Reviewerly/exHarmony huggingface dataset:
Active Contour driven by geometric mean and standard deviation-based energy fitting model for the left ventricle segmentation from cardiac magnetic resonance images

This paper proposes a geometric mean and standard deviation-based energy fitting model to improve the accuracy of segmentation of the left ventricle from cardiac Magnetic Resonance Imaging (MRI). Energy-fitting-based active contour models emerged so far suffer either from intensity inhomogeneity or gives wrong segmentation result due to an inappropriate initial contour. Thus, accurate and robust segmentation of the left ventricle from cardiac MRI still a challenging problem. Therefore, to tackle both the problems, a geometric mean-based energy-fitting model is proposed. Unlike the recent energy-fitting-based models which use the arithmetic mean to calculate the local energy, the proposed method uses geometric mean and scaled standard deviation to compute the energy functional which drives the active contour to the region of interest. In addition to that completely removes the initial contour problem by automating it according to the input. The initial contour in the proposed model is a circle its radius and the center are calculated from the input sample itself. This initial contour is an appropriate and automated one that helps to reduce the computation time for segmentation. Experiments are conducted on cardiac MRI images the result obtained is compared with ground truth and evaluated by Average perpendicular distance (APD) and DICE similarity coefficient. Further the visual, as well as evaluated parameters, evidences that the proposed model performs better than the existing methods.
The optimizing strategy of the library as a learning source center using AHP and Delphi methods

Various efforts have been made by teachers or educator managers to further improve and support a more effective and efficient learning process. Although many factors determine the quality of education or learning outcomes, one of them is related to the learning resource center. The problem in this research is how to increase the role of the school library as a learning resource center. The purpose of this research is to make a strategic plan for the development of the school library in achieving educational goals, namely the results of students who excel. The method used in this research is the determination of the criteria weight based on the hierarchy using Analytical Hierarchy Process (AHP) and the determination of some criteria using the Delphi technique. The results showed that strategic priority using the AHP concept obtained 5 strategies, namely 1) Common learning services with the highest weight, 0.42. 2) Multitasking service with a weight of 0.23. 3) Maker space service with a weight of 0.18. 4) Digital collection services with a weight of 0.11, and 5) Audio-visual services with a weight of 0.06.
The Rayleigh–Lorentz invariant for superconducting resonators and optimal adiabatic qubit-information detection

Abstract Dynamical properties of a resonator can be analyzed using the Rayleigh–Lorentz invariant which is not an exact constant but varies more or less over time depending on variations of parameters. We investigate the time behavior of this invariant for a superconducting nano-resonator in order for better understanding of qubit-information detection with the resonator. Superconducting resonators which uses parametric resonance in a Josephson junction circuit can be utilized in implementing diverse next generation nano-optic and nano-electronic devices such as quantum computing systems. Through the analyses of the temporal evolution of the invariant, we derive a condition for optimal adiabatic qubit-information detection with the resonator. This condition is helpful for controlling the dynamics of the resonators over long periods of time. It is necessary to consider it when designing a nano-resonator used for quantum nondemolition readouts of qubit states, crucial in quantum computation.
Pipeline Signed Japanese Translation Focusing on a Post-positional Particle Complement and Conjugation in a Low-resource Setting

Because sign language is a visual language, the translation of it into spoken language is typically performed through an intermediate representation called gloss notation.In sign language, function words, such as particles and determiners, are not explicitly expressed, and there is little or no concept of morphological inflection in sign language.Therefore, gloss notation does not include such linguistic constructs.Because of these factors, we argue that sign language translation is effectively processed by taking advantage of the similarities and differences between sign language and its spoken counterpart.We thus propose a pipeline translation method that clearly focuses on the difference between spoken Japanese and signed Japanese written in gloss notation.Specifically, our method first uses statistical machine translation (SMT) to map glosses to corresponding spoken language words.We then use three transformer-based seq2seq models trained using a large out-ofdomain monolingual Japanese corpus to complement postpositional particles and estimate conjugations for the verbs, adjectives, and auxiliary verbs in the first translation.We apply the seq2seq models in sequence until the translation converges.Our experimental results show that the proposed method performs robustly on the low-resource corpus and is +4.4/+4.9 points above the SMT baseline for BLEU-3/4.
GO Hessian for Expectation-Based Objectives

An unbiased low-variance gradient estimator, termed GO gradient, was proposed recently for expectation-based objectives E_q_γ(y) [f(y)], where the random variable (RV) y may be drawn from a stochastic computation graph (SCG) with continuous (non-reparameterizable) internal nodes and continuous/discrete leaves. Based on the GO gradient, we present for E_q_γ(y) [f(y)] an unbiased low-variance Hessian estimator, named GO Hessian, which contains the deterministic Hessian as a special case. Considering practical implementation, we reveal that the GO Hessian in expectation obeys the chain rule and is therefore easy-to-use with auto-differentiation and Hessian-vector products, enabling efficient cheap exploitation of curvature information over deep SCGs. As representative examples, we present the GO Hessian for non-reparameterizable gamma and negative binomial RVs/nodes. Leveraging the GO Hessian, we develop a new second-order method for E_q_γ(y) [f(y)], with challenging experiments conducted to verify its effectiveness and efficiency.
Pelatihan Pembuatan Media Pembelajaran Daring Bagi Guru SMA Menggunakan Open Broadcaster Software

This community service program carries out training on optimizing the use of Open Broadcaster Software (OBS) in online learning, primarily due to the Covid-19 pandemic. The training is given to 16 participants of SMAIT Abu Bakar Yogyakarta teachers. This training aims to introduce supporting media that can be used in the online teaching and learning process more interactively and present it live streaming via YouTube. In the pre-test, 69% of participants had never been familiar with the term OBS. Only 19% have observed people using OBS; in fact, none (0%) have ever used OBS. After the training, participants gained additional knowledge/insight about OBS, and 94% agreed that the training that had been provided made learning more structured about OBS. Participants stated that 100% agreed that OBS could be further optimized for learning. The enthusiasm of the participants can be seen from after the training. 100% of the participants will participate again if this kind of training is held
"Living in a Material World," Contemplating the Immaterial One—Musings on What Sounds Can Actually Tell Us, or Not

One-Musings on What Sounds Can Actually Tell Us, or Not
Workshop on Explainable User Models and Personalized Systems (ExUM 2021)

Adaptive and personalized systems have become pervasive technologies that are gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interact every day with algorithms that help us in several scenarios, ranging from services that suggest us music to be listened to or movies to be watched, to personal assistants able to proactively support us in complex decision-making tasks.
Towards a user-friendly tool for automated sign annotation: identification and annotation of time slots, number of hands, and handshape

The annotation process of sign language corpora in terms of glosses, is a highly labor-intensive task, but a condition for a reliable quantitative analysis. During the annotation process the researcher typically defines the precise time slot in which a sign occurs and then enters the appropriate gloss for the sign. The aim of this project is to develop a set of tools to assist the annotation of the signs and their formal features in a video irrespectively of its content and quality. Recent advances in the field of deep learning have led to the development of accurate and fast pose estimation frameworks. In this study, such a framework (namely OpenPose) has been used to develop three different methods and tools to facilitate the annotation process. The first tool estimates the span of a sign sequence and creates empty slots in an annotation file. The second tool detects whether a sign is one- or two-handed. The last tool recognizes the different handshapes presented in a video sample. All tools can be easily re-trained to fit the needs of the researcher.
DISTORY: PENGEMBANGAN KAMUS DIGITAL SEJARAH BERBASIS VISUAL BASIC PADA MATERI KERAJAAN TARUMANEGARA

The transformation of learning in the 21st century requires an adjustment between the development of science and technology. Learning sources are part of the learning system that is transformed from conventional learning sources into digital-based learning sources. Digital-based learning sources are considered more practical than conventional learning sources. Digital-based learning sources can be applied to history, which many students get bored easily when studying. Therefore, the aim of this study is to develop a digital-based learning source for history that makes it easier for students to study it. Digital History Dictionary (Distory) is an application of a digital-based learning source that has been developed based on learning requirement analysis. Distory also can make it easier for students to find and learn history. The main purpose of the development of Distory is an innovation of digital-based historical learning sources that is economical, practical, flexible, and appropriate with learning objectives. The research methodology used in the research and development of Distory, was based on Sugiyono’s research model, namely: (1) potential and problems; (2) data collection; (3) product design; (4) design validation; (5) design revision; (6) small class trials; (7) product revision; (8) large class trials; (9) product revision; and (10) mass production. Distory is validated by experts as a basis for product improvement before field trials are carried out. Small and large class trials were carried out on the students of Madrasah Aliyah Negeri (MAN) Batu. The results of the validation of media experts, small class trials, and large class trials were 85 percent, 88.75 percent, and 89 percent, respectively. Based on these results, the development of Distory can be categorized as very decent and ready to be implemented.
Reducing Neural Network Parameter Initialization Into an SMT Problem (Student Abstract)

Training a neural network (NN) depends on multiple factors, including but not limited to the initial weights. In this paper, we focus on initializing deep NN parameters such that it performs better, comparing to random or zero initialization. We do this by reducing the process of initialization into an SMT solver. Previous works consider certain activation functions on small NNs, however the studied NN is a deep network with different activation functions. Our experiments show that the proposed approach for parameter initialization achieves better performance comparing to randomly initialized networks.
Empirical Comparison of the Effects of Symmetrical and Asymmetrical Video Game Console Controllers on Players Performance

This study utilized an experiment to evaluate the effect of video game console controllers, namely Sony PlayStation 3 (symmetrical type) and Xbox 360 (asymmetrical type), on players’ performance. Twelve players were randomly chosen to perform tasks with both controllers. They were all males, their age ranged between 25 and 39 years with an average age of 30.75 years. All were right-handed. Each controller was tested when players were performing tasks consisting of playing selected levels of “Need for Speed” game. The first task is to complete a race on easy mode as a training task, and the second task is to complete a race on normal mode as the main experimental task. Player’s completion time of the second race and the number of errors were used as performance measures. Errors were divided into two types: number of crashes and number of wrong turns. Result of the experiment showed that there were significant differences between the two types of console controllers in completion time and number of errors. The asymmetrical controller (the Xbox 360) resulted in better performance than the symmetrical controller (PlayStation 3) did.
Edukasi Pengenalan Huruf Hijaiyah dengan Memanfaatkan Teknologi Augmented Reality

Augmented Reality (AR) technology provides opportunities for science and engineering. AR also has great opportunities in the world of education, which is to provide and display additional information in the form of 3D objects, video, sound, and text on an object. Smartphone software developers have developed Augmented reality technology that was previously developed on PC devices where this technology utilizes the existing camera on a smartphone. With this situation, AR technology has the opportunity to be used in the development of a media for recognizing hijaiyah letters, so that children will be happier learning because of its attractive appearance and teachers or parents can more easily teach lessons to their children. The way to use it is as follows: first, the user puts a registered and printed marker, second, the smartphone camera identifies (tracking) the marker. If the marker is invalid, the user repeats the identification process. If the marker is valid and identified, the marker will display the hijaiyah letter object in three-dimensional form. Third, users can understand the shape and pronunciation of hijaiyah letters by touching the virtual button on the marker
A Survey on Trust Metrics for Autonomous Robotic Systems

This paper surveys the area of “Trust Metrics” related to security for autonomous robotic systems. As the robotics industry undergoes a transformation from programmed, task oriented, systems to Artificial Intelligence-enabled learning, these autonomous systems become vulnerable to several security risks, making a security assessment of these systems of critical importance. Therefore, our focus is on a holistic approach for assessing system trust which requires incorporating system, hardware, software, cognitive robustness, and supplier level trust metrics into a unified model of trust. We set out to determine if there were already trust metrics that defined such a holistic system approach. While there are extensive writings related to various aspects of robotic systems such as, risk management, safety, security assurance and so on, each source only covered subsets of an overall system and did not consistently incorporate the relevant costs in their metrics. This paper attempts to put this prior work into perspective, and to show how it might be extended to develop useful systemlevel trust metrics for evaluating complex robotic (and other) systems.
Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances.

The NASA Planetary Data System hosts millions of images acquired from the planet Mars. To help users quickly find images of interest, we have developed and deployed content-based classification and search capabilities for Mars orbital and surface images. The deployed systems are publicly accessible using the PDS Image Atlas. We describe the process of training, evaluating, calibrating, and deploying updates to two CNN classifiers for images collected by Mars missions. We also report on three years of deployment including usage statistics, lessons learned, and plans for the future.
CONTENT OF A SPECIAL COURSE ON HIGH PERFORMANCE COMPUTING

At present, along with the dynamic development of computer technology in the world, the most effective ways of solving problems of practical importance are being considered. High performance computing takes the lead in this. Therefore, the development of modern society is closely related to the training of experienced, modern specialists in the field of information technology. This, in turn, depends on the inclusion of new courses in the curriculum and full coverage of these issues in the content of the taught courses. This article analyzes the courses on high performance computing, taught at experimental bases and abroad, on the basis of this, the topics of the special course and the content recommended for implementation in the educational process are determined. During the training, the competencies of students in high performance computing were identified.
N. I. Levitsky’s Letter to A. I. Lysakovsky about the Ministry of Foreign Affairs Representatives in the Petrograd Military Censorship Commission (1916)

The article publishes a letter from the chairman of the Petrograd Military Censorship Commission, Nikolai Ivanovich Levitsky, to the manager of the Press and Information Department of the Ministry of Foreign Affairs, Alexander Iosifovich Lysakovsky (dated December 22, 1916). In the letter, Levitsky insists on the need to include representatives of the Ministry of Foreign Affairs in the military censorship commission on a permanent basis. At the time, a different scheme was in effect: Levitsky sent diplomatic materials (newspaper and magazine articles, books, pamphlets, and cartoons) for verification to the Press and Information Department, which made its decision on their further fate. Levitsky pointed out the complexity and slowness of this scheme, which led to delays in printing, dissatisfaction of authors and editors. The main problem, Levitsky acknowledged, was insufficient competence of censors in foreign policy matters. Meanwhile, by the end of 1916, the topic of diplomatic censorship had become quite relevant. As the war drew to a close, more and more material appeared in press about the post-war world order and Russia's future relations with its allies and adversaries. Against this background, Levitsky advocated the inclusion of representatives of the Ministry of Foreign Affairs as censors. The idea itself was not new: in the autumn of 1916, the headquarters of the Northern Front had submitted a project to reform the Petrograd military censorship in order to improve its efficiency. The main focus of the project was on expanding its staff and creating new departments in the Petrograd Military Censorship Commission. The representative of the Ministry of Foreign Affairs was also to be included in the commission. Levitsky’s proposals followed the emerging trend: enhanced staffing and centralization of the military censorship, closer interaction of departments in order to increase its effectiveness. Lysakovsky approved the suggestion. Since January 1917, five officials of the ministry had been sent to the Petrograd Military Censorship Commission for daily and round-the-clock viewing of the press. However, this cooperation did not last: after the February Revolution, Foreign Ministry officials were released from the censorship work following the actual cessation of the preliminary censorship of press in Petrograd. Nevertheless, the unrealized project became a harbinger of future organization of press censorship after the Bolshevik’s assumption of power. The published document shows that the February Revolution was only a break in the process of censorship centralization and strengthening control over the press by the Russian state.
Rewriter-Evaluator Architecture for Neural Machine Translation

A few approaches have been developed to improve neural machine translation (NMT) models with multiple passes of decoding.However, their performance gains are limited because of lacking proper policies to terminate the multi-pass process.To address this issue, we introduce a novel architecture of Rewriter-Evaluator.Translating a source sentence involves multiple rewriting passes.In every pass, a rewriter generates a new translation to improve the past translation.Termination of this multi-pass process is determined by a score of translation quality estimated by an evaluator.We also propose prioritized gradient descent (PGD) to jointly and efficiently train the rewriter and the evaluator.Extensive experiments on three machine translation tasks show that our architecture notably improves the performances of NMT models and significantly outperforms prior methods.An oracle experiment reveals that it can largely reduce performance gaps to the oracle policy.Experiments confirm that the evaluator trained with PGD is more accurate than prior methods in determining proper numbers of rewriting.
THE ALZHEIMER’S DISEASE IMPACT ON ARTIFICIAL NEURAL NETWORKS

The Alzheimer’s Disease main impact on the brain is the memory loss effect. Therefore, in the “neuron world” this makes a disorder of signal impulses and disconnects neurons that causes the neuron death and memory loss. The research main aim is to determine the average loss of signal and develop memory loss prediction models for artificial neuron network. The Izhikevich neural networking model is often used for constructing neuron neural electrical signal modeling. The neuron model signal rhythm and spikes are used as model neuron characteristics for understanding if the system is stable at certain moment and in time. In addition, the electrical signal parameters are used in similar way as they are used in a biological brain. During the research the neural network initial conditions are assumed to be randomly selected in specified the working neuron average sigma I parameters range.
Designing a Tool to Estimate Software Projects Based on The Swarm Intelligence

Estimating models in software engineering are used to estimate some important and future characteristics of the software project, such as estimating the developed project effort, and that failure in the program is mainly due to wrong project management practices, so estimating the software effort is a very important step in the software management process for large projects.In this research, a software estimation tool was built to find an efficient and accurate method for estimating the effort.The average COnstructive COst MOdel COCOMO was used, which is classified as one of the best traditional methods among arithmetic estimation models.Four methods of swarm intelligence were used, the first of which is the Glowing worm Swarm Optimization GSO method and the second is the Bird Swarm Algorithm BSA and the third is the first proposed method hybrid BSA-GSO Method1 BSA-GSOM1, where the GSO and BSA algorithms were hybridized, and the performance of the third method was improved to form the fourth method, represented by the second hybrid new method, which called hybrid BSA-GSO Method2 BSA-GSOM2.The new tool was implemented with all its methods on the NASA data set and satisfactory results were obtained by the first and second swarms intelligence, and excellent results were obtained in the first proposed method, but the results of the second proposed method were better and more accurate than the previous ones.many measurements of performance were used for all the methods, the second proposed method yielded the best results from everyone.