Reviewerly/exHarmony huggingface dataset:
Workload Balancing via Graph Reordering on Multicore Systems

In a shared-memory multicore system, the intrinsic irregular data structure of graphs leads to poor cache utilization, and therefore deteriorates the performance of graph analytics. To address the problem, prior works have proposed a variety of lightweight reordering methods with focus on the optimization of cache locality. However, there is a compromise between cache locality and workload balance. Little insight has been devoted into the issue of workload imbalance for the underlying multicore system, which degrades the effectiveness of parallel graph processing. In this work, a measurement approach is proposed to quantify the imbalance incurred by the concentration of vertices. Inspired by it, we present <i>Cache-aware Reorder (Corder)</i> , a lightweight reordering method exploiting the cache hierarchy of multicore systems. At the shared-memory level, Corder promotes even distribution of computation loads amongst multicores. At the private-cache level, Corder facilitates cache efficiency by applying further refinement to local vertex order. Comprehensive performance evaluation of Corder is conducted on various graph applications and datasets. Experimental results show that Corder yields speedup of up to <inline-formula><tex-math notation="LaTeX">$2.59\times$</tex-math></inline-formula> and on average <inline-formula><tex-math notation="LaTeX">$1.45\times$</tex-math></inline-formula> , which significantly outperforms existing lightweight reordering methods. To identify the root causes of performance boost delivered by Corder, multicore activities are investigated in terms of thread behavior, cache efficiency, and memory utilization. Statistical analysis demonstrates that the issue of imbalanced thread execution time dominates other factors in determining the overall graph processing time. Moreover, Corder achieves remarkable advantages in cross-platform scalability and reordering overhead.
Two-Qubit Unitary Quantum Process Tomography by Multiple-Delay Output Measurements for One Unknown Input Pure State Value

Quantum process tomography (QPT) methods aim at identifying a given quantum process. QPT is a major quantum information processing tool, since it especially allows one to characterize the actual behavior of quantum gates, which are the building blocks of quantum computers. The present paper focuses on the estimation of a two-qubit unitary process. This class is of particular interest because quantum mechanics postulates that the evolution of any closed quantum system is described by a unitary transformation. Usual QPT methods can be hard to implement in practice because they require the user to prepare copies of a set of known quantum states. In contrast, the proposed methods only require the user to be able to provide copies of a single unknown input state which can be estimated along with the process. However, we need to be able to make measurements of the state of the system after 5 different time delays ∆ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> , 2∆ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> , 3∆ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> , 4∆ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> , 5∆ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> (one measurement per copy). We detail a least square approach which exploits tools used in the aerospace community. A slower but more precise maximum likelihood method is also introduced. It uses a simple parametrization of unitary matrices.
Theme Choice and Thematic Progression of Discussion Section in Engineering English Lectures

In the fourth industrial revolution, globalization of higher education has been significant demands for higher education across national borders, both in the field of teaching and research. Some research has found that linguistic issues become a major problem for most Indonesian students who are studying at overseas universities. The students mostly faced problem in capturing lecture contents or lecture important points delivered by lecturers. This study then looked into the types and functions of theme choices and thematic progression deployed in Engineering English Lecture-Discussion in a university in the Netherlands, where English is used as the medium instruction. The Hallidayan notion of Theme (1994) and thematic progression (Eggins, 2004) were applied. By employing a content qualitative analysis approach, seven lecturers were selected and video-taped during formal teaching hours. A total of 394 clauses in the Engineering English Lecture discussion were identified and analyzed. The results showed that all clauses have an ideational point of departure or theme and also, the speaker may choose to express an interpersonal and a textual theme. The ideational theme and the combination of constant - zig-zag Theme pattern dominantly occupied the theme system chosen for the Engineering English Lecture-Discussion. In summary, theme choice and progression can be used to develop a text and to facilitate the understanding of the message. So, it will be useful for EFL students to achieve specifiable goals such a lecture content.
A Case Study of Renewable Energy Resources Assessment Results in Argentina

With the increasing development of renewable energy resources in South America, to analyze "the location", "the quantity" and "the quality" of wind and solar energy resources will, not only benefit for one country on efficient resource utilization, but also promote the realization of large- scale international resource utilization and complementation. Argentina, as a major country in South America, researches on its renewable energy development is of great significance. This paper completed the renewable resources assessment through modeling and estimating the theoretical reserves, technical potential installed capacity and development cost of wind and solar energy in Argentina. Then, a more precise quantitative evaluation of development potential is given through the calculated development cost and capacity factor of technical potential installed capacity. Finally, after compared the existing power stations with the assessment results, suggestions on how to development renewable energy resources in Argentina are given.
Designing a Drone Controller using Electromyography Signals

The aim of this project is to introduce new-fangled way to control Unmanned Aerial Vehicle (UAV) using electromyography (EMG) signal. Electromyography signals are biopotential signals that are generated when any muscular action takes place in a person's body. The idea is to provide a novel method of controlling drones, and other remote-controlled objects by recognizing hand gestures using EMG Signals. This paper presents a controller designed that can be easily fitted to a band on the user's arm. It is composed by inertial (gyroscope, accelerometer) and electromyographic sensors, that will measure the muscle movements and construe them in references for the drone. Gesture recognition is achieved using an Artificial Neural Network to fully exploit the capabilities of both, the user and UAV - Unmanned Aerial Vehicle. UAV chosen to test with the controller is a quadcopter. The comparative results show the compactness and easy-to-handle interface that allows even inexperienced users, to intuitively have control over remote-controlled objects as compared to a standard remote controller.
Application of MAIR Model in the Analysis of Influencing Factors of College Students’ Innovation and Entrepreneurship

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021)

Adversarial learning methods and their applications such as generative adversarial network, adversarial robustness, and security and privacy, have prevailed and revolutionized the research in machine learning and data mining. Their importance has not only been emphasized by the research community but also been widely recognized by the industry and the general public. Continuing the synergies in previous years, this third annual workshop aims to advance this research field. The AdvML'21 workshop consists of three tracks: (i) open-call paper submissions; (ii) invited speakers; and (iii) rising star awards and presentations. The full details about the workshop can be found at https://sites.google.com/view/advml.
A Vision-based Forward Driving Vehicle Velocity Estimation Algorithm for Autonomous Vehicles

In this paper, we proposed a vehicle velocity estimation algorithm when it is in front of autonomous car based on the only vision system. In order for autonomous cars to drive safely, they need to recognize their surrounding drive environment using various sensors. Among them, the stereo camera can acquire not only a normal image but also an image showing a depth map. Therefore, in this paper, the speed of the target vehicle was estimated by using only the stereo camera without any other sensor. Also, to recognize the target vehicle, CNN based semantic segmentation model with high speed and accuracy was used. Unlike object detection models, the semantic segmentation model can acquire only the object shape, so there is a lot of depth information that can be obtained from the depth image. Through this, more accurate distance information between own car and target vehicle was extracted. Lastly, the relative speed between vehicles was estimated by using the estimated distance change and the time difference between frames. The superiority of the proposed algorithm performance was compared with the vehicle speed using a motor encoder. And it was confirmed that the speed of the target vehicle was accurately estimated using only the camera.
Correlates of the Adherence to a 24-hr Wrist-Worn Accelerometer Protocol in a Sample of High School Students

This study (a) compared accelerometer wear time and compliance between distinct wrist-worn accelerometer data collection plans, (b) analyzed participants’ perception of using accelerometers, and (c) identified sociodemographic and behavioral correlates of accelerometer compliance. A sample of high school students ( n = 143) wore accelerometers attached to the wrist by a disposable polyvinyl chloride (PVC) wristband or a reusable fabric wristband for 24 hr over 6 days. Those who wore the reusable fabric band, but not their peers, were instructed to remove the device during water-based activities. Participants answered a questionnaire about sociodemographic and behavioral characteristics and reported their experience wearing the accelerometer. We computed non-wear time and checked participants’ compliance with wear-time criteria (i.e., at least three valid weekdays and one valid weekend day) considering two valid day definitions separately (i.e., at least 16 and 23 hours of accelerometer data). Participants who wore a disposable band had greater compliance compared with those who wore a reusable band for both 16-hr (93% vs. 76%, respectively) and 23-hr valid day definitions (91% vs. 50%, respectively). High schoolers with the following characteristics were less likely to comply with wear time criteria if they (a) engaged in labor-intensive activities, (b) perceived that wearing the monitor hindered their daily activities, or (c) felt ashamed while wearing the accelerometer. In conclusion, the data collection plan composed of using disposable wristbands and not removing the monitor resulted in greater 24-hr accelerometer wear time and compliance. However, a negative experience in using the accelerometer may be a barrier to high schoolers’ adherence to rigorous protocols.
Blockchain-Powered Healthcare Information Exchange Systems to Support Various Stakeholders

Initially, blockchain technology was emerged for the enhancement of financial transactions, as it is independent of the need to have any third party to verify the transactions. Progressively, it has been slightly modified based on the different application-specific requirements such as data security and privacy. One of the emerging applications of blockchain is E-healthcare that concerns mainly about integrity, authenticity, and consistency of patients’ medical records. Due to the evolution of Internet of Things (IoT), a lot of healthcare data is being produced through the use of various devices like smart watches, smart sphygmomanometer, smart thermometer, etc. This imposes the need to concern about scalability along with interoperability. A novel architecture to handle the electronic medical records (EMR) of the patients by various medical entities is proposed. As large amounts of records are to be handled, the healthcare archives are kept in the cloud for streamlining the usage of information among diverse stakeholders. Also, there is a provision to enable the measures that handle security and privacy in the cloud architecture. Suitable public key cryptography and hashing methods are exploited to maintain the past transactions corresponding to the patients’ records. This preserves confidentiality, integrity, and availability. It also prevents the modification or falsification of data by unauthorized persons. Using blockchains, patients’ records can be added only at the end of the database, but they cannot be removed. New records are securely connected to the preceding record using cryptographic hashing. Special node called data validator is used to check the quality and authenticity of user-uploaded data, from which the records can be examined and patient health status reports are prepared. Again, encryption and digital signing are performed on the data to store it back to the blockchain. This proposed system ensures that no individual can modify or damage the verified records that are already stored. Our proposed novel architecture was tested against MIT-BIH Arrhythmia Database, and the stated functionality requirements are met.
Cloud Education Chain and Education Quality Evaluation Based on Hybrid Quantum Neural Network Algorithm

This paper proposes the functional model and application service implementation process of the education cloud platform application service architecture. The entire cloud application service architecture mainly includes four parts: cloud service management, cloud application service rapid creation and deployment, dynamic process configuration, and unified identity authentication. Based on the basic theory of workflow, the process status and business services of cloud application services are discussed. The BP neural network weight optimization model based on the improved quantum evolution method is studied, and a method that combines the improved quantum evolution algorithm (IQEA) and the BP algorithm to complete the back propagation neural network training is proposed, that is, the IQEA-BP algorithm. Firstly, the traditional quantum evolution algorithm is improved, and then, the improved quantum evolution algorithm is used to optimize the network weights as a whole to overcome the shortcomings of the BP algorithm that is easy to fall into the local optimum; then, we use the BP algorithm to find the better weight as the initial value to improve the training and prediction accuracy of the network. In order to enrich the school education quality evaluation system, this article adds soft indicators that can reflect school education performance on the basis of the existing “National Education Inspection Team” indicators and uses analytical methods to prove the effectiveness and feasibility of the new evaluation indicators. The X1-X10 index data is selected as the evaluation index of the school education quality evaluation system in this paper. Testing the performance of the BP neural network, the accuracy rate of the school education quality evaluation is 93.3%, the average absolute error is 0.067, and the accuracy and recall rate of the test set grade gradient of 0, 1, 2, 3, 5, 6, and 8 are all 93%, indicating that the IQEA-BP neural network algorithm has a good effect on the evaluation of school education quality.
A new over-sampling ensemble approach for imbalanced data

With the rapid development of information technology, data in various fields is being generated, collected and stored at an unprecedented speed. With the increase in the amount of information, problems such as data imbalance and noise have become increasingly serious. However, traditional classification algorithms aiming at maximizing the overall classification accuracy tend to favor the majority class while ignoring the minority class when dealing with imbalanced data. In order to solve this problem, this paper proposes a new algorithm, which combines oversampling technology with boosting ensemble learning algorithm, and uses the error function of each round to guide the next round of sampling procedures. Experiments have proved that our algorithm has higher classification accuracy than other widely-used imbalanced learning methods, and has better adaptability because it does not depend on the specific classifier.
Unsupervised Training Data Generation of Handwritten Formulas using Generative Adversarial Networks with Self-Attention

The recognition of handwritten mathematical expressions in images and video frames is a difficult and unsolved problem yet. Deep convectional neural networks are basically a promising approach, but typically require a large amount of labeled training data. However, such a large training dataset does not exist for the task of handwritten formula recognition. In this paper, we introduce a system that creates a large set of synthesized training examples of mathematical expressions which are derived from LaTeX documents. For this purpose, we propose a novel attention-based generative adversarial network to translate rendered equations to handwritten formulas. The datasets generated by this approach contain hundreds of thousands of formulas, making it ideal for pretraining or the design of more complex models. We evaluate our synthesized dataset and the recognition approach on the CROHME 2014 benchmark dataset. Experimental results demonstrate the feasibility of the approach.
Evaluation of Gadjah Mada University Yogyakarta Campus Drainage System Using Environmental Protection Agency Storm Water Management Model 5.1 (EPA SWMM 5.1)

Abstract The drainage system is an infrastructure which controls the water to prevent the flood and to maintain the public health. There are two concepts of the drainage system, conventional drainage and eco drainage. Universitas Gadjah Mada still using the concept of conventional drainage. In this final project, the writer evaluates the drainage system at Universitas Gadjah Mada Yogyakarta using EPA SWMM 5.1 software. The methodology used by the author is the research methods used in this final project are interviews, observation, participation, and literature. The analysis using in this final project are hydrology analysis, land-use analysis, hydraulic analysis, and management and maintenance analysis. The evaluation of the drainage system will use the calculation of the area is based on the location of the rain station with the calculated average method. Hydrology analysis using frequency analysis and SCS-CN rainfall effective method. Land-use change analysis in the Universitas Gadjah Mada area. Hydraulic analysis using EPA SWMM 5.1, and the management and maintenance analysis of drainage system of Gadjah Mada University. Based on the result of the simulation scenario, installation of the low impact development using permeable pavement that provides a significantly reduced runoff. The runoff coefficient using permeable pavement is 0,23. The peak of runoff using permeable pavement is very slowest than another. Permeable pavement installation is considered very effective.
Camera Calibration and Video Stabilization Framework for Robot Localization

Two major issues in robot localization are Camera Calibration (CC) and Video Stabilization (VS). The effectiveness of CC is highly provisional based on adjusting settings, image quality, and image gradient. Recent breakthrough methods employ fixed threshold to calculate pixel difference between frames and preset variables, and neglect slope information causing blurring effect for image frame selected in CC phase. Additionally, contemporary optical flow requires expert manual setting of Gaussian pyramid parameters such as sigma, down scale factor, and number of levels, which consume a lot of time and efforts to train and measure. Apart from that, the localization key challenges of humanoid stereo vision are large motion, motion blur, and defocus blurs of image. Though state-of-the-art approaches used landmark recognition and probabilistic models to overcome those issues, yet localization accuracy is still poor due to image distortion. This work proposed a framework for robot localization via CC and VS methods and triangulation concept. The framework with Fuzzy Camera Calibration (FCC) achieved better results in re-projection error compared to s about 0.85 and 2.62 in pairs based on self-collected dataset, whereas FCC versus Ferstl scored approximately 0.21 and 0.24 in pairs using Time-of-flight camera dataset. For VS, this framework with Fuzzy Optical Flow (FOF) method achieved second rank compared to the state-of-the-art methods such as Farneback, Brox (GPU), LK (GPU), Farneback (GPU), Dual_TVL1, and Simple Flow tested on SINTEL benchmark datasets. Finally, our proposed stereo vision, localization framework also outperformed Mono Vision method vision about 4.07 cm and 61.07 cm subsequently of distance errors.
Information Technology Hits the Automobile? Rethinking Road Traffic as Social Interaction

Drivers fall asleep and smash head-on with an oncoming car. Wild attempts at overtaking become fatal. Cars, roads and traffic as components seem to fit well into the vocabulary of the emergent research field devoted to the study of Large Technical Systems where a distinction is made between vehicle unit, operation and network. It is apparent from May's and Kronborg's discussion that the people are unable to understand the operation of traffic simply by studying the activities of a traffic control center. The new technology would then start to play an active role in automobile driving. Thus, three paths or trajectories are visible for drivers' integration in road traffic as a cultural system, depending on different foundations for its social order: autonomous driving, centrally dependent driving and locally dependent driving. The application of Dannefer's classification of different forms of operation opens up a discussion concerning the impact of cooperative driving on the configuration of the road transport system.