Reviewerly/exHarmony huggingface dataset:
Pengembangan Pembelajaran Berbasis Autoplay Media Studio 8 Pada Mata Pelajaran Marketing SMK Negeri 2 Blitar

Autoplay Media Studio 8 software aims to create an interactive learning media by using a presentation integrated with video, sound, images, flash, and text on the basic competencies of Goods and Services Marketing Strategy Implementers. The goal author aims by conducting this research is to ascertain the feasibility of learning media as a marketing subject for class X students of online business and marketing. Method used for this research is development model Research and Development which also commonly known as educational research and development. Several steps included within research and development method are recognizing problems and potential, data collection, designing product, designing validation, designing revision, testing product in trial, producing revision, user trial, product revision, and mass product. To validate data for this questionnaire, the author used Likert scale. The research shows a sign that the development of instructional media got positive results. Autoplay Media Studio 8 Software allure student’s interest and simplify the way student tries to understand presented material. DOI: https://dx.doi.org/10.17977/UM014v14i12021p71
Explainable Artificial Intelligence Model: Analysis of Neural Network Parameters

In recent years, artificial neural network is becoming a popular technology to extract the extremely complex pattern in the data across different segments of research areas and industrial applications. Most of the artificial intelligence researchers are now focused to build smart and user-friendly applications which can assist humans to make the appropriate decision in the business. The aim to build these applications is mainly to reduce the human errors and minimize influence of individual perceptions in the decision-making process. There is no doubt that this technology will be able to lead to a world where we can enjoy AI-driven applications for our day-to-day life and making some important decisions more accurately. But what if we want to know the explanation and reason behind the decision of AI system. What if we want to understand the most important factors of the decision-making processes of such applications. Due to the intense complexity of inherent structure of AI algorithm, usually researchers define the artificial neural network as “black box” whereas the traditional statistical learning models are more transparent, interpretable and explainable with respect to data and underlying business hypothesis. In this article, we will present TRAnsparent Neural Network (TRANN) by examining and explaining the network structure (model size) using statistical methods. Our aim is to create a framework to derive the right size and relevant connections of network which can explain the data and address the business queries. In this paper, we will be restricting us to analyse the feed-forward neural network model through nonlinear regression model and analyse the parameter properties guided by statistical distribution, information theoretic criteria and simulation technique.
Campus Bullying Detection Algorithm Based on Audio

With the continuous breakthroughs in various technologies, voice recognition has become a research hotspot. It is a method to detect the phenomenon of bullying in time by detecting whether the campus bullying emotion is contained in the voice. This paper builds a convolutional neural network model to recognize speech emotions. Firstly, pre-process the audio data, then extract the MFCC feature parameters from the pre-processed audio data, and finally design a classification algorithm. This paper selects the CASIA database, which has a total of 300 voice audios, including six emotions: angry, scared, happy, neutral, sad, and surprised. Using fivefold cross-validation to test the performance of the model, the accuracy of the classification algorithm is 68.51%. Finally, the classification algorithm is used to perform emotion recognition on a test sample selected from a campus bullying movie section. This section shows “fear” emotion, and the algorithm judges that the audio shows “fear” emotion. The actual scenes are consistent, indicating that the classification algorithm in this paper has certain stability and practicability.
Evaluation Metrics for the Cost of Data Movement in Deep Neural Network Acceleration

Hardware accelerators are designed to support a specialized processing dataflow for everchanging deep neural networks (DNNs) under various processing environments. This paper introduces two hardware properties to describe the cost of data movement in each memory hierarchy. Based on the hardware properties, this paper proposes a set of evaluation metrics that are able to evaluate the number of memory accesses and the required memory capacity according to the specialized processing dataflow. Proposed metrics are able to analytically predict energy, throughput, and area of a hardware design without detailed implementation. Once a processing dataflow and constraints of hardware resources are determined, the proposed evaluation metrics quickly quantify the expected hardware benefits, thereby reducing design time.
Utilizing Blockchain Technology to Manage Functional Areas in Healthcare Systems

A blockchain is a public ledger that is secured and trusted which automatically records and verifies huge amount of digital transactions. This study highlights the importance of blockchain technology, and provides an overview on how blockchain can be implemented in the healthcare sector. It mainly focuses on applying blockchain technology in pharmacy, blood banks, laboratory and radiology department. The paper also mentions the various challenges and limitations of implementing blockchain technology in healthcare systems. In addition, it investigates how blockchain can be used as an important tool in fighting against pandemic situations. The present study will help and support decision makers for effective implementation of blockchain technology in the healthcare sector.
Tuning Hyperparameters on Unbalanced Medical Data Using Support Vector Machine and Online and Active SVM

This paper is a comparative study of different tools using the Support Vector Machines (SVM) and the Online and Active SVM (LASVM), where we will present different results on different medical datasets from the ICU. Also, there is an emphasis on tuning the classifiers’ hyperparameters due to data imbalance. SVM’s results will be built on the utilization of two commonly used tools, whereas LASVM’s results will be based on two other different tools. The objective of this article is to see which classifier outputs the best results through the calculation of some selected metrics.The data are samples from Health Monitoring Systems (HMS), which monitors the state of ICU patients in real time. By using audible alarms, HMS determine either if patients are in critical state or in a normal state. In other words, the alarm is triggered if a patient is in a life-threatening situation and nothing happens if he is in a normal state.In the first and second section, we will respectively introduce the theoretical definition of SVM and LASVM. In the third section we present the hyperparameters that can be tuned, and the fourth section will focus on the experimental results generated by both algorithms to determine what is the best tool used.
Bayesian Fisher Information, Shannon Information, and ROC Analysis for Classification Tasks

We have previously shown how the Bayesian Fisher Information (BFI) is related to the average of the Minimum Probability of Error (MPE) for the task of detecting a small change in a parameter. The prior probabilities for the two hypotheses corresponding to two nearby values of the parameter are derived from the prior distribution on the parameter, as they are for the Ziv-Zakai inequality. The average in this result and others to be discussed is performed over the parameter values using the prior. We extend this result to the case where a cost function is assigned to the detection task. In this case we determine the lowest order approximation of the average of the Bayesian Risk for the detection task. We have shown that there is a similar relation between the average Shannon Information (SI) for this detection task and the BFI, and that there is an integral relation between SI for any detection task and the MPE for that task. We will show the Bayesian perspective on this last result, and how these two results about SI are related.
Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learning

Low-prior targets are common among many important clinical events, which introduces the challenge of having enough data to support learning of their predictive models. Many prior works have addressed this problem by first building a general patient-state representation model, and then adapting it to a new low-prior prediction target. In this schema, there is potential for the predictive performance to be hindered by the misalignment between the general patient-state model and the target task. To overcome this challenge, we propose a new method that simultaneously optimizes a shared model through multi-task learning of both the low-prior supervised target and general purpose patient-state representation (GPSR). More specifically, our method improves prediction performance of a low-prior task by jointly optimizing a shared model that combines the loss of the target event and a broad range of generic clinical events. We study the approach in the context of Recurrent Neural Networks (RNNs). Through extensive experiments on multiple clinical event targets using MIMIC-III [8] data, we show that the inclusion of general patient-state representation tasks during model training improves the prediction of individual low-prior targets.
PRECISE Photonic Hybrid Electromagnetic Solver

PRECISE, Photonic hybRid EleCtromagnetIc SolvEr, is a Matlab-based library to model large and complex photonic integrated circuits. Each circuit is modularly described in terms of waveguide segments connected through multi-port nodes. Linear, nonlinear, and dynamical phenomena are simulated by solving the system of differential equations describing the effect to be considered. By exploiting the steady-state approximation of the electromagnetic field within each node device, the library can handle large and complex circuits even on desktop PCs. We show that the steady-state assumption is fulfilled in a broad number of applications and we compare its accuracy with an analytical model (coupled mode theory) and experimental results. PRECISE is highly modular and easily extensible to handle equations different from those already implemented and is, thus, a flexible tool to model the increasingly complex photonic circuits.
Design intervention process and intervention strategy of “Constant-holistic Shadow” space in hot summer area

Abstract It is one of the goals of outdoor space design in hot summer areas to construct a “constant-holistic shadow” space for outdoor people that can always be in the shadow during the required period of time in summer. However, the relevance and process of design elements related to the space of “constant-holistic shadow” are different from those of conventional design, as well as methods, stages and objectives of design intervention. Therefore, it should be controlled according to appropriate stage design objectives and contents. Based on the relationship between the design goal and the design process of “constant-holistic shadow” space, this paper analyzed the feasibility of the design process intervention under the construction goal of “constant-holistic shadow” space and clarified the corresponding design intervention process and strategies. It is expected to provide green design ideas and methods for promoting the utilization efficiency of summer shadow and optimizing the thermal comfort of outdoor public space in summer hot areas, as well as enrich the relevant theories and methods of outdoor public space design.
An Ensemble-based Regularization Method for Multi-Head Attention

Multi-head attention is applied in Transformer to jointly attend to information from different representation sub-spaces at different positions. In this work, we are inspired by ensemble learning propose a new disagreement term regularization to ensure that the multi-head mechanism works effectively. Specifically, we treat each head as a base learner of emsemble model and make multi-head more diverse by the bias-variance-covariance decomposition. The test results show that our method is quite effective in Multi-30K Enlish⇒German and IWSLT16 Enlish⇒German translation tasks.
Towards Efficient LPN-Based Symmetric Encryption

Due to the rapidly growing number of devices that need to communicate securely, there is still significant interest in the development of efficient encryption schemes. It is important to maintain a portfolio of different constructions in order to enable a quick transition if a novel attack breaks a construction currently in use. A promising approach is to construct encryption schemes based on the learning parity with noise (LPN) problem as these schemes can typically be implemented fairly efficiently using mainly “exclusive or” (XOR) operations. Most LPN-based schemes in the literature are asymmetric, and there is no practical evaluation of any LPN-based symmetric encryption scheme. In this paper, we propose a novel LPN-based symmetric encryption scheme that is more efficient than related schemes. Apart from analyzing our scheme theoretically, we provide the first practical evaluation of a symmetric LPN-based scheme, including a study of its performance in terms of attainable throughput depending on the selected parameters. As the encryption scheme lends itself to an implementation in hardware, we further evaluate it on a low-end SoC FPGA. The measurement results attest that our encryption scheme achieves high performance rates in terms of throughput on such hardware, providing evidence that symmetric encryption schemes based on hard learning problems may be constructed that can compete with state-of-the-art encryption schemes.
A novel secure diffusion Kalman filter algorithm against false data injection attacks

This paper proposes a novel secure diffusion Kalman filter (dKF) algorithm to improve the estimation performance crippled by false data injection attacks on sensors in wireless sensor networks (WSNs). Different from the conventional dKF, each adjacent node in the WSNs is detected to ascertain its trustworthiness before local estimate fusion, so as to form a new secure network topology. Then the combination step is performed to fuse the information collected from the secure topology. The proposed secure dKF algorithm, having a better estimation performance, is robust to false data injection attacks on multiple sensors and partial elements of measurements. For the proposed secure dKF algorithm, its mean and mean-square performance are derived, based on which its convergence is analysed. Additionally, the estimating and tracking problem of projectile position is investigated to confirm the effectiveness of the proposed secure dKF algorithm. It is shown by simulations that the proposed secure dKF algorithm achieves a significant estimation performance gain.
Ensuring Resilience Using Augmented Reality: How Museums Can Respond During and Post COVID-19?

This chapter explores how cultural and heritage sites such as museums can weather the unpredictable storm of the Covid-19 pandemic by quickly adapting to the unprecedented situation to continue serving their communities in new innovative ways. The museums are awakening to the need for proactive and innovative measures for mitigating the impacts of the pandemic in order to ensure survival. These include offering online educational resources, showcasing of museum collections and engaging its communities with art-related discussions on social media. Powered by the key enabling technologies of the Fourth Industrial Revolution, many popular museums create virtual tours using mixed reality technologies to improve visitors' discovery in an interactive, engaging and enjoyable way. This chapter also highlights the challenges of the innovative applications. What might be introduced as temporary measures to address the current situation, could become paradigm shifts that lead to higher and more impactful engagement between the museums and the society, ensuring museums' resilience and irreplaceable status in the people's minds. Also, during such times of high stress in society, culture can play an important healing role as it can offer rallying beacons of solidarity leading to emotional resilience and overall well-being.
Domain-specific In-memory Computing Architecture: CMOS Annealing Machine to Solve Combinatorial Optimization problems

It has been a long time since it was said to be the age of big data, and a lot of data has been accumulated, but the data has not been effectively utilized. To effectively use various and large amount of data, and massive computing power is required. The performance of general-purpose computers, e.g. processors, has dramatically improved with semiconductor scaling, but the improvement will be saturated in the near future due to the slowdown of the semiconductor scaling. So the traditional computing with sequential processing cannot be used for the large-scale data processing and a new-paradigm computing is now required. One of the solution is domain-specific computing that is not based on the sequential operation.
Obstacle Avoidable G2-continuous Trajectory Generated with Clothoid Spline Solution

This research engages in generating trajectories with continuous curvature and applying blending cases. Clothoid spline has an important property that its curvature is linear related to its arclength, this property can provide a curvature (G2) continuity to the interpolation operation. But it is rarely used in the trajectory plan because its shape is hard to control to avoid obstacles. This paper uses a collision-free corridor to limit the trajectory generation to avoid obstacles. In a transformable system, using line segments to connect with Clothoid spiral segments, the computation complexity could be greatly reduced by setting the starting conditions equal to zero. With the computation, a unique solution to the spiral segment is found. The computation process and the corresponding algorithm are elaborated in this paper.
CHIIoT 2: 2nd Workshop on Computer Human Interaction in IoT Applications

The CHIIoT workshop series brings together researchers and practitioners from human-computer interaction (HCI) design, computer science, and electrical engineering working on new challenges in industry and academia. In EICS 2021, This workshop will provide a platform for participants to review and discuss challenges and opportunities in the intersection of computer-human interaction and the internet of things, focusing on human-centered applications using emerging connectivity and sensing technologies. We aim to jointly develop a design space and identify opportunities for future research.
Investigating the Relationship between Data Literacy and Tracker Abandonment

We explore whether data literacy is related to tracker abandonment by comparing former and current users of wearable trackers on their data literacy skill. In an online survey, former and current users ( N=233) completed a data literacy scale and then interpreted a heart rate chart which described a person in a dangerous resting heart rate during sleep. We found that these two user groups had similar levels of data literacy on a self-reported scale but current users were better at recognizing the dangerous heart rate data and also better at effectively making use of their data than former users. We discuss the practical implications of our findings to support users' long-term tracker engagement.
Pedagogical Support for Professional Development of Officers of the Ministry of Internal Affairs of Russia

The system of professional training of officers of the Ministry of Internal Affairs of Russia is meant to ensure the competence of a specialist and his high professional readiness for implementing the law-enforcement functions by means of a deep theoretical training, practical orientation of the training process, and introduction of students to values of the profession. The primary focus is on the formation of students’ competencies at all levels, which act as an important guide for organizing the activities of managers and teachers, as well as the desired result of mastering the content of education, which is shown in the ability to apply variably formed knowledge and skills depending on the situation. Professional readiness, in addition to the accumulated competencies and practical experience in their application, includes a personal component that characterizes the officer’s attitude towards the processes and phenomena, formed through the perception of professionally and personally important information, understanding of an environmental context, development of his attitude towards the phenomena of society in the process of the reflexive activity. The teacher’s function is transformed from an active translator of professionally significant information into a subject supporting accumulation of the personal experience in a planned person-centered environment that provides for a partner model of cooperation and reflexivity of relations between the subjects of joint activity. The goal of the research is to analyze the methodological basis of the process of professional becoming of officers from the Ministry of Internal Affairs of Russia, and to identify significant characteristics of the process of planning the educational environment based on pedagogical support in the formation of personal experience. Such methods as a comparative analysis of methodological approaches in organizing the person-centered education, the study of independent characteristics, search of static information, method of expert assessment were applied. The methodological approaches in organizing the professional becoming of a specialist have been studied, the role of pedagogical support in the professional becoming of a specialist has been substantiated; the relations between the effectiveness of professional development and organization of pedagogical support of this process have been detected.
Incorporating Domain Knowledge into Text Classification Diagnosis in Customer Service Dialogue Field

Abstract The customer service dialogue process is an important way for consumers to communicate with manufacturers. In order to enhance the consumer experience as well as to assist the staff, we build a knowledge base that can categorize consumer questions and provide suitable answers. However, due to labeling deviations, there are some errors in the knowledge base. So we propose a domain knowledge-based text classification diagnosis method, which innovatively transforms the question and answer task into the text classification task. We use an ERNIE-based structure to match consumer questions with multivariate groups of answers from the knowledge base, judged by similarity. Also for incorrectly matched pairs, our method provides a list of suitable candidates for selection. Compared with other baselines, our model achieves competitive results. At the same time, good results are obtained on cross-province data, proving that our method has good scalability.