Reviewerly/exHarmony huggingface dataset:
Depth Augmented Networks with Optimal Fine-tuning.

Convolutional neural networks (CNN) have been shown to achieve state-of-the-art performance in a significant number of computer vision tasks. Although they require large labelled training datasets to learn the CNN models, they have striking attributes of transferring learned representations from large source sets to smaller target sets by normal fine-tuning approaches. Prior research has shown that these techniques boost the performance on smaller target sets. In this paper, we demonstrate that growing network depth capacity beyond classification layer along with careful normalization and scaling scheme boosts fine-tuning by creating harmony between the pre-trained and new layers to adjust more to the target task. This indicates pre-trained classification layer holds high-level (global) image information that can be propagated through the newly introduced layers in fine-tuning. We evaluate our depth augmented networks following our designed incremental fine-tuning scheme on several benchmark datatsets and show that they outperform contemporary transfer learning approaches. On average, for fine-grained datasets we achieve up to 6.7% (AlexNet), 5.4% (VGG16) and for coarse datasets 9.3% (AlexNet), 8.7% (VGG16) improvement than normal fine-tuning. In addition, our in-depth analysis manifests freezing highly generic layers encourage better learning of target tasks. Furthermore, we have found that the learning rate for newly introduced layers of depth augmented networks depend on target set and size of new layers.
Memory-Efficient Algorithm for Copying Two-Sided Cards

This chapter is devoted to an intelligent algorithm for the processing of scanned images of various two-sided cards, such as identification, bank, or business cards. Card images differ significantly from common document images and conventional approaches developed for text document image processing do not operate well for card images. For single-page and duplex printing, our technique provides a sophisticated layout of images for both sides of a card. The method detects the location, skew angle and orientation of the card image. The orientation detection is based on Arabic digit segmentation and recognition. The classification of digits in card images is a challenging problem because cards often have a complex colour background. The digits can also be an arbitrary size, colour and typeface. For digit segmentation on a complex background, we apply the labelling of connected regions to the outcome of the edge-detection filter. The edge pixels are labelled for dark digits on a light background and vice versa, independently from each other. For the classification of digits, we construct decision trees with a high generalization capability for the recognition of various typefaces. The final decision rule for image orientation detection is based on digit recognition outcomes. Our algorithm consumes an extremely small memory volume, operates very fast and outperforms optical character recognition (OCR) applications in digit recognition and orientation detection for card images.
Penicuik property repair event

The Penicuik Heritage Regeneration Project is inviting property owners to a property repair and maintenance event.
Motivation and values of teachers in the formation and use of ict

The objective of this work is to know both the motivations and the values that move teachers to train and use ICT in educational centers with long experience and Good Practices with ICT. A quantitative research is proposed with a descriptive method and a survey design, whose instrument of data collection is a questionnaire to the teachers. After a non-probabilistic and intentional sample, a final sample of 136 teachers from 18 educational centers is reached. Among the results, it highlights a more elevated intrinsic than extrinsic motivation and high values of trust, creativity and sharing. Likewise, there are significant differences in motivations and values in the formation and use of ICTs based on a series of variables related to ICT integration plans and programs, as well as the involvement of teachers at different levels. In this way, the relevance of both ICT policies and awareness of their importance among teachers is evident
Design of Wavelet Digital Filter for Edge Detection of Medical MRI image

The aim of this study is detecting the edge of the medical MRI (Magnetic Resonance Imaging) images. This paper describes as efficient and accurate enhancement and detection method by designing 5-Tap bandpass filter signal and two high frequency sub-band MRI images in digital wavelet domain. Simulation results shows that the proposed method has high accuracy and enhancement in detecting the edge images as is compared to existing method. It provide a helpful and efficient solution for detecting disease lots of medical MRI image, and this method provide new insights in overcoming the scale sensitivity and noises in edge detection.
Pengamanan Citra Digital Menggunakan Algoritma Pohlig Hellman

Image encryption is a technique to protect the confidentiality of images from illegal access. With image encryption, people who do not know how the image is hidden will not know how to open it. Encryption is needed because in today's digital era digital images are easily stored or transmitted through public channels such as the internet. Image transmission through public channels is prone to tapping and storing images in storage media prone to access by parties without authority. For this reason, it is necessary to do a cryptographic method to secure confidential images from parties who do not have the authority to understand the actual image. This study presents an image encryption algorithm using Pohlig Hellman's Algorithm. The Pohlig Hellman algorithm is an algorithm used to compute discrete logarithms in a multiplicative group whose rank is not a safe prime number. Cryptography, Image, Pohlig-Hellman
How Loud Would It Be If Every Alexa In The World Played "Despacito" At The Same Time

The “This is so sad, Alexa play Despacito” meme has become incredibly widespread since its origin in 2018. The meme, which mocks posts of the form “This is so sad, can we get X likes”, takes the form of an instruction to Alexa, the personal assistant software by Amazon, to play the song ‘Despacito’. This paper aims to calculate, based on the physics of sound and the properties of a typical speaker, how loud the perceived volume would be if every Alexa-enabled device in the world were gathered in one place and, using the speaker for the Echo – the most common Alexa-enabled device – played the song ‘Despacito’ at the same time, assuming the sound produced by the Echo’s speakers is perfectly in phase and undergoes only constructive interference. It is calculated that the volume would be 159 dB, equivalent to a shotgun blast. The maximum distance at which this would be audible over background noise of 50 dB is then calculated using the inverse square law relating sound intensity and distance, and is found to be 28 km.
Biasing MCTS with Features for General Games

This paper proposes using a linear function approximator, rather than a deep neural network (DNN), to bias a Monte Carlo tree search (MCTS) player for general games. This is unlikely to match the potential raw playing strength of DNNs, but has advantages in terms of generality, interpretability and resources (time and hardware) required for training. Features describing local patterns are used as inputs. The features are formulated in such a way that they are easily interpretable and applicable to a wide range of general games, and might encode simple local strategies. We gradually create new features during the same self-play training process used to learn feature weights. We evaluate the playing strength of an MCTS player biased by learnt features against a standard upper confidence bounds for trees (UCT) player in multiple different board games, and demonstrate significantly improved playing strength in the majority of them after a small number of self-play training games.
Alterações emocionais em idosos com Diabetes Mellitus cadastrados na atenção básica à saúde

This study aimed to investigate emotional aspects in elderly patients with Diabetes Mellitus enrolled in primary health care. Therefore, this was a descriptive, cross-sectional study with a quantitative approach, carried out in the period from September 2017 to March 2018 in two Health Centers (FHS and UBS) of the city of Vitoria da Conquista, BA. A total of 63 elderly people enrolled in the Family Health Program of both sexes, with type I or II Diabetes Mellitus, with preserved cognitive ability, evaluated by the Mini Mental State Exam, and who agreed to participate in the study by signing the Informed Consent Form. For data collection, the following instruments were used: A economic socio-demographic questionnaire; Beck's Anxiety Scale and Geriatric Depression Scale (GDS), with the following data: of the 63 elderly people who participated in the study, 68.3% (43) were female and 31.7% (20) were male; 57.1% (36) are in the age range of 60 to 70 years; 63.5% (40) had a minimum degree of anxiety, and 74.6% (47) were suspected of depression. It is concluded that the elderly patients with Diabetes Mellitus of the present study had a low level of anxiety, but with the lowest degree of the disease. The study also showed that most individuals had a prevalence of depressive symptoms, which calls attention and at the same time suggests that promotion and prevention actions must be taken regarding primary health care with a view to promoting mental health.
Human and organisational factors in cybersecurity: applying STAMP to explore vulnerabilities

The human and organisational factors contributing to information security are still poorly understood, primarily due to a lack of research and absence of suitable techniques to assess complex digital systems. This paper presents the application of the System-Theoretic Accident Models and Process (STAMP) technique to the 2013/2014 Target Corporation data breach. The aims of the study are to investigate the causal factors using a systemic approach, and to demonstrate the benefits of the technique to information security applications. A number of critical control flaws were identified through the STAMP analysis include: i) poor external and internal communication/co-ordination of new threats and vulnerabilities; ii) inadequate learning from past events, internally and externally; iii) a lack of proactive security management to understand and learn from system successes and good practices as well as system failures; iv) ineffective management and co-ordination with the supply chain and their security systems.
PENGARUH STRATEGI GUIDED WRITING UNTUK MENINGKATKAN KETERAMPILAN MENULIS SISWA KELAS V SD NEGERI 187 PEKANBARU

This research is motivated by the low writing skills of fifth-grade students of SD Negeri 187 Pekanbaru. This is because students writing skills are still experiencing difficulties. One of them is the difficulty of writing descriptions that are in accordance with the rules of Indonesian writing. In the learning process students look less creative in developing ideas that are in themselves. This study aims to determine the differences and the effect of writing description skills between students who learn to use guided writing strategies with students who study conventionally. This type of research is quasi-experimental research with research subjects of fifth-grade students of SD Negeri 187 Pekanbaru. The results showed that there was an influence of guided writing strategies to improve the skills of writing descriptions of fifth-grade students of SD Negeri 187 Pekanbaru. This can be seen from the increase in writing description skills from the average initial test 68,82 to 76,82 in the final test with an average increase of 0,27 low categories. While the control class experienced an increase in the initial test average of 69,26 to 70,51 in the final test with an average increase of 0,12 in the low category. Guided writing strategies have an influence with a very strong level of relationship with a coefficient of determination 70,22. In other words, a guided writing strategy influences the skills of writing student descriptions.
Location Aspect Based Sentiment Analyzer for Hotel Recommender System

Recently personal recommender system has spread fast, because of its role in helping users to make their decision. Location-based recommender systems are one of these systems. These systems are working by sensing the location of the person and suggest the best services to him in his area. Unfortunately, these systems that depend on explicit user rating suffering from cold start and sparsity problems. The proposed system depends on the current user position to recommend a hotel to him, and on reviews analysis. The hybrid sentiment analyzer consists of supervised sentiment analyzer and the second stage is lexicon sentiment analyzer. This system has a contribute over the sentiment analyzer by extracting the aspects that users have been mentioned in their reviews like (cleanness, service, etc.) by using accurate parsing system built on latent semantic analysis results. The accuracy measurements of the proposed sentiment analyzer were perfect.
Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networks for Non-Uniform Single Image Deblurring.

Multi-scale approach has been used for blind image / video deblurring problems to yield excellent performance for both conventional and recent deep-learning-based state-of-the-art methods. Bicubic down-sampling is a typical choice for multi-scale approach to reduce spatial dimension after filtering with a fixed kernel. However, this fixed kernel may be sub-optimal since it may destroy important information for reliable deblurring such as strong edges. We propose convolutional neural network (CNN)-based down-scale methods for multi-scale deep-learning-based non-uniform single image deblurring. We argue that our CNN-based down-scaling effectively reduces the spatial dimension of the original image, while learned kernels with multiple channels may well-preserve necessary details for deblurring tasks. For each scale, we adopt to use RCAN (Residual Channel Attention Networks) as a backbone network to further improve performance. Our proposed method yielded state-of-the-art performance on GoPro dataset by large margin. Our proposed method was able to achieve 2.59dB higher PSNR than the current state-of-the-art method by Tao. Our proposed CNN-based down-scaling was the key factor for this excellent performance since the performance of our network without it was decreased by 1.98dB. The same networks trained with GoPro set were also evaluated on large-scale Su dataset and our proposed method yielded 1.15dB better PSNR than the Tao's method. Qualitative comparisons on Lai dataset also confirmed the superior performance of our proposed method over other state-of-the-art methods.
Building a Serious VR Game for Soft-Skill Learning – Lessons Learned

The presented game design article aims at analyzing and reporting the main features of the simulation VR game CICERO. CICERO is a serious game designed for the porpoise of teaching and learning public speaking skills in a VR environment. The game supports two modes of play practice mode and challenge mode. In the practice mode the players have to recreate the existing presentation, and in the challenge mode they can use their presentation in the game and analyze their behavior and presentation skills.  Game measures sight (gaze mechanic), speaking speed, speaking sound volume and hand gestures made by the player. In the article, we discuss the optimal setting of the game in the learning process and limitations of the VR technology in learning.
Unmasking Hate on Twitter: Disrupting Anonymity by Tracking Trolls

Author(s): Ascher, Diana L | Abstract: Notions of free speech and expectations of speaker anonymity are instrumental aspects of online information practice in the United States, which manifest in greater protections for speakers of hate, while making targets of trolling and hate speech more vulnerable. In this chapter, we argue that corporate digital media platforms moderate and manage “free speech” in ways that disproportionately harm vulnerable populations. After being targets of racist and misogynist trolling ourselves, we investigated whether new modes of analysis could identify and strengthen the ties between the online personas of anonymous speakers of hate and their identities in real life, which may present opportunities for intervention to arrest online hate speech, or at least make speakers known to those who are targets or recipients of their speech.
Dados abertos nas universidades federais: envolvimento interno e divulgação para a sociedade

The present article discusses the implementation and management of the Open Data Policy – published through decree n. 8.777/2016 – and points how it has been developed within Brazilian federal universities. This decree forces Brazilian public administration to provide open format data, in order to improve the culture of public transparency. A study was conducted in twelve universities to analyze the engagement of the academic community in the construction of the Open Data Plan, moreover to monitor the compliance of the project and the disclosure of the Open Data Policy to society, in order to verify if citizens are being informed about this subject. In this research, techniques of document analysis of open data plans and structured interviews within the sectors responsible for the management of these data were used in the investigation. According to the results, it was possible to verify that these institutions have difficulty fulfilling the policy. In some of them, the data publication schedule has not been put into practice and, in other cases, there is no open format data avaliable, which compromises the work done on this subject within these organizations. This paper highlights the importance of further studies on mapping the data policy situation in all Brazilian federal universities.
“A STUDY ON CRYPTOCURRENCY IN INDIA – BOON OR BANE”

The present study is focused on assessing the role and impact of Bitcoin. As India is in the technical era, techno-Indians speak, invest, and possess Bitcoin. This paper is an outcome of an exploratory research on the role of Bitcoin and its future in the Indian economy. The study answers the question “Is Bitcoin boon or bane in India”.
Matching Topological Structures for Handwritten Character Recognition

This work presents a locking and deforming process to accomplish the recognition. The best matched features of the template are locked to their target features of the unknown pattern. The whole template is then deformed and calibrated according to these features. Improved similarity score can be obtained from the deformed template. This work illustrates this process and its operations. This process indirectly overcomes difficult distortion problems.
Deep Learning Trackers Review and Challenge

Recently, deep learning has achieved great success in visual tracking. The goal of this paper is to review the state-of-the-art tracking methods based on deep learning. First, we categorize the existing deep learning based tr... | Find, read and cite all the research you need on Tech Science Press
Library Guides: Undergraduate Research Workshop Series at Berks: Designing & Creating a Poster

Workshop series to prepare for the HECBC Undergraduate Research & Creativity Conference (or any other conference)
LibGuides: Find It in the South Dakota State University Archives and Special Collections!: Archival Research

A list of topics found in the collections of the SDSU Archives and Special Collections Find information on how to do archival research
Getting more fiber with polymer-coated metal core

The hemocompatibility of blood-contacting medical devices remains one of the major challenges in biomedical engineering and makes research in the field of new and improved materials inevitable. However, current in-vitro test and analysis methods are still lacking standardization and comparability, which impedes advances in material design. For example, the optical platelet analysis of material in-vitro hemocompatibility tests is carried out manually or semi-manually by each research group individually.As a step towards standardization, this paper proposes an automation approach for the optical platelet count and analysis. To this end, fluorescence images are segmented using Zach’s convexification of the multiphase-phase piecewise constant Mumford–Shah model. The non-background components then need to be classified as platelet or no platelet. For this purpose, a supervised random forest is applied to feature vectors derived from the components using features like area, perimeter and circularity. With an overall high accuracy (> 93 %) and low error rates (≤ 5 %), the random forest achieves reliable results. This is supported by high areas under the receiver–operator characteristic curve (≥ 0.94) and the prediction–recall curve (≥ 0.77), respectively.We developed a novel method for a fast, user-independent and reproducible analysis of material hemocompatibility tests. The automatized analysis method overcomes the current obstacles in the way of standardized in-vitro material testing and is therefore a unique and powerful tool for advances in biomaterial research.
Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering

Tracking user reported bugs requires considerable engineering effort in going through many repetitive reports and assigning them to the correct teams. This paper proposes a neural architecture that can jointly (1) detect if two bug reports are duplicates, and (2) aggregate them into latent topics. Leveraging the assumption that learning the topic of a bug is a sub-task for detecting duplicates, we design a loss function that can jointly perform both tasks but needs supervision for only duplicate classification, achieving topic clustering in an unsupervised fashion. We use a two-step attention module that uses self-attention for topic clustering and conditional attention for duplicate detection. We study the characteristics of two types of real world datasets that have been marked for duplicate bugs by engineers and by non-technical annotators. The results demonstrate that our model not only can outperform state-of-the-art methods for duplicate classification on both cases, but can also learn meaningful latent clusters without additional supervision.
Bureaucratic Political Interpretation of the Fragmented System of Official Development Assistance(ODA): Focusing on Enactment Process of Korea’s Framework Act on International Development Cooperation

이 글은 한국의 공적개발원조(ODA) 체계 법적 근거를 마련한 국제개발협력기본법 제정에도 불구하고 ODA 총괄기관 분절화 현상이 지속되는 현상에 주목하였다. 한국 ODA 분절화 관련 기존 연구는 분절화 현상을 독립변수로 설정하고 원조효과성을 종속변수로 설정하는 경향이 강하여 안타깝게도 분절화 현상의 법적 근거인 국제개발협력기본법 제정 과정에 대한 분석이 현실화되지 못한 것이 사실이다. 이 글은 국제개발협력기본법 제정 과정에 관여한 주요 행위자, 주요 쟁점 별 각 행위자의 견해, 그리고 쟁점별 부처 간 어떠한 타협이 이루어졌는지 살펴본 후, 어떠한 이유로 인하여 새로운 ODA 기본법 탄생에도 불구하고 한국의 ODA 분절화 체계가 지속되고 있는지에 관하여 경로의존성 측면에서 설명한다.This article focuses on the continuation of Korea’s fragmented Official Development Assistance(ODA) system despite of the enactment of new ODA law, Framework Act on International Development Cooperation. Past literatures tend to treat this fragmentation as an independent variable and to set inefficiency problem caused by this fragmentation as a dependent variable. Therefore the Framework Act has got less attention even though it is the legal basis of the fragmentation. This article tries to examine who the main actors are, views of those actors on major issues, and how much compromises are made among them. Also, this article explains the present situation in which the fragmented system still continues to exist even though the new ODA law has been enacted in terms of path dependency.