Reviewerly/exHarmony huggingface dataset:
Knowledge Transfer between Similar Atari Games Using Deep Q-Networks to Improve Performance

Humans have excellent real-world generalization power. It is a characteristic of high-level intelligence capable of finding general patterns between different tasks and transferring the concept learned from one task to another. While transfer learning is fairly popular in supervised learning tasks, its application in reinforcement learning has not been explored extensively. In this paper, we determine if knowledge transfer between similar Atari game pairs is feasible to gain decent learning performance improvement and reduction in training time and computational resources to speed up agent creation for new similar games. We make use of the Deep Q-Learning (DQN) algorithm to train our game pairs using the OpenAI gym environment and perform different strategies to enable transfer learning. We evaluate whether the agent is able to transfer knowledge gained from training it on one game, to a similar game effectively.
Technical evaluation of ultrafiltration unit in Siwalanpaji’s water treatment plant, PDAM Sidoarjo

Abstract Raw water with high turbidity requires pretreatment on its ultrafiltration unit to prevent fouling. Based on that, it is necessary to evaluate the ultrafiltration unit’s quality, quantity, and operation in Siwalanpanji’s WTP. The evaluation method compares the design criteria, technical specification, and ultrafiltration with the conventional to see the existing condition. The results of ultrafiltration turbidity and organic substance efficiency removal are about 53% and 18%, it’s not more good than conventional unit efficiency removal, which has 89% for turbidities removal and 37% for organic substance removal. Moreover, the quality of the production water fulfils the standards of Indonesia Health Minister No. 492 of 2010, except for the value of organic substances, which is in this analysis it around 13.69 mg/L meanwhile, in standard, it must not be more than 10 mg/L. Evaluation of the operating performance of each treatment unit related to the membrane unit at the WTP in Siwalanpanji has fulfilled the criteria design. However, using an ultrafiltration unit results in treated water quality is equivalent to a conventional treatment unit. So from this evaluation, it is found that there is a need for recommendations pretreatment, which is better for raw water to treat before ultrafiltration.
Time-optimal Planning for the Quadrotor Transportation System Landing a Payload on the Mobile Platform

Nowadays, aerial transportation has been widely used in various aspects, such as fire fighting, life-rescue, emergency-supplies delivery, and etc. Existing works for quadrotor transportation systems concentrate on the objectives of quadrotor positioning and payload swing suppression. Nevertheless, the process to land the payload onto a mobile platform is seldom discussed. In this paper, a time-optimal trajectory generation algorithm for unmanned quadrotor transportation system is proposed with effective quadrotor positioning, payload antiswing and landing performance. Specifically, the system model is discretized and various constraints are included to guarantee safe transportation, based on which, a nonlinear programming problem is constructed and solved. Finally, a series of simulation tests are implemented to verify the effectiveness of the proposed trajectory generation method.
Monitoring and evaluation procedure with LMS Moodle

The purpose of the article is to develop recommendations for results processing in Moodle electronic system. Methodology: the authors presented formulas for calculating the reliability of the tests used to verify students' training, the description of what is required for monitoring, the actions of the teacher to perform a quality assessment system. The process of calculating the final grade for the course is presented, which will provide the necessary conditions for conducting student knowledge control. Results: in the process of study, it was concluded that modern methods of processing the data used during the control measures in higher education institutions within the framework of the Moodle system contribute to improving the graduates training.
Lightweight Real-Time Mask Wearing Detection Algorithm Based on YOLOv3

The novel coronavirus has broken out from 2019 and quickly become a global pandemic. It spreads rapidly from person to person through droplets, aerosols and other carriers. In order to prevent the spread of the virus, people must wear masks when entering and leaving public places and taking public transport to reduce the risk of virus transmission. How to detect the wearing of masks in public places and other natural environments has become a new research problem. This paper proposes a lightweight deep neural network (E-YOLO) to realize mask wearing detection in real-time scenarios. E-YOLO improves the general target detection YOLOv3 algorithm by the follows methods. Firstly, the Efficient-Net series B2 backbone feature extraction network is used to replace the original Darknet53 feature extraction network. Combined with the spatial pyramid pooling module and the bidirectional feature pyramid structure to enrich the semantic information of the feature layer, so as to achieve the balance between the speed and accuracy of target detection in real-time scenes. The experimental results show that, compared with the YOLOv3 algorithm, the E-YOLO algorithm possesses the same accuracy and speed. The network floating point calculation amount is one twelfth of YOLOv3, and the model size is only one quarter of YOLOv3, which is more suitable for resource-constrained platforms.
Real-Time Obstacle Avoidance Algorithm for Dynamic Environment on Probabilistic Road Map

This paper deals with the motion planning problem for a robot operating in presence of dynamic obstacles. In our approach, we used the Probabilistic Road Map technique for sampling the configuration space in offline mode followed by finding an optimal path with the help of A-star. Whenever a dynamic obstacle appears in the path of the robot, we locally resample the map for obstacle avoidance. The algorithm samples the free configuration space in the vicinity of the obstacles but, with a fewer number of samples, then again uses an $A^{\ast} $ based algorithm to find the path around the dynamic obstacles and finally superimpose the two paths. Motion planning in presence of dynamic obstacles can be memory and computation-intensive. Therefore, classical motion planning techniques fail to perform reliably when implemented in online mode. The approach we propose here is memory efficient and versatile. The algorithm can be efficient from the viewpoint of computation time but, can also produce an optimal path depending upon the number of nodes used in the re-planning phase and tuning of certain parameters. The algorithm is capable of handling both polygonal and circular obstacles. We demonstrate these capabilities with the help of simulations.
Handwriting Imitation with Generative Adversarial Networks

Handwriting imitation is a challenging and interesting deep learning topic. This paper proposed a method to imitate handwriting style by style transfer. We proposed an neural network model based on conditional generative adversarial networks (cGAN) for handwriting style transfer. This paper improved the loss function on the basis of the GAN. Compared with other handwriting imitation methods, the handwriting style transfer's effect and efficiency have been significantly improved. The experiments showed that the shape of the generated Chinese characters is clear and the analysis of experimental data showed the Generative adversarial networks showed excellent performance in handwriting style transfer. The generated text image is closer to the real handwriting and achieved a better performance in term of handwriting imitation.
Development of Text Classification Based on Difficulty Level in Adaptive Learning System using Convolutional Neural Network

Text classification is advantageous and necessary in a variety of fields. The adaptive learning system is one of the sectors that requires text classification. Adaptive learning is a type of online learning that makes learning recommendations based on the needs of the students. The learning module and the testing module are the two aspects of adaptive learning. The purpose of the learning module is to give recommendations of learning materials. The testing module, on the other hand, is used to assess and test the student’s progress. The Learning Module requires text classification based on difficulty level to ensure that students have access to learning resources that are appropriate for their ability level. The dataset used in this study is the ebook of Science for junior high school, and we also crawling from RuangGuru (one of the E-learning platforms in Indonesia). The method that used in this study is CNN that combines with word embedding techniques. The dimension of Glove and Word2Vec are 100 dimensions. CNN-Word2Vec performs better than CNN-Glove with 96% accuracy and 61.20 seconds of computing time. Based on this, CNN-Word2Vec is appropriately applied to Adaptive learning that requires accuracy and fast computing time.
Is One Teacher Model Enough to Transfer Knowledge to a Student Model?

Nowadays, the transfer learning technique can be successfully applied in the deep learning field through techniques that fine-tune the CNN’s starting point so it may learn over a huge dataset such as ImageNet and continue to learn on a fixed dataset to achieve better performance. In this paper, we designed a transfer learning methodology that combines the learned features of different teachers to a student network in an end-to-end model, improving the performance of the student network in classification tasks over different datasets. In addition to this, we tried to answer the following questions which are in any case directly related to the transfer learning problem addressed here. Is it possible to improve the performance of a small neural network by using the knowledge gained from a more powerful neural network? Can a deep neural network outperform the teacher using transfer learning? Experimental results suggest that neural networks can transfer their learning to student networks using our proposed architecture, designed to bring to light a new interesting approach for transfer learning techniques. Finally, we provide details of the code and the experimental settings.
Performance Analysis of MDPC and RS codes in Two-channel THz Communication Systems

We analyze whether a multidimensional parity check (MDPC) or a Reed-Solomon (RS) code in combination with an auxiliary channel can improve the throughput and extend the THz transmission distance. While channel quality is addressed by various coding approaches, and an effective THz system configuration is enabled by other approaches with additional channels, their combination is new with the potential for significant improvements in quality of the data transmission. Our specific solution is designed to correct data bits at the physical layer by using a low complexity erasure code (MDPC or RS), whereby original and parity data are transferred over two separate and parallel THz channels, including one main channel and one additional channel. The results are theoretically analyzed to see that our new solution can improve throughput, support higher modulation levels and transfer data over the longer distances with THz communications.
The Effect of AIDA Model to Purchasing Decision: The Case of HappyFresh Instagram Promotion

The high use of internet especially social media has made this virtual world as a marketing tool. Therefore, this study aims: to determine the effect of Instagram-social media promotion on purchasing decisions, the level of AIDA (Attention, Interest, Desire and Action) among consumers; its influence on purchasing decisions; and to determine the suitable AIDA model for fresh products. The research population is Instagram followers @HappyFresh_id. The sample is 100 people based on five-percent-error-probability of Slovin formula, and it is derived by a simple random sampling technique. The data collected during March-April 2021, and adopting multiple linear regression analysis, proves AIDA simultaneously influences purchasing decisions with a determination level of 67%. Likewise, the interest, desire, and action have a significant effect on buying decisions. Accordingly, the research presents three variations of AIDA model for effective social media marketing, but it needs further analysis
Development of a Web-Based Information System to Improve Health Services for Pets

Information system technology is no stranger to hearing in the digital era. The development of information system technology will affect the development of information technology in various aspects of human life. One important aspect of human life is health services for pets. Information technology that is touched is in the form of information management needed by veterinarians to improve the effectiveness of services for pets. This research uses a case study approach. In this study, it was found that the pet health service information system "PetHub.com" which was developed can improve health services for pets. In addition, "PetHub.com" also provides a chat service feature with general practitioners and specialists. The process of developing this information system technology includes the selection of doctors as partners in providing health information, various choices of health information content, socialization of "PetHub.com" health information services, especially among adolescents and adults by providing directions for all provisions when accessing health information on "PetHub.com".
Collaborative activity recognition with heterogeneous activity sets and privacy preferences

One of the major challenges in Human Activity Recognition (HAR) based on machine learning is the scarcity of labeled data. Indeed, collecting a sufficient amount of training data to build a reliable recognition problem is often prohibitive. Among the many solutions in the literature to mitigate this issue, collaborative learning is emerging as a promising direction to distribute the annotation burden over multiple users that cooperate to build a shared recognition model. One of the major issues of existing methods is that they assume a static activity model with a fixed set of target activities. In this paper, we propose a novel approach that is based on Growing When Required (GWR) neural networks. A GWR network continuously adapts itself according to the input training data, and hence it is particularly suited when the users share heterogeneous sets of activities. Like in federated learning, for the sake of privacy preservation, each user contributes to the global activity classifier by sharing personal model parameters, and not by directly sharing data. In order to further mitigate privacy threats, we implement a strategy to avoid releasing model parameters that may indirectly reveal information about activities that the user specifically marked as private. Our results on two well-known publicly available datasets show the effectiveness and the flexibility of our approach.
Embedded Attention Network for Semantic Segmentation

Semantic segmentation, as a fundamental task in computer vision, is capable of providing perception ability in many robot applications, such as automatic navigation. To enhance the segmentation accuracy, self-attention mechanism is adopted as a key technique for capturing the long-range dependency and enlarging the receptive fields. However, it requires high computation complexity and GPU memory. In this letter, we propose an Embedded Attention Network to relieve the undesired computational cost. Specifically, we introduce an Embedded Attention (EA) block to improve the segmentation performance and efficiency. Firstly, EA block generates a group of compact while coarse feature bases with the capability of reducing large amount of computation cost. Then an embedded attention is employed to collect the global contextual information and update the representation of the coarse bases from a global view. Finally, the updated bases are leveraged to estimate the attention similarity. We take the well-estimated feature bases to perform feature aggregation. Our approach achieves a considerable computation cost reduction, which suggests it is more powerful than other counterparts in most robot platforms. We conduct extensive experiments on two benchmark semantic segmentation datasets, i.e., CityScapes and ADE20 K. The results demonstrate that the proposed Embedded Attention network delivers comparable performance with high efficiency.
Double iterative learning-based polynomial based-RBFNNs driven by the aid of support vector-based kernel fuzzy clustering and least absolute shrinkage deviations

Recently, the polynomial-based radial basis function neural networks (P-RBFNNs) have been successfully applied to regression tasks. However, the redundant and non-linear partitioned data easily interfere with accurate partitioning of clusters completed in P-RBFNNs, affecting the regression performance of this existing model. Because the squared error is used as the cost function of the learning method, P-RBFNNs are sensitive to noise interference. In order to cope with these problems, this study introduces a double iterative learning-based polynomial based-RBFNNs (DP-RBFNNs) modeling that focuses on the formation of architectures to improve the accuracy of regression performance as well as enhance the robustness through double iterative learning as follows: a) support vector-based Gaussian kernel fuzzy c-means (SV-GKFCM) as a kind of the support vector-based kernel fuzzy clustering are designed to determine connections (weights) between the input and hidden layers of the proposed model. SV-GKFCM helps effectively reduce the number of redundant data to re-modify the partitioning of clusters in the DP-RBFNNs. In addition, the cluster centers can be accurately updated from the non-linear partitioned data with the aid of Gaussian kernel distance in SV-GKFCM; b) least absolute shrinkage deviations (LASD) as a robust estimation are designed to update connection weights between the hidden and output layers. The SAE (sum of absolute error) function in the LASD method is used as a cost function to reduce the noise interference in the procedure of weight estimation as well as enhance the robustness of the DP-RBFNNs. The superiority of the proposed model is demonstrated through the experimental results.
Chinese Short Text Classification Based on Multi-level Semantic Feature Extraction

Text classification, aiming to predict the category of a given text based on it's semantic information, is a fundamental part of natural language procession applications such as spam detection and user intention classification. Existing Chinese short text classification models mostly use neural network methods to extract text classification features, but still, there are problems of insufficient feature extraction and poor classification results. This paper constructs a Chinese short text classification model that incorporates multi-level semantic features. The model first uses Convolutional Neural Network(CNN) and Bidirectional Gated Recurrent Unit (BiGRU) to extract character and word features of texts; secondly, it builds a multi-level semantic extraction network to produce multi-level semantic representation by capturing local features and context features of texts, screening them, and fusing them with the character and word feature; finally, classify texts with a Softmax classifier. The experimental results on THUCNews are inclined to show that our model’s performance is further improved compared with existing models, and the classification accuracy reaches 93.59%.
PRNG-Broker: A High-Performance Broker to Supply Parallel Streams of Pseudorandom Numbers for Large-Scale Simulations

The generation of large streams of pseudorandom numbers may lead to performance degradation in simulation applications. Both the PRN generator and how it is used impact the efficiency of generating multiple PRNs. A PRNG-Broker was developed for parallel servers with/without accelerators, which transparently manages the efficient execution of PRNG implementations from CPU/GPU libraries, with an intuitive API that replaces the user PRNGs requests. PRNG-Broker allows the development of efficient PRN-intensive applications without the need of explicit parallelization and optimization of PRNG tasks. It was validated with scientific analyses of proton beam collisions from CERN, which require 30 Ki PRNs per collision. Outcomes show a performance boost over the original code: 48x speedup on a 2*12-core server and over 70x speedup when using a GPU.
MXDAG

Emerging distributed applications, such as microservices, machine learning, big data analysis, consist of both compute and network tasks. DAG-based abstraction primarily targets compute tasks and has no explicit network-level scheduling. In contrast, Coflow abstraction collectively schedules network flows among compute tasks but lacks the end-to-end view of the application DAG. Because of the dependencies and interactions between these two types of tasks, it is sub-optimal to only consider one of them. We argue that co-scheduling of both compute and network tasks can help applications towards the globally optimal end-to-end performance. However, none of the existing abstractions can provide fine-grained information for co-scheduling. We propose MXDAG, an abstraction to treat both compute and network tasks explicitly. It can capture the dependencies and interactions of both compute and network tasks leading to improved application performance.
Chapter 12. Using Twitter to build a corpus for linguistic variation

Data for linguistic analysis from social media are being used for a variety of reasons. One of the most attractive reasons is the possibility of gathering very large quantities of data in the digital form. However, collecting data from these sources is not always a simple task. In this chapter, a few methods to harvest data from Twitter will be introduced like FireAnt, 140dev Streaming API Framework, and Elastic Stack, as well as visualization tools like QGIS to map geotagged tweets. Finally, some examples of tweet analyses are discussed.
Cross-layer knowledge distillation with KL divergence and offline ensemble for compressing deep neural network

Deep neural networks (DNN) have solved many tasks, including image classification, object detection, and semantic segmentation. However, when there are huge parameters and high level of computation associated with a DNN model, it becomes difficult to deploy on mobile devices. To address this difficulty, we propose an efficient compression method that can be split into three parts. First, we propose a cross-layer matrix to extract more features from the teacher's model. Second, we adopt Kullback Leibler (KL) Divergence in an offline environment to make the student model find a wider robust minimum. Finally, we propose the offline ensemble pre-trained teachers to teach a student model. To address dimension mismatch between teacher and student models, we adopt a computation rate.