Reviewerly/exHarmony huggingface dataset:
Automatic initialization for active contour models based on particle swarm optimization and application to medical images

The active contour models (ACMs) are one of the most widely used techniques in image segmentation, localization and object tracking. Although there are several existing updated versions of ACMs, in which most of the models do not converge to the desired results in images due to complex background and depends on the initial placement of contour. Among the methods based on the level set, the Local Gaussian Distribution Fitting (LGDF) Energy and the Local Binary Fitting (LBF) Energy are successful algorithms, that suffers from image contours and initial position selection, which are the considerable demerits of the models. To overcome these adversities, we present an efficient and socially-inspired population based stochastic algorithm called particle swarm contour search (PSCS) which is the modified version of particle swarm optimization (PSO) algorithm, considered to be one of the most important optimization methods in swarm intelligence. Firstly, we apply smoothing filters on image to remove high intensities noise. Secondly, we utilized the PSCS algorithm to find the dominant points around the object’s boundaries. The PSCS selects some extra points in different parts of the image rather than the required object, such points are removed in our post PSCS step by utilizing different morphological operations. Furthermore, we calculate the center position and radius of the object for initial contour with the help of points generated using PSCS. The experimental outcome of the segmentations indicate that our proposed approach is automatic and fast for its initialization and successfully segment the desire object in medical images.
Dataset Curation Beyond Accuracy

Neural networks are known to be data-hungry, and collecting large labeled datasets is often a crucial step in deep learning deployment. Researchers have studied dataset aspects such as distributional shift and labeling cost, primarily using downstream prediction accuracy for evaluation. In sensitive real-world applications such as medicine and self-driving cars, not only is the accuracy important, but also the calibration -- the extent that model uncertainty reflects the actual correctness likelihood. It has recently been shown that modern neural networks are ill-calibrated. In this work, we take a complementary approach -- studying how dataset properties, rather than architecture, affects calibration. For the common issue of dataset imbalance, we show that calibration varies significantly among classes, even when common strategies to mitigate class imbalance are employed. We also study the effects of label quality, showing how label noise dramatically increases calibration error. Furthermore, poor calibration can come from small dataset sizes, which we motive via results on network expressivity. Our experiments demonstrate that dataset properties can significantly affect calibration and suggest that calibration should be measured during dataset curation.
HOW POKEMON GO DEALS WITH COVID-19: THE LOCKDOWN CONUNDRUM

The paper reflects on the digital game Pokemon GO and its gameplay changes to comply with social restrictions that are being enforced to circumvent the global threat of COVID-19. Pokemon GO can be categorised into the group of augmented reality location-based digital games. In those, game mechanics interact with real-world objects and/or locations to create an immersive experience for the player. Pokemon GO, in its current state, is a game that heavily focuses on social interactions. This mostly consists of Raids, where players meet at dedicated locations to battle a specified enemy. Live events used to be held as well, either by the developer Niantic, or in the form of community driven player-versus-player tournaments;these interactions now have to be altered or cancelled to preserve a safe playing environment. In the text, we explain the changes that Pokemon GO had to undergo in its structure to help players with social distancing, as well as new or altered mechanics that were implemented to make this inherently social game playable even though the player is alone.
LibGuides: Current Library Services: Booking a study space

Find out what services are currently available from the Library and how they are being delivered.
EL USO DEL CORREO ELECTRÓNICO (E-MAIL) COMO BENEFICIO EN LA COMUNIDAD ESCOLAR

The objective of the following research presented here is to investigate the degree of use that the student gives to the technological tool known as electronic mail or commonly referred to as e-mail. Also, know the benefit it provides to the school society in general. The research is quantitative approach, and three groups belonging to the second year of high school were chosen in a semischolarized model (upper secondary education), it is known that the population is made up of 100 students, in this way the population formula was applied finite and known, to determine a sample of 80 people. In addition, to collect the information, a questionnaire structured by closed type variables was applied, and for the information treatment the statistical program SPSS version 19 was used, where contingency tables were created for the interpretation of the data and to present the final results. It is known that 53.9% of the total respondents have at least one registered account and only 3.8% do not have registered accounts, these being in domains such as Hotmail with 68.8% and 22.6% in Gmail. Thus, this type of service provided by email gives students a better performance in school activities.
Upphovsrätt och parodiundantaget : En analys av rätten att använda skyddat material i parodierande syfte

Parody has always been a way for people to comment and criticize society, often using other people’s copyrighted work as a basis. This has naturally led to conflicts between the maker of the parodi ...
RETRACTED: Teaching Reform of English Reading Ability Based on Big Data

Abstract With the development of the information age, the channels through which students obtain knowledge and information through reading are constantly changing. Among the four basic skills of listening, speaking, reading and writing, college English reading ability is an important part of them. This article aims to study the reform ability of English reading data based on big data. This article explains the teaching reform strategy in the context of big data, and highlights the practicality of the reform of English reading teaching. In addition, this article constructs a college English reading teaching model based on big data to improve students’ reading level. The results of the teaching experiment in this paper show that the reform of college English reading teaching based on big data helps students fully master reading cognitive strategies, cultivate their awareness of cognitive strategies, and improve their English reading level by 15%.
Patient and Nursing Professional Safety in the Face of Pandemic - COVID-19

Objective: To identify the performance of nursing professionals in relation to the essential aspects for the provision of safe care to patients infected by COVID-19 hospitalized in the emergency department. Method: A simple literature review with data collected during the peak period of the disease in real time in available databases. Discussion: COVID-19 spreads around the world and reaches almost every continent. Immediate, important measures and education of the population, training of health professionals becomes a priority. In addition to effective prevention and control measures. Conclusion and final considerations: For a pandemic it is no easy task. However, in the case of COVID-19, it seems possible, since asymptomatic cases have not played an important role in transmission. Noticeable priority of protection to health professionals, isolate in quarantine the communicators and act quickly, given the lethality of the disease and that cases increase every day.
Research on Intelligent Translation Strategy Based on Human Machine Coupling

With the development of artificial intelligence technology, machine translation plays an increasingly important role in language service industry, and traditional language service enterprises are transforming to artificial intelligence.In view of the existing problems of AI + translation, this paper analyzes the representative technology applications, summarizes the development status of AI technology, and discusses the coupling path and development prospect of AI Artificial translation and machine translation.
Model Selection in Beta Regression Analysis Using Several Information Criteria and Heuristic Optimization

In the context of generalized linear modelling (GLM), the beta regression analysis is used to estimate regression models when the dependent variable lies between (0,1). In this paper, we carried out a model selection process using several information criteria with heuristic optimization. We employed the differential evolution algorithm as a heuristic optimization method to select the best model for beta regression analysis. The results show that the alternative-type information criteria provide competitive results during the model selection process in beta regression analysis.
Optimization Tools for ConvNets on the Edge

The shift of Convolutional Neural Networks (ConvNets) into low-power devices with limited compute and memory resources calls for cross-layer strategies spanning from hardware to software optimization. This work answers to this need, presenting a collection of tools for efficient deployment of ConvNets on the edge,
Analogical Reasoning for Visually Grounded Compositional Generalization

Children acquire language subconsciously by observing the surrounding world and listening to descriptions. They can discover the meaning of words even without explicit language knowledge, and generalize to novel compositions effortlessly. In this paper, we bring this ability to AI, by studying the task of multimodal compositional generalization within the context of visually grounded language acquisition. We propose a multimodal transformer model augmented with a novel mechanism for analogical reasoning, which approximates novel compositions by learning semantic mapping and reasoning operations from previously seen compositions. Our proposed method, Analogical Reasoning Transformer Networks (ARTNet), is trained on raw multimedia data (video frames and transcripts), and after observing a set of compositions such as washing apple or cutting carrot, it can generalize and recognize new compositions in new video frames, such as washing carrot or cutting apple. To this end, ARTNet refers to relevant instances in the training data and uses their visual features and captions to establish analogies with the query image. Then it chooses a suitable verb and noun to create a new composition that describes the new image best. Extensive experiments on an instructional video dataset demonstrate that the proposed method achieves significantly better generalization capability and recognition accuracy compared to state-of-the-art transformer models.
Study on specific species of malaria in relation to haematological changes

Background: As malaria is a potential medical emergency, prompt and accurate diagnosis is criticalfor its effective management. Conventional microscopic examination of peripheral thick and thinblood smear examination remains the “gold standard” for malaria diagnosis. Material: A 2 yearprospective study from 2011 to 2013 was conducted at Navodaya Medical College Hospital andResearch Centre, Raichur. The study comprised of a total number of 150 cases positive for Malaria,collected from above mentioned centre and those referred from peripheral referral centres. Clinicalhistory regarding age, sex, nature and duration of illness were elicited. Blood sample forhaematological study was taken before starting anti-malarial drugs in all these cases. Result: Out oftotal cases, P. vivax infected cases (62%) were common than P. falciparum (34%) and mixedinfection cases [PV &amp; PF] (4%). Most common presenting symptom of cases affected by PV, PF andmixed infection were fever and chills &amp; rigors and least common symptom was cough. Mostimportant haematological changes observed were anaemia and thrombocytopenia (77.3% each) andleucopenia (31.3%). All the above mentioned changes in haematological parameters were speciesspecific and statistically significant, except for red cell indices, DLC and ESR. Conclusions: Inconclusion, changes in haematological profile can help in early diagnosis of specific species ofmalaria for timely and appropriate treatment that can reduce the severity and prevention ofadditional consequences.
Problems and solutions of online learning under an epidemic situation

Since the outbreak of the epidemic at the start of this year, colleges and universities have responded positively to the call of “Suspension of classes and non-stop learning” by the Ministry of Education, such as primary and secondary schools, and have adopted online teaching methods to organize teachers and students to teach and study. This paper mainly introduces an analysis of the main problems in online teaching mode under this background, and proposes the SPOC online course mode of “DingTalk+Chaoxing,” that is, using the Chaoxing network teaching platform to customize courses, analyze learning situations and course content, innovate online teaching design, and teaching organization. With this system teachers lead students to learn online and offline actively and then achieve good learning results.
On the Inductive Bias of a CNN for Distributions with Orthogonal Patterns

Training overparameterized convolutional neural networks with gradient based optimization is the most successful learning method for image classification. However, their generalization properties are far from understood. In this work, we consider a simplified image classification task where images contain orthogonal patches and are learned with a 3-layer overparameterized convolutional network and stochastic gradient descent (SGD). We empirically identify a novel phenomenon of SGD in our setting, where the dot-product between the learned pattern detectors and their detected patterns are governed by the pattern statistics in the training set. We call this phenomenon Pattern Statistics Inductive Bias (PSI) and empirically verify it in a large number of instances. We prove that in our setting, if a learning algorithm satisfies PSI then its sample complexity is O(d2log⁡(d)) where d is the filter dimension. In contrast, we show a VC dimension lower bound which is exponential in d. We perform experiments with overparameterized CNNs on a variant of MNIST with non-orthogonal patches, and show that the empirical observations are in line with our analysis.
자문적 영업역량이 직무성과에 미치는 영향: 제약 및 의료기기 영업사원을 대상으로

As the medical industry develops and competition intensifies, the proportion of salespeople''s competence in the profit-making activities of related companies is increasing. Recently, companies related to pharmaceuticals and medical devices are trying to establish strategies for ways to provide value to customers, away from traditional sales strategies. As a way to avoid providing biased and generalized information and to provide customers with the core values they want to convey, consultative selling competency can be considered. This study examines the relationship between pharmaceutical sales and medical device salespeople''s consultative selling competency and job performance. Through this, the purpose is to provide the implication that effective competency development and training of salespeople is important for improving job performance. For the purpose of this study, job performance was divided into team roles, organizational roles, and job roles, and the effect of consultative selling competency on job performance was examined.The empirical analysis results of this study are as follows. First, customer selection activities and the introduction of customized customer needs had a statistically significant effect on job roles and team roles. Second, customer selection activities had a statistically significant effect on organizational roles.The implications of this study are as follows. The efforts of customer selection activities to find ways to increase sales for existing customers and to devise strategies to drive long-term purchases will improve the role of pharmaceutical and medical device sales personnel. In addition, it can be said that the ability to improve job performance is to increase customer satisfaction, recognize the need for continuous purchase, and devise strategies to drive purchase.
LibGuides: Events Around Campus: Home

This guide is created for students awareness of events around campus and as a reference guide for those events
Analysis of Multicultural Education Textbook Content Feasibility in PGSD, Sarjanawiyata Tamansiswa University

Research on the feasibility of a multicultural education textbook by Dwi Wijayanti has been carried out. This study aims to determine the feasibility of the content of teaching materials, the feasibility of language aspects, and the level of readability of the Multicultural Education books used in lectures at PGSD UST. This research uses qualitative methods with a descriptive approach. The data were collected using direct observation and documentation techniques. The data collection instruments are a book content feasibility sheet and a language feasibility observation sheet. The data analysis steps are analysis of observation data (book content feasibility and language feasibility), analysis of reference book readability based on Fry Graph. The results showed that the book had met the eligibility standards for use in lectures. The results of content feasibility analysis showed that the book includes the learning outcomes set by the campus, but the book needs to write down the learning achievement indicators and concept maps. There are 2 misconceptions in chapter IV and chapter IX, as well as repetition of concepts in chapter V and chapter VII of the book. The results of the language feasibility analysis are several grammatical errors in both the Indonesian text (36 spelling errors) and the English text (10 spelling errors). The results of measuring the readability level of the Indonesian language text using fry chart analysis showed that 4 samples were included in the appropriate criteria (50%) and 4 samples entered the difficult criteria (50%). The appropriate criteria mean the readability level is suitable for students. While the difficult criteria mean the readability level is difficult for students.
Cultivating Heritage: Recuperating Dutch Colonial Architecture’s Relation to Local Community’s Practices through Heritage and Socio-Cultural Approaches in Kota Lama Semarang

“Cultivating Heritage” proposes to bridge the long-standing distance between colonial architecture that represents history of discrimination and the local community that suffers from poverty and manifold environmental issues. This project is a part of Shared Heritage Lab studio that focuses on Semarang historical area, Indonesia, the former city center during Dutch colonialization period in Indonesia. Through heritage and socio-cultural approaches, the project utilizes heritage as a tool towards sustainable socio-economy and environment with community being the center of the driving force. The project incorporates colonial heritage that has been left disconnected, decayed, and deteriorated as a part of local community’s everyday practices. It transforms NV Cultuurmaatshappij der Vorstenlanden, a former agricultural trading office that was linked to the forced labor system into a co-operative (koperasi) office that integrates, manages, and educates the surrounding community’s informal trading as well as introducing permaculture production to the city. The project implements improved traditions as the design strategy. Close-readings of the existing architecture fabrics and the community’s spatial culture result in three types of interventions that consider the reciprocity between the old and the new: 1. Preservation of the high-valued entrance, with subtle changes to establish the building’s connection to the public realm. It is done by inserting elements without damaging the existing fabrics. 2. Interlocking the office’s upper structure with local crafts and communal spatial configuration to reverse the existing segregated spaces, 3. Adding a socio-production pod that introduces permaculture practices and connecting the site’s courtyard to the immediate surroundings. On the city scale, the co-operative is connected to the urban interventions, in which river, train, and three-wheeler bike (becak) are reactivated as environmental-friendly transportations for trade. The urban interventions also include the revitalization of storage and production land to support the trading and production activities. By reintroducing familiar elements as a new layer of the existing, the project intends to let the community appropriate the shared-heritage by themselves while nurturing a sense of belonging and keeping the memory of the past. The project also contextualizes the building that was built for and designed by the Dutch in Indonesia’s social and natural environment contexts as the future embodiment of shared-heritage.
RETRACTED: Research on the Development Path of Russian Teaching Innovation in Newly-elevated Undergraduate Colleges in Contemporary Old Industrial Bases based on the Analysis of Big Data

Abstract In today’s increasingly close social interactions, foreign language teaching has become more and more important. The newly promoted undergraduate colleges in the old industrial bases are a new topic in the development of foreign language teaching. This paper analyzes the teaching principles and working methods of the old industrial bases and introduces the teaching of the newly promoted undergraduate colleges and the primary stage of Russian in the old industrial base from the analysis of big data. This has very important practical significance for the new method of teaching Russian primary stage.
End-to-End on-device Federated Learning: A case study

With the development of computation capability in devices, companies are eager to utilize ML/DL methods to improve their service quality. However, with traditional Machine Learning approaches, companies need to build up a powerful data center to collect data and perform centralized model training, which turns out to be expensive and inefficient. Federated Learning has been introduced to solve this challenge. Because of its characteristics such as model-only exchange and parallel training, the technique can not only preserve user data privacy but also accelerate model training speed. In this paper, we introduce an approach to end-to-end on-device Machine Learning by utilizing Federated Learning. We validate our approach with an important industrial use case, the wheel steering angle prediction in the field of autonomous driving. Our results show that Federated Learning can significantly improve the quality of local edge models and reach the same accuracy level as compared to the traditional centralized Machine Learning approach without its negative effects. Furthermore, Federated Learning can accelerate model training speed and reduce the communication overhead, which proves that this approach has great strength when deploying ML/DL components to real-world embedded systems.