Reviewerly/exHarmony huggingface dataset:
Effect of Different Block Sizes on the Quality of JPEG-like Compressed Image

Discrete Cosine Transform (DCT) is a common method in image compression processing. In this method, the original image is first divided into several blocks for processing, and then the information of each pixel is converted to frequency coefficients, and a certain high frequency component is discarded, thus the image is lossy compressed. In this paper, the effect of block sizes on the quality of decompressed image is studied, so as to find out the optimal block sizes. Firstly, we make a theoretical analysis, and then verify my analysis results through experiment. In the experiment, the block sizes range from 1x1 to 512x512. We use different metrics to evaluate the image quality, including MSE, PSNR and SSIM.
Online Reinforcement Learning Control of an Electromagnetic Manipulator

Machine Learning Control is a control paradigm that applies Artificial Intelligence methods to control problems. Within this domain, the field of Reinforcement Learning (RL) is particularly promising, since it provides a framework in which a control policy does not have to be programmed explicitly, but can be learned by an intelligent controller directly from real-world data, allowing to control systems that are either arduous or even impossible to model analytically. However, in spite of such considerable potential, the RL paradigm poses a number of challenges that effectively hinder its applications in the real-world and in industry. It is therefore critical that research in this field is advanced until RL-based controllers can be practically demonstrated to be real-world feasible and reliable. This thesis report presents the attempts made at applying control strategies based on Reinforcement Learning to solve a precise positioning task with a physical experimental setup. The setup at hand is a magnetic manipulator (magman) characterized by a high degree of nonlinearity. The controller uses the spatially continuous magnetic field generated by four actuators to displace a steel ball, constrained to move in one dimension, towards a reference position. Two different implementations of the Q-learning algorithm (Sutton, Barto, et al., 1998) were deployed. In spite of the good results obtained in a simplified simulated environment, both implementations failed on the experimental setup. The negative outcome of these experiments is mainly due to the fact that, since the task at hand is an accurate positioning task, the reward obtained by the learner while interacting with the environment is too sparse for it to be able to learn a stabilizing control policy. Other factors have presumably contributed to the controllers’ failure, such as the circumstance that the agent does not have access to the full system state information and a sub-optimal tuning of the algorithms’ hyper-parameters. Besides model-free RL, the Value Iteration model-based method was successfully applied both in simulations and with the experimental setup. The present findings suggest that, in order to solve the magman task with model-free RL, more sophisticated algorithms need to be deployed, such as for example an agent that can naturally deal with continuous state and action spaces, as the DDPG algorithm (Lillicrap et al., 2015), with exploration carried out in the parameter-space rather than in the control action space (Plappert et al., 2017), in addition to a more optimal exploitation of the information extracted from the environment, for example using Hindsight Experience Replay (Andrychowicz et al., 2017).
Bezalel - Towards low-cost pin-based shape displays

The usage of shape changing interfaces is widely discussed in the HCI field as a promising strategy for the physical representation of digital data. Such interfaces are expected to greatly impact a wide range of applications, such as virtual reality, architectural design and education of blind people. Unfortunately, the widespread usage of pin-based shape displays is currently limited by their typically high manufacturing costs. The high costs of pin-based shape displays are mainly due to the number of actuators, which tends to grow quadratically with display resolution. Given that, we present Bezalel: a solution for pin-based shape displays that allows 2 actuators to efficiently actuate pin-based shape displays with n pins. Our solution is able to fully render any 2.5D shape within a time proportional to , which outperforms the 2014 Lemelson-MIT ”Use it!” graduate winner solution using half as much actuators. Additionally, results also show that, for specific shapes, our approach can perform as well as the most efficient and much more expensive technologies currently used. We expect that our solution will make it possible to create low-cost actuated surfaces with different sizes, from small tactile objects to large structures such as shape-changing floors.
Video Monitoring of Tourist Density in Cultural Heritage Sites

Cultural heritage sites are of great significance to human and they are the important treasures of mankind. They convey the culture of each nation and country. Therefore, protecting their safety and carrying on cultural inheritance has farreaching significance. At the present time, as the universality of surveillance systems continues to increase, almost all cultural heritage sites are equipped with surveillance cameras. These surveillance cameras allow the administrator to view a display screen at a specific place for remote monitoring. However, currently, in most of cultural heritage sites, the videos captured by these surveillance cameras are monitored artificially and considering the fatigue of people, this method is less efficient. In recent years, with the powerful support of big data and GPU, artificial intelligence technology with deep learning as the core has developed rapidly and has been widely used in many fields. Therefore, this paper proposes to apply the computer vision based on convolutional neural network (CNN) to monitor the number of tourists in an exhibition hall of the Forbidden City by analyzing the surveillance video and then calculate the tourist density in this exhibition hall. After applying the method to every open area in the Forbidden City, by comparing the distribution of the tourist density in each open area, the tourists can better plan their visiting routes and the managers can schedule personnel more easily. The deep learning algorithm used in this paper is You Only Look Once (YOLO) v3. One of the advantages of YOLO v3 is that it has a fast computing speed and the requirements for running platforms is relatively low, which can even be run on mobile terminals. And its accuracy is sufficient for human recognition
Performance Optimization of a Service in Virtual and Non - Virtual Environment

In recent times Cloud Computing has become an accessible technology which makes it possible to provide online services to end user by the network of remote servers. With the increase in remote serv ...
Usability testing to improve search results

Goals of testing: To test the usefulness of the search box layout used on the homepage. To test the effectiveness of the layout and results options on the search results page.
Dense-ACSSD for End-to-end Traffic Scenes Recognition.

Traffic scenes recognition is the cornerstone of autonomous driving. However, most of the current algorithms are individually trained for tasks such as object detection and road segmentation. In addition, the training data used are mainly concentrated in small datasets such as KITTI, and the trained models are highly susceptible to weather, lighting and other factors. In order to solve the above problems, we propose an end-to-end CNN model for drivable area segmentation and multiple object detection. The feature extraction part of the network is powerful DenseNet. The atrous convolution and spatial pyramid pooling are used for road segmentation, and single shot detection is used for multiple object detection. According to its characteristics, we named the network Dense-ACSSD. Dense-ACSSD is trained on the current largest autonomous driving dataset, called BDD100K. The final training results show that the mIOU of the drivable area segmentation part is as high as 84.15%, and the mAP of the multiple object detection part reaches 30.82%. In addition, the inference time of Dense-ACSSD can meet real-time requirements.
Analysis and implementation of multiagent deep reinforcement learning algorithms for natural disaster monitoring with swarms of drones

Natural disasters are the cause of a great amount of deaths and economic loss everyyear. The rapid and efficient deployment of mitigation and relief work is of great value butcan be hampered by the lack of information in real time about the situation. Technologicalprogress has allowed for a gradual improvement of this process of information retrieval.Automatic detection of wildfires and floods from images is now a reality. However, thereal-time update of natural disaster maps still requires the physical presence of sensors.A natural disaster surveillance strategy that is gaining traction in recent years usesswarms of drones to acquire images of the environment. The cost reduction of this architecturedepends on the automation of swarm cooperative navigation.The goal of this Master’s thesis is to build upon the recent advancements in the use ofDeep Reinforcement Learning techniques for swarm navigation to improve them and applythem to a wider variety of situations.In particular, we have developed a platform for the simulation and training of swarms ofdrones and a series of multiagent algorithms. Lastly, we have trained deep neural networkswith and without memory in simulations of both wildfires and floods, following severaldifferent strategies with varying levels of training decentralization, and we have studied thepotential benefits of each approach.The final practical outcome is a series of trained neural networks which can be loadedonboard the drones of the swarm to control their navigation in a decentralized manner.
Contextualized sparse representation with rectified n-gram attention for open-domain question answering

Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models. In this paper, we aim to improve the quality of each phrase embedding by augmenting it with a contextualized sparse representation (Sparc). Unlike previous sparse vectors that are term-frequency-based (e.g., tf-idf) or directly learned (only few thousand dimensions), we leverage rectified self-attention to indirectly learn sparse vectors in n-gram vocabulary space. By augmenting the previous phrase retrieval model (Seo et al., 2019) with Sparc, we show 4%+ improvement in CuratedTREC and SQuAD-Open. Our CuratedTREC score is even better than the best known retrieve & read model with at least 45x faster inference speed.
The Influence of Visual 3D Media and Graphics Media for Design Machine Models

Quality education requires qualified and capable educators to play a professional role in school and community settings. In line with this, the teacher's competence demands also adjust to these developments. Teachers are not only able to deliver the subject matter, but the teacher must also be able to teach children. The purpose of this study is to prove that there is a significant difference between the learning that uses three dimensional visual media with learning using graphic media during the learning process takes place. Research method used in this research is experiment, because researcher try to see difference or influence happened to research subject. The learning process will be effective if supported by adequate means used to facilitate the learning process in school. With the supporting facilities in learning can lead to interaction between teachers with learners where there is mutual in the learning process. One of the means referred to as learning support is learning media. By using appropriate and appropriate media can improve learners' learning outcomes. Media can attract the attention of learners in learning, even can make it easier for learners to understand the material delivered.
The Correspondence between V. I. Malyshev and M. N. Tikhomirov about the Preservation of Manuscripts

The article analyzes the correspondence between two outstanding archaeographers of the 20th century – academician M. N. Tikhomirov and founder of the Archive of Ancient Relics (Institute of Russian Literature (the Pushkin House), Russian Academy of Sciences) V. I. Malyshev. The paper aims to research, how they understood and discussed the problem of preservation of manuscripts. The authors studied 182 letters from Malyshev to Tikhomirov (for 1947–1965) stored at the private fund of M. N. Tihomirovand 66 letters from Tikhomirov to Malyshev (for the same period) as well as the memoirs and articles of Tikhomirov and Malyshev themselves, their students and followers. Comparison of the sources revealed their understanding of the essence of the problem and possible ways of its solution. The authors highlighted the general and particular in the views of two correspondents on the issue of the preservation of manuscripts. According to Malyshev, at that time the most urgent task was a collection of the manuscripts kept by ordinary people. Tikhomirov, recognizing the importance of the above-mentioned mission, insisted that maintenance of archives was just as crucial. At the same time both of them agreed on the need to organize the archaeographic expeditions, examine and registrate private collections, monitor preservation of materials in state archives, publish manuscripts, involve government resources and stimulate public attention to the problem. The letters show the mutual respect of the correspondents, their understanding of the merits of each other as well as demonstrate their successful cooperation. Together Malyshev and Tikhomirow completed reconstruction of the Archaeographic Commission, effectively promoted their common cause in the press and impressively replenished the Tikhomirov’s collection of manuscripts and old printed books (later accurately transferred to Novosibirsk). The analysis of the above-mentioned sources leads to the following conclusions: even today the experience of Soviet scholars is still relevant.
RANCANG BANGUN SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN MAKANAN SEHAT BAGI BALITA BERBASIS ANDROID

Food and health are the main things in life. Without eating humans can not survive. And without human health also will not live in peace. Food is the source of life, but food can also cause health disruption. Then as a living creature healthy food selection is needed in order to live healthy. Choosing healthy foods for toddlers on a regular basis can be quite confusing. Lack of knowledge of parents, especially parents who have new toddlers about healthy food for toddlers can cause disturbance of illness and development of children under five does not work well. Therefore the selection of healthy foods for toddlers is needed. The purpose of this research is to build a decision support system is a system used to solve problems effectively and efficiently. With this application, is expected to help parents, especially parents who have new toddlers in the selection of healthy menu for toddlers quickly and accurately by providing an alternative decision-making is good. So that digestion and growth for the toddler can run well. Keywords : Decision Support System, Food, Nutrition, Toddler, Android
Better Word Representations with Word Weight

As a fundamental task of natural language processing, text classification has been widely used in various applications such as sentiment analysis and spam detection. In recent years, the continuous-valued word embedding learned by neural network attaches extensive attentions. Although word embedding achieves impressive results in capturing similarities and regularities between words, it fails to highlight important words for identifying text category. Such deficiency could be attenuated by word weight, which conveys word contribution in text categorization. Toward this end, we propose an effective text classification scheme by incorporating word weight into word embedding in this paper. Specifically, in order to enrich word representation, the bidirectional gated recurrent units (Bi-GRU) is first employed to grasp context information of words. Then the word weights yielded by term frequency (TF) are used to modulate the word representation of Bi-GRU for constructing text representation. Extensive experimental results on several large text datasets verify that the accuracy of our proposed text classification scheme outperforms the state-of-the-art ones.
Research on the Application of University Management Accounting Based on Internet + Environment

With the strong support of the government, “Internet +” has entered a path of rapid development, and gradually penetrated into various industries of society, bringing changes in development methods to different fields. In the report of the 19th National Congress of the Communist Party of China, the requirements for comprehensively deepening the reform of the public financial management system were clearly put forward, and the financial department of the university, as the core institution of the school's strategic decision-making, should shift the focus of accounting work from the traditional service function under the new reform and development situation. For the management function, the school's financial management level has been effectively improved, and the effectiveness of various tasks has been ensured through the introduction of “Internet +”. Based on the Internet + environment, the article deeply explores and analyzes the current challenges faced by university management accounting, and aims to develop a series of improvement measures. In the process of accounting management system, system and talent innovation, the level and quality of accounting management in colleges and universities are improved.
내러티브 역량 지향 다양성 교육에 관한 담론과 함의

The purpose of this article is to take a look at the discourse on diversity education, which tries to develop and expand narrative competence, and its implications. To achieve the goal, this paper deals with following themes: theoretical basis for diversity education, diversity and narrative competence, and the relationships of narrative competence, diversity education and global citizenship education. The result of this inquiry can be formulated as follows: First, in light of narrative diversity, diversity education should search for courses of action to develop narrative competence of learner. Second, globalization and individualization, universalization and digitalization of mass media, migration and cultural pluralization are the preconditions for and also the results of increasing diversity. Third, in considering the goals and tasks of diversity education, we need to pay attention also to the narratives taking a sceptic position toward diversity or not recognizing the values of diversity. Fourth, considering the increase of difference and diversity, schooling needs to reflect diverse narratives, multiple perspectives, diverse interpretations in its curriculums and activities. Fifth, task of education lies in searching for shared memories beyond divided memories, and, in so doing, we can expect to develop desirable narrative competence in its authentic sense. Sixth, if we succeed in developing narrative competence by considering and treating appropriately narrative diversity in diversity education, then we can also expect to contribute to global citizenship education.
Development of Methods of Semantic Search on the Internet, Based on Treelike Linguistic Templates

The article is devoted to development of methods of search of information on the Internet. Currently the issue of fast and automatic data extraction from different sources is very actual. The aim of this work is creation of instrument to solve this problem. The idea of the approach is to develop semi-automatic methods of linguistic templates construction for given fragment of atomic diagram. Algorithm of templates construction for the fragment of atomic diagram is performed in 2 stages. On the 1st stage generation of the simple templates is done, on the 2nd stage more seldom and complicated templates are found, using templates from 1st part of algorithm. Processing texts in natural language on the Internet using gotten templates, needed information may be found.
A Machine Learning Based Prediction Method of Collection Time Level for Free Visit and Pickup Service of End-of-Life Consumer Electronics

The Free Visit and Pickup Service collection drivers have been getting paid the national standard price for collecting End-of-Life (EOL) Consumer Electronics (CE) which is causing a deviation in wages and labor. In this study, 18 different factors affecting the collection of EOL CE was selected as the independent variable. The experiment was conducted to predict the collection time levels classified into five categories using machine learning models. In order to improve the performance levels of the models, variables were selected using genetic algorithm. As a result, 7 of 18 independent variables were selected for the study. The machine learning models used were Decision Tree, Random Forest, Gradient Boosting, Support Vector Classification, and Multi-Layer Perception, while the Hyper parameter was set based on the Grid search. The evaluation index used the F1 Score due to the data imbalance and the results identified the MLP model to have the highest F1 Score of 0.651.
Developing Classifier Model for Hand Gesture Recognition for Application of Human Computer Interaction (HCI)

The hand gesture technique that is regarded as the natural and easy method for the human-machine interaction, has paved way for the development of the multitudes of applications. The hand gestures basically employed in most of the application are either sensor based or the vision based. In case of verbal communication the gesture depiction involves the application of the natural and the bare hand gestures. So the paper proposes a bare hand gesture recognition with the light in variance conditions, involving the image cropping algorithm in the preprocessing, considering only the region of interest. The mapping of the image oriented histogram is primarily done utilizing the Euclidean distance method and further supervised neural network are trained using the images mapped, to have a better recognition of images with the same gestures under different light intensities.
Estimating the Gradient for Images with Missing Samples Using Elliptical Structuring Elements

Estimating the Gradient for Images with Missing Samples Using Elliptical Structuring Elements