Reviewerly/exHarmony huggingface dataset:
Local Feature Descriptors with Deep Hypersphere Learning

Recent works have demonstrated the power of L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> normalization in local feature descriptor learning. While the descriptors are typically learned in the Euclidean space, the similarity between descriptors is often evaluated on a unit hypersphere due to the post-processing of L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> normalization for descriptors, which creates a gap between the training stage and the usage stage of feature descriptors. To bridge the gap, we propose a hyperspherical descriptor learning model, where the whole network is projected onto the hyperspherical space. In addition, a squared angular triplet loss is designed to enable the proposed hyperspherical model to learn angularly discriminative descriptors. Experiments on UBC dataset show that the proposed hyperspherical descriptor outperforms its Euclidean counterparts and the state-of-the-art methods on the feature matching task.
3D-Domotic: A 3D Mobile Application for Domotic Control

Domotics integrates a set of systems for home or office automation in order to improve comfort, security, energy savings, communication facilities and entertainment possibilities for the user. In turn, Domotics is closely related to the most recent concept of the Internet of Things, which proposes the interconnection of everyday objects in a home or office through the Internet. In this work, a 3D mobile application called 3D-Domotic is presented. This application allows to recreating the three-dimensional structure of a house and remotely control the electronic devices in it. These devices are located in their actual physical location, and are controlled directly on the model of the house, in a simple and completely visual way. Device status is monitored in real time, simplifying the control of the house.
A large quantity of steel balls automatic precise counting device based on dual sensor auto-correcting method

A large quantity of Steel Balls Automatic precise Counting device is introduced in this paper. Inductance sensors are used for ball detection and STC15F2K60S2 microprocessor is used to do the signal processing. When steel balls are sent to the PVC pipe at the same time, some balls will connect together because of the collision. The sensor will send a connected signal instead of multiple signals if it meets the connected balls. This could cause counting mistake of the device. A correction method is used to solve this problem. The device setup angle could be gotten by using dual sensors to get the average moving speed of the steel balls according to the fixed relations between the setup angle and the speed, which is not affected by whether the steel balls overlap or not because it used two voltage Jump edge to judge the start and end moment of timer. After that, the device setup angle is used to get the standard moving time to single sensor according to the known function relationship. The counting numbers are corrected by using the standard duration to compare with each recording low voltage duration passing single sensor. This method solves the counting mistake problem when a lot of steel balls are pushed into the PVC pipe together. The device improves the efficiency and accuracy of the ball counting.
Desingning An E-Voting Information System For Website-Based Village Head Elections 

Voting in a democratic country is an important part of the means of choosing leaders. The village head election process in Indonesia still uses conventional voting methods, namely using ballot paper media in the election process. Voting that is carried out conventionally has several obstacles, including the lack of guaranteeing the authenticity of voters' votes, so that people think the results of voting results are often manipulated. In addition conventional selection is deemed inaccurate and time-consuming and costly. In this study the aim of this research is to design an information system e-voting that can be used for the Election of the Village Head of Cemandi, Sedati, Sidoarjo, East Java, where by using this system the election process becomes easier by ensuring the accuracy of the vote count. This system development method uses the model of software engineering waterfall. The test results blackbox show that the functions of the features in the system are running well. Based on the UAT test, the system received an average percentage rate of 86%.
Research on the Application of Computer-assisted Task-based Teaching Method in English Teaching

Abstract Computer based Task-based English teaching method mainly refers to the use of computers in English teaching activities to set relevant teaching tasks, so that students can achieve language knowledge acquisition in the whole process of task completion. Students’ communicative competence is increasingly enhanced. The specific teaching task is regarded as the learning motivation, and the task process is regarded as the English learning process. The ultimate teaching achievement can be reflected by the way of displaying the task achievements and teaching achievements. The use of computer-based task-based teaching method greatly mobilize the enthusiasm of students to learn English, activate the atmosphere of classroom teaching. Although the traditional five step teaching method is easy to operate and evaluate the teaching results, the classroom effect is rigid and rigid, which limits the students’ initiative learning enthusiasm and makes students lack of creativity and independent inquiry ability in learning [1].
Parallel computing of fuzzy integrals: Performance and test

Fuzzy integral in data mining is an excellent information fusion tool. It has obvious advantages in solving the combination of features and has more successful applications in classification problems. However, with the increase of the number of features, the time complexity and space complexity of fuzzy integral will also increase exponentially. This problem limits the development of fuzzy integral. This article proposes a high-efficiency fuzzy integral—Parallel and Sparse Frame Based Fuzzy Integral (PSFI) for reducing time complexity and space complexity in the calculation of fuzzy integrals, which is based on the distributed parallel computing framework-Spark combined with the concept of sparse storage. Aiming at the efficiency problem of the Python language, Cython programming technology is introduced in the meanwhile. Our algorithm is packaged into an algorithm library to realize a more efficient PSFI. The experiments verified the impact of the number of parallel nodes on the performance of the algorithm, test the performance of PSFI in classification, and apply PSFI on regression problems and imbalanced big data classification. The results have shown that PSFI reduces the variable storage space requirements of datasets with aplenty of features by thousands of times with the increase of computing resources. Furthermore, it is proved that PSFI has higher prediction accuracy than the classic fuzzy integral running on a single processor.
Is Online Theatre Really Theatre?: Teaching and Researching During a Pandemic

At the beginning of the 2020 pandemic, I grew concerned and also curious about how theatre educators would transition theatre and drama instruction to an online platform. I began a research study interviewing theatre artists while they shifted their formerly in-person summer camps to virtual spaces. One of those instructors, Flannery, was initially hesitant to accept online learning, but through her experiences teaching camps and our conversations, she came to embrace Zoom, an online platform, for its strengths. Through this exploration, I, too, grew more confident that online platforms during this time could be an effective teaching, learning, and performance tool for theatre.
Study Marbles: A Wearable Light for Online Collaborative Learning in Video Meetings

Video meetings gained popularity for remote communication, both for work and education. As a result, collaborative online learning has become increasingly widespread. However, it is a challenge to make students feel engaged and connected during video meetings. In this study, we addressed this problem with a prototype for a wearable user interface called Study Marbles. It aimed to create a more social and active sense of remote, collaborative learning in video conferences. Our device is a tangible necklace with attachable, illuminated marbles that can be worn during video meetings. It could visualize students’ learning status, moderate group discussions, and enable voting. The user study showed that participants perceived our prototype as a good way to create a more active and connected environment and to improve the interaction between group members in video conferences.
Integration and Classification of Various Massive Datasets using Big Data Framework

The integration of different big data platforms and input data from corresponding sources ensure the expected analysis and prediction of massive data with its nature. This paper incorporates the mechanism of combining and configuring data processing frameworks to generate input data, transform and analyze respective data based on its nature and requirement. In this paper, we look at multiple tasks in three different categories. Initially, it involves the classification of a local dataset with a large volume using a machine learning algorithm. Secondly, integration and operation of SQL data generated from Relational Database management systems. And lastly, the analysis of incoming data streams generated as a source from the Twitter app manager into Apache Spark. Based on this experiment the expected result is properly categorized, classified from each local dataset, integrated relational database, and results of most populated hashTags of real-life Twitter post data. Eventually, the experiment of the system depicts and evaluates analysis and combination of technologies for handling big data streams along with corresponding platforms.
Demystifying “bad” error messages in data science libraries

Error messages are critical starting points for debugging. Unfortunately, they seem to be notoriously cryptic, confusing, and uninformative. Yet, it still remains a mystery why error messages receive such bad reputations, especially given that they are merely very short pieces of natural language text. In this paper, we empirically demystify the causes and fixes of "bad" error messages, by qualitatively studying 201 Stack Overflow threads and 335 GitHub issues. We specifically focus on error messages encountered in data science development, which is an increasingly important but not well studied domain. We found that the causes of "bad" error messages are far more complicated than poor phrasing or flawed articulation of error message content. Many error messages are inherently and inevitably misleading or uninformative, since libraries do not know user intentions and cannot "see" external errors. Fixes to error-message-related issues mostly involve source code changes, while exclusive message content updates only take up a small portion. In addition, whether an error message is informative or helpful is not always clear-cut; even error messages that clearly pinpoint faults and resolutions can still cause confusion for certain users. These findings thus call for a more in-depth investigation on how error messages should be evaluated and improved in the future.
A Complete Semantics of $$\mathbb {K}$$ and Its Translation to Isabelle

$$\mathbb {K}$$ [46] is a rewrite-based executable semantic framework in which programming languages, type systems and formal analysis tools can be defined using configurations, computations and rules. Isabelle/HOL [41] is a generic proof engine which allows mathematical formulas to be built into a formal language and provides tools to prove those formulas in a logical calculus. In this paper we define IsaK, a reference semantics for $$\mathbb {K}$$ , which was developed through discussions with the $$\mathbb {K}$$ team to meet their expectations for a semantics for $$\mathbb {K}$$ . Previously, we defined the static semantics for $$\mathbb {K}$$ [28]; thus, this paper mainly focuses on its dynamic semantics. More importantly, we investigate a way to connect $$\mathbb {K}$$ and Isabelle by building a translation framework, TransK, to translate programming languages defined in $$\mathbb {K}$$ into theories defined in Isabelle, which can not only allow programmers to define their programming languages easily in $$\mathbb {K}$$ but also have the ability to reason about their languages in Isabelle. In order to show a well-established translation, we prove that the $$\mathbb {K}$$ specification is sound and relatively complete with respect to the translated Isabelle theory by TransK. To the best of our knowledge, IsaK is the first complete formal semantics defined for $$\mathbb {K}$$ , while TransK is the first complete translation from a real-world, order-sorted algebraic system to a many-sorted one. All the work is formalized in Isabelle/HOL at https://github.com/liyili2/KtoIsabelle .
Towards a System Monitoring Modeling Language (SyMoLa)

Best practices in software development suggest that systems include monitoring functionalities, allowing verification, auditing, traceability of operations, and quick response to incidents. On the other hand, domain-specific modeling languages (DSML) have shown great utility by allowing them to portray knowledge at a high level of abstraction. Even the application of DSML transformation tools towards specific implementations considerably reduces development time and effort, optimizing resources to take advantage of them in improving the design of systems, without worrying about low-level details.In this paper, a domain-specific modeling language oriented to systems monitoring specification is proposed, with a focus on the generation of cloud platforms for monitoring services. For this, the monitoring needs of different areas and applications were considered, the relevant concepts were synthesized, everyday needs were unified and consolidated, allowing to generalize and provide the modeling language with sufficient expression capabilities for monitoring the most domains. The syntax and semantics modeling language was provided, defining the graphical components that allow expressing each of the monitoring needs, and specifying restrictions, contexts, and notations, respectively. This study aims to contribute to Industry 4.0, with a design tool to facilitate the development processes of system monitoring solutions. This language is oriented to the integration of data in system of systems, extending the scope of the monitoring towards the perspective of a third party (regulatory entity, audit or monitoring services in the cloud).
Density-Based Remote Override Traffic Control System

Vehicular traffic is growing almost everywhere in the world which is the prime cause of congestion, especially at road intersections. Due to unreliable and unpredictable road travel conditions, the emergency and essential services may get trapped in traffic congestion. The traffic lights traditionally have a fixed time-scheduling and this further blocks the smooth flow of traffic. In this paper, a density-based remote override traffic control system has been proposed which provides a solution to smartly manage the dynamic traffic conditions. In this test device, the design of the junction density calculation would be centered on infrared (IR) sensors, installed on each lane and interfaced with the microcontroller. The IR will be enabled as vehicles travel through the path. This may be achieved by changing the sequential order of traffic lights generated by the autonomous road surveillance network by utilizing the automated remote sensing-based system.
Forecasting Needs Of Mountain Types Of DDD Bike Using The Seasonal Autoregressive Integrated Moving Average Model Approach

One of the bicycle manufacturers in Indonesia, namely PT. DDD is a manufacture engaged in the production of various types of bicycles with a make to stock production system. Market demand that fluctuates every year results in a lack of readiness to meet market needs. So a re-planning is needed in order to meet all market demands. The Box Jenkins statistical method, the Seasonal Autoregressive Integrated Moving Average model, is one of the appropriate approaches to solve problems at PT. DDD. The advantages of the SARIMA model can be used to forecast seasonal or non-seasonal time series simultaneously. The best SARIMA model approach to forecasting demand for mountain bikes at PT. DDD is SARIMA (0,0,0)(0,1,1)12 with the equation Zt=Zt-12+ΘQat-12+at with the smallest MAPE value of 32.35%. So that the model is said to be feasible to predict mountain bikes and the model can predict up to 12 periods in 2021.
On the Establishment of Integrating the Idea of Computer Mathematical Modeling into the Main Courses of Mathematics

Abstract How to conduct scientific research and research on some detailed problems, people who understand and have studied mathematical thinking in depth may know that the use of mathematical modeling is a way to maximize benefits. Even in today’s high-tech experiments on natural sciences, whether it is mathematical modeling theory or its main application methods, they can play a great role [1]. Of course, the foundation of using mathematical modeling is the in-depth and mastery of mathematical courses. Then, according to the thinking of education, if we can apply the learning method of mathematical modeling to the teaching of the original mathematics course, we can apply some mathematical network teaching resources intact and effective to modern media teaching. in. This approach can not only help students improve their self-learning mastery, but also improve the quality of mathematics teaching in each school from the side.
The Cursive Writing Learning Methods in Curriculum 2013 in The Primary School of Sragen

Cursive writing is an activity writing beginnings in Primary school. Students in Primary school when learning to write cursive often experience problems berupa the writing is still not neat, the legibility is not clear and not fluent in writing capital letters, and the writing is not in accordance with the lines of the book. The lack of student ability is due to a lack of knowledge in writing rules and proper methods of writing cursive. The purpose of this research is know the method learning to write cursive used in the 2013 curriculum at the State Primary School in Sragen in and method proper learning for students of the existing methods. The method used in this research is descriptive method with a qualitative approach in primary schools in Sragen. The research subjects were second grade students of primary School. The sampling technique was carried out by purposive sampling. Data collection techniques through interviews, observation and documentation. The analysis technique uses an interactive analysis model which consists of three stages, namely: data reduction, data presentation, and conclusion drawing. The results showed that in learning activities to write cursive writing, most teachers apply several methods such as demonstrations, groups/ discussions, assignments, picture and picture, complete sentences, SAS, and Drill. The most appropriate and effective Drill method to be applied in learning because students can practice cursive so that they can form a good habit of writing cursive in students.
GPG-NET: Face Inpainting With Generative Parsing Guidance

Face inpainting is an interesting while challenging task in the fields of computer vision and image processing. In this paper, a parsing based approach is worked out to solve facial image inpainting problems. Clearly, an intact facial image may contain extensive details, and hence a straight forward overall reconstruction is hardly achieved when serious damages exist. To deal with this problem, we choose to first recover the overall image structure represented by parsing images which are quite simple compared with detailed original images. Base on this idea, a two stages based face inpainting framework is proposed, where the first stage exclusively conductss parsing image inpainting, while the following second stage recover all details. Furthermore, a Semantic Compensation Module (SCM) is fused into the first stage to ensure effective context information aggregation, and a Contextual Attention Module (CAM) is brought into the second stage to further improve the appearance rationality. Extensive experiments are conducted on the publicly available CelebA-HQ dataset to verify the effectiveness of the proposed approach.
Enhancing the Privacy Protection in Social Networks

This paper aims to design an application to find solutions for protecting the privacy of social networks’ big data by using the artificial neural networks (ANN). In particular, the researcher designs a new algorithm for training an ANN to detect the fake accounts of Twitter according to some conditions to protect the user from the external attacks by the hackers. The program was written in (JAVA) language to train ANN by collecting information about each account which supported by Twitter4j. The researchers concluded many results, the main conclusions of the paper were that any Twitter account will have sensitive to the fake account by applying our program. So the fake account will not be able to exploit the user. Fake accounts detection can be complement intrusion prevention techniques to increase the SNS security. The proposed ANN should be done locally in each connect in the SNS. Moreover, attacks detection should get the place in all SNS types.
State Estimation of Multi-sensor System with Observation Delay Based on SCI-AUFK

In order to deal with the fusion estimation problem of the multi-sensor system with observation lag, the local suboptimal recursive Kalman filter is introduced, and the improved covariance cross fusion algorithm is used to propose an improved covariance cross fusion Kalman filter algorithm, that is, fast sequence covariance cross Fusion adaptive unscented Kalman filter algorithm (SCI-AUKF). It can avoid the large computational burden caused by the calculation of cross-covariance, and can deal with the fusion problem of systems with unknown cross-covariance. Compared with the traditional covariance cross-fusion Kalman filter, it has higher robust accuracy. The improved covariance cross-fusion Kalman filter has higher accuracy than each local sensor, and is closer to the accuracy of the Kalman filter weighted by the matrix, so it has Good performance. The simulation example verifies its validity and consistency, and gives a geometric explanation of the accuracy relationship.
Visual Management system of Intelligent Factory tools based on Odoo

The management and visualization of intelligent factory tools play a key role in maximizing the efficiency of tool use and cost saving. Aiming at the problems of inefficient, error-prone and difficult operation of traditional factory tool management, this paper puts forward a tool management scheme with complete life cycle, state visualization and real-time dynamic management. Based on odoo open source framework, Echarts visualization components, OCR recognition algorithm and Postgresql database, an intelligent factory tool management system is developed. The system realizes the data management, inventory management and roller grinding management of workshop tools, and realizes the visual management of the whole process of tools from storage, grinding to scrapping. The dynamic management process is based on the local OCR identification algorithm, and mobile devices can be used to identify and manage the basic information of factory tools in the local area network.