

This paper presents a novel method of mission planning under uncertain conditions for UUV combat operations. Planning within this domain is challenging because complex combat environment prevents computation of an optimal strategy in advance. Based on the analysis of the key factors affecting the execution of combat missions, such as combat environment, operation objectives and UUV system, the mission planning method which combines online monitoring, evaluation, prediction and adaptive adjustment is designed. This method has the ability of revising the initial plan in respond to observed information. The energy, data storage space and weapon loads evaluation models related to combat missions are established respectively. The mission goals selection algorithm is studied to enable UUV to adjust the plan autonomously. The simulation results show that the method has good adaptability to complex operation conditions, which can effectively reduce the operational risk of UUV.
Dynamic Data security for Hadoop Systems using Fuzzy Adaptive Security Profiles (FASP)

Hadoop is an efficient parallel processing platform with ability to handle large volumes of data termed as big data. Ability to handle large volume of data using parallel processing means has made Hadoop widely adapted by various academics and Industries. Hadoop was not designed with security considerations during initial times and as Hadoop got deployed in public clouds; its security vulnerabilities became important. Towards this end, a Fuzzy Adaptive Security Profiles (FASP) is proposed in this work. The FASP is able to provide dynamic security profiles adaptive to the security and processing requirements. The system ensures data security for the HDFS with machine learning based attack detection.
A Hybrid Alter Learning Rate Learning Vector Quantization Gaussian Gravity Search Algorithm for Multi Robots’ Tasks Allocation and Route Planning

Given the problems of lower system running efficiency and real-time tracking leading by the complex information, huge computation, non-equilibrium of robots' tasks allocation in the process of tasks allocation, firstly we proposed the Alter Learning Rate Learning Vector Quantization (ALLVQ) algorithm to conduct tasks allocation. That is the direction of robot's location is updated by setting the distance threshold of sub-tasks and the location is updated by adopting learning rate mixing distance cost, which makes the multi robots can adjust the location and distribution in the global tasks allocation independently and efficiently. Then, in order to improve the task execution efficiency, the Gaussian Gravity Search Algorithm (GGSA) is proposed to solve the route planning of allocated tasks, in case the optimal solutions fall into in the local solution. Finally, the correctness and validation of the proposed algorithms are testified by simulation experiments, in contrast with, K-means, Affinity Propagation Clustering (AP), Genic Algorithm (GA), Particle Swarm optimization Ant Colony optimization (PSOACO).