Reviewerly/exHarmony huggingface dataset:
Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images

As one of the most commonly ordered imaging tests, the computed tomography (CT) scan comes with inevitable radiation exposure that increases cancer risk to patients. However, CT image quality is directly related to radiation dose, and thus it is desirable to obtain high-quality CT images with as little dose as possible. CT image denoising tries to obtain high-dose-like high-quality CT images (domain Y ) from low dose low-quality CT images (domain X ), which can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain X (noisy images) and a target domain Y (clean images). Recently, the cycle-consistent adversarial denoising network (CCADN) has achieved state-of-the-art results by enforcing cycle-consistent loss without the need of paired training data, since the paired data is hard to collect due to patients’ interests and cardiac motion. However, out of concerns on patients’ privacy and data security, protocols typically require clinics to perform medical image processing tasks including CT image denoising locally (i.e., edge denoising). Therefore, the network models need to achieve high performance under various computation resource constraints including memory and performance. Our detailed analysis of CCADN raises a number of interesting questions that point to potential ways to further improve its performance using the same or even fewer computation resources. For example, if the noise is large leading to a significant difference between domain X and domain Y , can we bridge X and Y with a intermediate domain Z such that both the denoising process between X and Z and that between Z and Y are easier to learn? As such intermediate domains lead to multiple cycles, how do we best enforce cycle- consistency? Driven by these questions, we propose a multi-cycle-consistent adversarial network (MCCAN) that builds intermediate domains and enforces both local and global cycle-consistency for edge denoising of CT images. The global cycle-consistency couples all generators together to model the whole denoising process, whereas the local cycle-consistency imposes effective supervision on the process between adjacent domains. Experiments show that both local and global cycle-consistency are important for the success of MCCAN, which outperforms CCADN in terms of denoising quality with slightly less computation resource consumption.
The Forecasting of a Traffic Accident in the Pandemic of Covid-19

Speaking of the increase in traffic accidents, transportation safety plays an in important role. This study aims to analyze the number of traffic accidents in Makassar City. On the other hand, the implementation of Large-Scale Social Restrictions (LSSB) to prevent the spread of Covid-19 has an impact on transportation movements in Makassar City. According to this, the traffic accident data used is accident data for 2016 to 2020, which the data for 2020 is data on traffic accidents affected by the LSSB policy. From the results of data analysis, it is known that the accident rate trend tends to follow seasonal patterns so that the model used is the SARIMA Model. Sarima's model with a period (1,1,1) (1,1,1)6 is assumed to be the best model with a MAPE value of 81.6%. Based on the parameters generated by this model, it shows that the number of accidents in 2021 has decreased significantly. This is due to the government policy against the spread of the Covid-19 virus, which is implementing the PSBB. This model cannot be utilized in forecasting for a long period. This is because a long period can cause large estimates that fluctuate in value and even have a tendency for the accident rate to be negative.
University of Amsterdam at CLEF 2020 : Notebook for the Touché Lab on Argument Retrieval at CLEF 2020

This paper documents the University of Amsterdam’s participation in CLEF 2020 Touche Track. This is the first year this track has been introduced at CLEF, and we were attracted to participate in it due to its potentialities for Parliamentary debates we are currently working on. This track consists of two tasks: Conversational Argument Retrieval and Comparative Argument Retrieval. We submitted a run to both tasks. For the first task, we used a combination of the traditional BM25 model and learning to rank models. BM25 model helps to retrieve relevant arguments, and learning to rank model helps to re-rank the list and put stronger arguments on top of the list. For the second task, Comparative Argument Retrieval, we proposed a pipeline to re-rank documents retrieved from Clueweb using three features: PageRank scores, web domains, and argumentativeness. Preliminary results on 5 queries have shown that this heuristic pipeline may help to achieve a balance among three important dimensions: relevance, trustworthiness, and argumentativeness.
Hierarchical Infinite Relational Model

This paper describes a new probabilistic generative model for noisy, sparse, and heterogeneous relational data called the hierarchical infinite relational model (HIRM). Given a set of relations defined over a collection of domains, the model first infers multiple non-overlapping clusters of relations using a top-level Chinese restaurant process. Within each cluster of relations, a Dirichlet process mixture is then used to partition the domain entities and model the probability distribution of relation values. The HIRM generalizes the standard infinite relational model and can be used for a variety of data analysis tasks including dependence detection, clustering, and density estimation. We present new algorithms for fully Bayesian posterior inference via Gibbs sampling. We illustrate the efficacy of the method on a density estimation benchmark of twenty object-attribute datasets with up to 18 million cells and use it to discover relational structure in real-world datasets from politics and genomics.
An Optimization and Generalization Analysis for Max-Pooling Networks

Max-Pooling operations are a core component of deep learning architectures. In particular, they are part of most convolutional architectures used in machine vision, since pooling is a natural approach to pattern detection problems. However, these architectures are not well understood from a theoretical perspective. For example, we do not understand when they can be globally optimized, and what is the effect of over-parameterization on generalization. Here we perform a theoretical analysis of a convolutional max-pooling architecture, proving that it can be globally optimized, and can generalize well even for highly over-parameterized models. Our analysis focuses on a data generating distribution inspired by pattern detection problem, where a ``discriminative'' pattern needs to be detected among ``spurious'' patterns. We empirically validate that CNNs significantly outperform fully connected networks in our setting, as predicted by our theoretical results.
Pengaruh Pembebanan Pada Perhitungan Susut Umur Transformator Daya Nomor 4 di Gardu Induk 150 kV Serpong

The power transformer at the substation serves to reduce the transmission voltage to a medium voltage while simultaneously delivering electrical power. Therefore the power transformer is strived to live longer and operate longer. Factors causing reduced age of transformer use include ambient temperature and loading patterns, where the greater the transformer loading the greater the heat arising on the windings which can damage the insulation where the insulation life is the basic life of the transformer. The purpose of this study was to determine how the effect of loading on the age loss of power transformer number 4 at the 150 kV Serpong substation. The data used is the transformer loading data in the highest month in 2018, namely in November. From the results of this study the remaining life of transformer 4 can be for 16.40 years. Based on the heat caused by the imposition of a distribution transformer, it can be analyzed to estimate the age of the transformer losses.
Study on Codon Modulo Cryptosystem for Privacy Preservation of Vertically Partitioned Outsourced Data

Data passes crossways the cloud by the methods of assorted way. It is fundamental to safeguard the data from unapproved users to access the information in any structure. The information refuge is guaranteed by changing a plain text into an incomprehensible configuration by encoding text utilizing cryptographic calculations and these techniques are espoused for scrambling the text to made sure about their data from aggressors to guarantee data protection. Deoxyribo Nucleic Acid (DNA) is an encryption method utilized to provide security to the distributed computing data. In this paper Codon Modulo Cryptography-based Algorithm for vertically partitioned cloud data is applied to provide security for the transactional data. Affiliation Rule Mining and Frequent Itemset strategies are applied to aggregate the Association Rules among the Frequent Items in a scrambled exchange of vertically partitioned information base. The exhibition of Rule Mining calculations such as Apriori, FP-Growth and Eclat with the proposed Codon Modulo algorithm is contrasted with the conventional Homomorphic Encryption. The result exhibits that the proposed algorithm outperforms the Homomorphic Encryption in performance with a high-security level.
Knowledge Discovery in Cloud-Computing Environment and the Challenges

Today, in the area of telecommunication, social media, the internet of things (IoT), and the virtual world, enormous amounts of data are being generated which are extracted to discover knowledge. Knowledge discovery from data in the cloud-computing environment entails the extraction of the new and necessary information from the large and complex datasets. This study is qualitative and exploratory in nature. To review based on the recent literature, the articles published in the last five years (2014-2018) were searched. Different databases were searched using the keywords: ‘Knowledge management’ or ‘Knowledge discover*’ and ‘Cloud computing.'. The literature review section is divided into three sub-section based on the findings. The first two sub-sections present the data security and data privacy concerns under two main techniques (Big data analytics and machine learning) used in knowledge discovery, and the last sub-section presents various protocols proposed to address the related security and privacy concerns. This review consolidates the related data security and privacy challenges under two techniques used for knowledge discovery in a cloud environment. Also, the review consolidates the proposals proposed by different experts to address the data security and privacy concerned
Real-time Obstacle Detection and Object Tracking using Machine Learning Techniques

Identify the real-time obstacle that plays a key role in everyday life. With the use of a camera, we attempted to identify many obstacles in a single frame in this paper. In this paper, we use classifiers to classifies objects: K-nearest neighbor (KNN), random forest, and decision tree. Determine if the obstacle is static or dynamic using the above classifiers, then compare the results, and also if an obstacle is dynamic then object tracking process can be done.
ATUAÇÃO DO FARMACÊUTICO FRENTE A PANDEMIA DO COVID-19 NA CIDADE DE VALPARAÍSO DE GOIÁS - GO

Objective: to analyze the level of tolerance in friendship relationships among health professionals during the COVID-1Objective: to demonstrate the difficulties encountered by pharmacists in the face of the coronavirus pandemic; check if there are measures that support pharmacists; seek possibilities for a safe performance of professionals in times of pandemic. Method: This is a bibliographic approach, where the study in question will use, in conjunction with the method, the field research in which information will be collected for 20 pharmacists through a sociodemographic questionnaire; a semi-structured questionnaire that had made it possible to analyze the day-to-day life of pharmacists from various care units, the research base will be through the analysis of scientific works related to this theme, using the MedLine, Scielo database. Results: In view of the theme studied, the results presented refer to content collected from the application of a questionnaire to 10 pharmacists, in order to raise the difficulties encountered by professionals in the face of the coronavirus pandemic. Conclusion: Through the investigation, it is concluded that professionals in the pharmaceutical field had to adapt in a record way to be able to meet the demands and adaptations imposed by the Ministry of Health against the coronavirus. Therefore, it is interesting to emphasize the need for studies related to changes in the population’s behavior and the accurate knowledge about the ways of prevention and care with the Covid-19 virus.
Maximal Ancestral Graph Structure Learning via Exact Search

Generalizing Bayesian networks, maximal ancestral graphs (MAGs) are a theoretically appealing model class for dealing with unobserved variables. Despite significant advances in developing practical exact algorithms for learning score-optimal Bayesian networks, practical exact algorithms for learning score-optimal MAGs have not been developed to-date. We develop here methodology for score-based structure learning of directed maximal ancestral graphs. In particular, we develop local score computation employing a linear Gaussian BIC score, as well as score pruning techniques, which are essential for exact structure learning approaches. Furthermore, employing dynamic programming and branch and bound, we present a first exact search algorithm that is guaranteed to find a globally optimal MAG for given local scores. The experiments show that our approach is able to find considerably higher scoring MAGs than previously proposed in-exact approaches.
LibGuides: Teach Yourself Tech: Zoom Video Conferencing

Free online computer and technology courses, quick tutorials, tips from Tech-Talk and more, on computer basics, popular software programs, Internet security, social media, . . .
Forming a culture of interpersonal relationships of senior preschoolers by means of art and play activities

The article is concerned with an urgent issue of art pedagogy, namely, the socialization and aesthetic development of senior preschoolers through group play activities. The authors of the article have highlighted the importance of individual and personal characteristics of children of this age when they are involved in joint creative activity and group interaction. Particular attention is paid to the culture of communication and interaction of senior preschoolers with their peers and teachers. At the same time, the study covers the issues of realizing the creative potential of senior preschoolers through art and games. A separate direction of this pedagogical research of senior preschoolers is the comprehensive personal diagnostics of such children and the corresponding typology of their personality. Based on the characteristics of each type of child, the authors have proposed the main pedagogical strategies for the formation of a culture of interpersonal relationships that can be used in various programs of aesthetic education and development of senior preschoolers. The article also highlights the corresponding typology of educational games assessed as a condition for forming scientifically grounded technologies of psychological, spiritual and moral development of senior preschoolers. The study might be of interest to a wide range of specialists.
INOVASI LAYANAN PERPUSTAKAAN SELAMA PANDEMI COVID-19 OLEH TAMAN BACA MASYARAKAT OMAH BUKU DI DESA BLONDO KABUPATEN MAGELANG

This research discusses Library Service Innovations during the Covid-19 Pandemic by the Omah Buku Community Reading Park in Blondo Village, Magelang Regency. The purpose of this study was to determine the condition of library services in the Omah Buku Reading Park before and during the Covid-19 pandemic and the innovations made in dealing with it. This research is a research that uses a descriptive qualitative research method, where data collection is carried out by means of observation and interviews. Interviews were conducted with two participants, namely between the researcher and the owner and chairman of the Omah Buku Community Reading Park. The data that has been obtained from observation and interview activities are then processed and analyzed using data analysis techniques by exploring real life, contemporary limited systems or cases or various limited systems or various cases, with detailed and in-depth data collection techniques involving multiple sources of information. as well as reporting case descriptions and research case themes to help facilitate the process of interpretation of how innovative activities during the Covid-19 pandemic at TBM Omah Buku are interpreted and linked to existing theories. Based on the data that has been collected and processed, this study shows that there is a significant difference in service aspects between the time before and during the Covid-19 pandemic. The difference in this aspect of service then becomes the cause of the emergence of innovation and actions such as running activities while adhering to health protocols, limiting the number of visitors, delivery of library material collections by volunteers to several points in the surrounding community. This innovation is expected to attract people's attitudes to pay attention to the importance of reading gardens in the community.
OPTIMALISASI PEMANFAATAN APLIKASI SENAYAN LIBRARY MANAGEMENT SYSTEM (SLiMS) PADA PERPUSTAKAAN PERGURUAN TINGGI DI KOTA PEKANBARU

The purpose of this study was to determine the optimization of the utilization of the Senayan Library Management System (SLiMS) application in the library in Pekanbaru City. The method used in this research is qualitative. The sampling technique used is purposive sampling, which is sampling intentionally based on officers who are directly involved with the required sample requirements. The results of the study indicate that from several indicators that have been determined, the utilization of the SLiMS application cannot be said to be optimal because the features in the SLiMS application have not been fully utilized. Unilak Library UPT does not take advantage of two features, namely the Inventory feature and serial publication control feature, while the UMRI Library UPT has utilized all the features but there are still some sub-features that are not utilized. Utilization of existing features in the SLiMS application at UPT UMRI Library and UPT Unilak Library is based on the needs and policies of the Library.
3D printing improved with reusable support

The problem of cooperative protection in the presence of a superior opponent is considered. The note addresses the Target–Attacker–Defender problem. The challenging case where the Attacker is faster than the Target and the Defender and also more lethal than the Defender is considered. This note presents novel cooperative strategies for the Target and Defender where they need to rendezvous and to coordinate their close range strategy in order to deny capture of the Target by the Attacker.
Implementasi The Law of Repetition untuk Meningkatkan Kompetensi Mahasiswa dalam Membuat Laporan Keuangan Berbasis SAK ETAP

This study purpose to determine the implementation of the law of repetition to improve student competencies in making financial reports based on SAK ETAP in Bengkalis State Polytechnic. The type of data used in this study is a combination of quantitative and qualitative data. The research data were obtained from Bengkalis State Polytechnic students of the Applied Public Accounting Accounting Study Program semester 6 (six). Data collection techniques in this research are interviews, documentation studies and tests. The results of this study were obtained based on the results of the tests of students of the Bachelor of Applied Accounting Program in Public Finance in the 6th semester (six) after being given training to carry out the test which had greatly improved value before being given training. The average value after being given training increased to 98.6 or if presented at 98%.
THE IMPLEMENTATION OF CURRICULUM 2013 ASSESSMENT ON STUDENTS’ WRITING SKILL AT SENIOR HIGH SCHOOL

This paper aims to describe the implementation of curriculum 2013 assessment on students’ writing skill at senior high school and the teacher’s difficulties in implementing the curriculum for assessing their students’ writing skills. A qualitative method was used in this study by using the research approaches of classroom observations, semi-structured interviews, and documentations. The study was conducted in SMAN 2 Langsa, Aceh. The participants of this study were two English teachers at the school. The findings of the study showed that the teacher had implemented the curriculum 2013 assessment on students’ writing skill as in line with the framework contained in the curriculum. Further, individual students’ factors, complex assessment, and limited teaching-learning time were considered as difficulties by the teachers in implementing the curriculum 2013 assessment.
EXPERT REVIEWS ON USING AUGMENTED REALITY IN MOBILE FLIPPED LEARNING MODULE

This study aimed to identify experts’ views on using augmented reality in the mobile flipped learning module. A mobile flipped learning module was developed with the integration of Kolb’s experiential learning theory and Brame’s model. The module was introduced to and implemented in the final year students at a private university. The researchers briefly explained the practices or steps involved in the implementation of the mobile flipped learning approach. This study used a qualitative research methodology. The iinterviews were evaluated by two panels of experts. The findings highlighted the experts’ positive remarks on the mobile flipped learning module. From the findings, the researcher found that this study has implication to educators, especially in Malaysia on using the mobile flipped learning approach and the steps proposed in the module. Further research is needed to thoroughly explore the effectiveness of implementing mobile flipped learning as compared to the traditional method.
ARCSECURE: Centralized Hub for Securing a Network of IoT Devices

As far as it is considered, IoT has been a game changer in the advancement of technology. In the current context, the major issue that users face is the threat to their information stored in these devices. Modern day attackers are aware of vulnerabilities in existence in the current IoT environment. Therefore, securing information from being gone into the hands of unauthorized parties is of top priority. With the need of securing the information came the need of protecting the devices which the data is being stored. Small Office/Home Office (SOHO) environments working with IoT devices are particularly in need of such mechanism to protect the data and information that they hold in order to sustain their operations. Hence, in order come up with a well-rounded security mechanism from every possible aspect, this research proposes a plug and play device “ARCSECURE”.
LibGuides: Making Music With Apps: Equipment / Devices

The New York Tech library has many items available for making music. However, since we've had to close the physical library during the COVID-19 pandemic, the librarians have compiled a list of alternative options that you can access from home.