Reviewerly/exHarmony huggingface dataset:
Breast mass classification in ultrasound based on Kendall's shape manifold

Morphological features play an important role in breast mass classification in sonography. While benign breast masses tend to have a well-defined ellipsoidal contour, shape of malignant breast masses is commonly ill-defined and highly variable. Various handcrafted morphological features have been developed over the years to assess this phenomenon and help the radiologists differentiate benign and malignant masses. In this paper we propose an automatic approach to morphology analysis, we express shapes of breast masses as points on the Kendall's shape manifold. Next, we use the full Procrustes distance to develop support vector machine classifiers for breast mass differentiation. The usefulness of our method is demonstrated using a dataset of B-mode images collected from 163 breast masses. Our method achieved area under the receiver operating characteristic curve of 0.81. The proposed method can be used to assess shapes of breast masses in ultrasound without any feature engineering.
SISTEM PAKAR MENDIAGNOSA PENYAKIT FLU BURUNG SECARA ONLINE DENGAN METODE FORWARD CHAINING

An expert system can be defined as a computer software that has a knowledge base for a particular domain and uses inference reasoning resembling an expert in solving a problem. Expert System to diagnose bird flu disease to discuss about how to create an expert system application that if able to diagnose bird flu disease and provide solutions of the disease. Where the expert system when associated with the ability of doctors in the early mediagnosa patient health conditions, can be created a computer system that is tasked to know and analyze the symptoms of illness suffered by patients to then provide direct advice to these patients. Inference technique that is done is forward tracking (forward chaining) with the method of search (Best First Search).
GANchors: Realistic Image Perturbation Distributions for Anchors Using Generative Models

We extend and improve the work of Model Agnostic Anchors for explanations on image classification through the use of generative adversarial networks (GANs). Using GANs, we generate samples from a more realistic perturbation distribution, by optimizing under a lower dimensional latent space. This increases the trust in an explanation, as results now come from images that are more likely to be found in the original training set of a classifier, rather than an overlay of random images. A large drawback to our method is the computational complexity of sampling through optimization; to address this, we implement more efficient algorithms, including a diverse encoder. Lastly, we share results from the MNIST and CelebA datasets, and note that our explanations can lead to smaller and higher precision anchors.
On road object detection using image processing

Object detection involves detecting the instances of objects from a particular class in the image. The goal of object detection is to detect all instances of objects from known class, such as people, cars or faces in an image. Object detection using haar feature based cascade classifier is an effective object detection method. It is a machine learning based approach where a cascade function is trained from lots of positive and negative images. It is used to detect the objects in other images. In this work, we propose a new application for on-road moving object detection in the goal to participate in reducing the big number of road accidents. Road moving object detection in traffic video is a difficult task. Hence, in this work, we present a system in order to control the outside car risks by detecting the different road moving objects. Experimental results indicate that suggested method object detection can be achieved with a high detection ratio.
Decision Support System for Distribution of Assistance for Fishermen with Analytical Hierarchy Process

Sei Kapitan (Kapitan River) is a village located in Kumai District, West Kotawaringin Regency, Central Kalimantan Province with a population density of 5,660 inhabitants. 25% from 5,660 people work as fishermen so do fishermen in this village). The government has provided assistance for fishermen to reduce poverty, but it isn’t in the right target. The government only provides assistance without providing standard data and criteria to determine the beneficiaries. While from the village, they chose beneficiaries based on meetings with village officials, there will be many factors recommended at the meeting. Therefore they need system that can determine and conduct the assistance to fishermen who are entitled to receive support, it is the AHP (Analytical Hierarchy Process) method. By using the system development method, called &lt;em&gt;prototypes&lt;/em&gt;, and criteria for fisheries ownership every month, types of fisheries, ship, and fishing gear ownership can be detected. This method was chosen because it can select the best alternative from several alternatives. The final result of this system is an alternative decision making that is interesting in determining the beneficiary fishermen. The future hope is that the results of decision making will be right on target and the system can be operated online.
First Advanced Mud Logging Measuruments in Russia

Summary Advanced mud logging meusuruments was used on the field where fluid contacts between gas, oil and water hardly complicated on triple combo measuruments. This technology demonstrate rebost quantitative measuruments which was proved by sampling fluid tool.
Leveraging Label Information in Representation Learning for Multi-label Text Classication

Author(s): Wu, Jiayu | Advisor(s): Wu, Ying Nian | Abstract: The thesis studies the problem of multi-label text classification, and argues that it could benefit from bringing the question into the stage of language understanding. In specific, rather than limit the use of annotated labels to providing supervision in classification only, we also rely on them as auxiliary information to guide the learning of an effective representation that is tangent to the down-stream task. Two approaches are discussed: a) learn a label-word attention layer for composition of word embedding into document vectors; b) learn a high-level latent abstraction via an auto-encoder generative model with structured priors conditional on labels. We introduce two designs of label-enhanced representation learning: Label-embedding Attention Model (LEAM) and Conditional Variational Document model (CVDM) with application on real-world datasets, in order to demonstrate their ability in promoting the classification performances with improved interpretability.
Exploration of deep resources in Taoxikeng tungsten deposit: evidence from mineralogy of concealed rock mass

Acta Geologica Sinica - English EditionVolume 93, Issue S1 p. 348-348 Session 9: Emerging techniques for deep earth explorationFree Access Exploration of deep resources in Taoxikeng tungsten deposit: evidence from mineralogy of concealed rock mass Yuqi Wang, Yuqi Wang 517129391@qq.com Chinese Academy of Geological Sciences, Beijing, ChinaSearch for more papers by this authorZhao Zheng, Zhao Zheng Chinese Academy of Geological Sciences, Beijing, ChinaSearch for more papers by this authorShanbao Liu, Shanbao Liu Chinese Academy of Geological Sciences, Beijing, ChinaSearch for more papers by this authorNaxin Guo, Naxin Guo Chinese Academy of Geological Sciences, Beijing, ChinaSearch for more papers by this authorChunli Guo, Chunli Guo Chinese Academy of Geological Sciences, Beijing, ChinaSearch for more papers by this authorPingan Wang, Pingan Wang Chinese Academy of Geological Sciences, Beijing, ChinaSearch for more papers by this author Yuqi Wang, Yuqi Wang 517129391@qq.com Chinese Academy of Geological Sciences, Beijing, ChinaSearch for more papers by this authorZhao Zheng, Zhao Zheng Chinese Academy of Geological Sciences, Beijing, ChinaSearch for more papers by this authorShanbao Liu, Shanbao Liu Chinese Academy of Geological Sciences, Beijing, ChinaSearch for more papers by this authorNaxin Guo, Naxin Guo Chinese Academy of Geological Sciences, Beijing, ChinaSearch for more papers by this authorChunli Guo, Chunli Guo Chinese Academy of Geological Sciences, Beijing, ChinaSearch for more papers by this authorPingan Wang, Pingan Wang Chinese Academy of Geological Sciences, Beijing, ChinaSearch for more papers by this author First published: 31 May 2019 https://doi.org/10.1111/1755-6724.14131AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume93, IssueS1Special Issue: Abstracts of the International Symposium on Deep Earth Exploration and Practices, 24–26 Oct 2018, Beijing, ChinaMay 2019Pages 348-348 RelatedInformation
The Use Of Games In Teaching English To Civic Education Department Students In English Tutorial Program At Muhammadiyah University Of Surakarta

The objectives of this study are to know the teaching technique of game used and the procedure of teaching using the game by tutor in teaching English Tutorial Program (ETP) at Muhammadiyah University of Surakarta. The subject of this research was 9 students of Civic Education. This research used qualitative method. The qualitative data were obtained through observation and interview. The data were analyzed through data reduction, data display and conclusion. The result of the research showed that various types of game in teaching English used by tutor in learning process were: don’t forget please game and guess and get challenge game. The procedures are: 1) giving instruction, 2) explaining the rules, 3) playing games, 4) giving feedback.
Research on Grassland Demarcation Line Extraction Based on Improved Least Squares Method

The rapid development of mobile robots has made intelligent mowing robots widely concerned. The dividing line between grassland and non-grass is an important reference for robots to carry out mowing navigation.Aiming at this problem, this paper proposes a grassland boundary line extraction algorithm based on improved least squares method based on image segmentation binary image.Firstly, the corner point in the image is detected by the Shi-Tomasi algorithm, and the approximate range of the grass boundary line is obtained. Then, the modified corner point is fitted by the improved least squares method, and finally the boundary line between the grassland and the non-grass is obtained. Finally, three straight line fitting methods were compared by experiments. The results show that the improved least squares method has the best effect and the fastest running speed.
The Effect of Co-adaptive Learning & Feedback in Interactive Machine Learning

In this paper, we consider the effect of co-adaptive learning on the training and evaluation of real-time, interactive machine learning systems, referring to specific examples in our work on action-perception loops, feedback for virtual tasks, and training of regression and temporal models. Through these studies we have encountered challenges when designing and assessing expressive, multimodal interactive systems. We discuss those challenges to machine learning and human-computer interaction, proposing future directions and research.
Responsive Web Applications Using ASP.NET MVC

Responsive web applications are essential in modern application development. Users need to be able to view the content of your web application on any device. This means that a web application needs to resize itself based on whatever device it is being viewed on.
Using Python Based Fine Tuning of [imageai + a Particle Picker] in the Context of Cryo-em Image Processing Towards Developing Novel Cryo-em Informatics Platform.

Using Python based Fine Tuning of [IMAGEAI + a PARTICLE PICKER] in the Context of cryo-EM Image Processing towards Developing Novel cryo-EM Informatics Platform. [AI Touch for Particle Picking in the cryo-EM Image Processing Application ]
Incremental fuzzy clustering based on a fuzzy scatter matrix

For clustering large-scale data, which cannot be loaded into memory entirely, incremental clustering algorithms are very popular. Usually, these algorithms only concern the within-cluster compactness and ignore the between-cluster separation. In this paper, we propose two incremental fuzzy compactness and separation (FCS) clustering algorithms, Single-Pass FCS (SPFCS) and Online FCS (OFCS), based on a fuzzy scatter matrix. Firstly, we introduce two incremental clustering methods called single-pass and online fuzzy C-means algorithms. Then, we combine these two methods separately with the weighted fuzzy C-means algorithm, so that they can be applied to the FCS algorithm. Afterwards, we optimize the within-cluster matrix and between-cluster matrix simultaneously to obtain the minimum within-cluster distance and maximum between-cluster distance. Finally, large-scale datasets can be well clustered within limited memory. We implemented experiments on some artificial datasets and real datasets separately. And experimental results show that, compared with SPFCM and OFCM, our SPFCS and OFCS are more robust to the value of fuzzy index m and noise.
Y-GAN: A Generative Adversarial Network for Depthmap Estimation from Multi-camera Stereo Images

Depth perception is a key component for autonomous systems that interact in the real world, such as delivery robots, warehouse robots, and self-driving cars. Tasks in autonomous robotics such as 3D object recognition, simultaneous localization and mapping (SLAM), path planning and navigation, require some form of 3D spatial information. Depth perception is a long-standing research problem in computer vision and robotics and has had a long history. Many approaches using deep learning, ranging from structure from motion, shape-from-X, monocular, binocular, and multi-view stereo, have yielded acceptable results. However, there are several shortcomings of these methods such as requiring expensive hardware, needing supervised training data, no ground truth data for comparison, and disregard for occlusion. In order to address these shortcomings, this work proposes a new deep convolutional generative adversarial network architecture, called Y-GAN, that uses data from three cameras to estimate a depth map for each frame in a multi-camera video stream.
Transaction cost analysis of digital innovation governance in the UK energy market

Energy markets are undergoing significant changes.Legacy systems developed around inflexible, centralized and monodirectional supplies are being replaced by flexible, distributed and bidirectional supply-and-demand systems.Where legacy systems are less entrenched, such as in decentralized renewable energy, flexibility and energy service markets, the pace of change is faster, and new technologies, business models and ideas are more likely to be tested and applied.This conceptual paper analyzes the changing governance of decentralized renewable energy, flexibility and energy services in the United Kingdom from a transaction cost perspective.Particular emphasis is placed on the impact of potentially disruptive innovations such as distributed ledgers, emerging digital technologies and big data analytics on the one hand, and the need for value creation from just and affordable decarbonization on the other.In doing so, this paper sheds light on some contradictions between current energy governance and the requirements for a decarbonized, decentralized, digitalized and democratized energy system.The paper concludes that energy governance is increasingly shaped by decentralization and digitalization, which can either facilitate or inhibit value creation through democratization (social value) and decarbonization (environmental value).
The visual association test – Extended: detecting non-credible memory performance

Saliency detection is the task of locating informative regions in an image, which is a challenging task in computer vision. In contrast to the existing saliency detection models that focus on either local or global image property, an effective salient object detection method is introduced based on joint modeling global shape and local consistency. To this end, Restricted Boltzmann Machine (RBM) is utilized to model salient object shape as global image property and Conditional Random Field (CRF), on the other hand, is adopted to achieve its local consistency. In order to obtain the final saliency map, a universal framework is introduced to combine the results of RBM and CRF. Experimental results on five benchmark datasets demonstrate that the proposed saliency detection method performs favorably against the existing state-of-the-art algorithms.
Validation of smart card for securing digital documents

In the current scenario, most of the organizations go with the theme of digitalization to encourage paperless culture. All the organizations are reforming their systems to perform all the transactions using smart card technology. Considering the Educational institutions in the category of digitalization we recommend to use this smart card technology in the place of maintaining the certificates in files in the form of hard copies. It is easy to carry pocket-sized cards which will have a certified verification number, which makes each card unique. For the process of validation of card, we use LUHN algorithms. This card will help us to handle our certificated in an easy manner.
Library Guides: Interlibrary Loan/Document Delivery/GIL-Express: ILL (ILLiad)

Information on the Resource Sharing Services available through Georgia Southern University Libraries
A aprendizagem de línguas assistida por dispositivos móveis para ultrapassar as barreiras dos alunos relativamente ao treino de pronúncia

This paper describes a qualitative investigation of learner’s perceptions of a mobile assisted language learning (MALL) tool for improving attitudes towards English pronunciation study. Our goal was to reduce the barriers to learning and improve learners’ experience. To do this, MALL was implemented via the development of an e-learning app called SOMONA. The curriculum design of the SOMONA program was based on Judy Gilbert’s Prosody Pyramid framework. The creative design was done intuitively through a series of brainstorming sessions. Simplicity, animation, bright colors and music were implemented as the key design features. Once the app passed usability testing, 20 learners were randomly selected to use the app and complete the English pronunciation course. Afterwards, they were given a survey to evaluate their attitudes towards the course material and the app as a learning tool. The objective was to determine if SOMONA was an effective way to overcome barriers to learning English pronunciation. The results showed an overwhelmingly positive response from the users and indicated that the application was indeed useful in overcoming learning barriers. More study needs to be done to determine if the app alone is effective in facilitating quantifiable improvement in English pronunciation.
Innovative Approaches in Teaching –Learning Process of Engineering Education

Before following any teaching technique, it is very important to Learn the Learner and then to implement the teaching technique based on the learner's capability.Hence, I propose Learner -centered Teaching methodology [LCTM] or Student -Centric Teaching Technique [SCTT].Understudy focused adapting, otherwise called student focused training, extensively incorporates strategies for instructing that a shift with the focal point of guidance from the teacher to the students.Learners focused learning scopes individuals interests first, recognizing students voice as key to the learning background.
De-camouflaging Chameleons: Requiring Transparency for Consumer Protecton in the Internet of Things

Information and communications technology (ICT) and the development of the so-called ‘Internet of Things’ (IoT) provide new and valuable affordances to businesses and consumers. The use of sensors, software, and interconnectivity enable very useful adaptive capabilites. However, the rapid development of so-called ‘smart devices’ means that many everyday items, including software applications, are now impenetrable ‘black boxes’, and their behaviours are not fixed for all tme. They are ‘chameleon devices’, which can be subverted for corporate deceit, surveillance, or computer crime. While aspects of the IoT and privacy have been discussed by other scholars, this paper contributes to the literature by bringing together examples of digital devices being surreptiously diverted to purposes undesired by the consumer, reconceptualising these in the context of Foucauldian governmentality theory, and setting out a variety of proposals for law reform.
Eksplorasi Tana Toraja Dalam Media Game Digital Genre Puzzle

Lost in Toraja game is an educational game with adventure-puzzle genre. The main character is Orion, who explores Tana Toraja, South Celebes, one of local area in Indonesia. Orion is a character from West Java, and his intention to Tana Toraja is looking for her missing sister when she’s on her trip there.Lost in Toraja has a playtime around 30-45 minutes. The player will become a main character, who enter the game area, and they have to find the way out. Each area has several puzzle that player has to solved. The game was made using digital technique in Unity game engine, and will be build in Android platform and Windows platform.The game’s intention is to spread Tana Toraja culture among Indonesian’s citizen itself, even into foreign country. Game is a pleasing media to have fun and leart at once.Keywords: Game, Adventure-Puzzle, 2D, Education, Tana Toraja
Internet-Based Immersive Learning (IIL): Applying Ubiquitous Web 1.0 and Web 2.0 Resources in EFL Learning

Authentic language materials are always considered ideal language acquisition resources. Today’s generation lives with the Internet, surfing over Internet resources every day. Web 1.0 and Web 2.0 provide authentic and learner-directed resources and platforms with accessible and ubiquitous features, which greatly benefits English as a Foreign Language (EFL) learners living in non-English speaking countries. Despite abundant English resources available, most EFL learners usually still rely on the Internet resources of their first language. Therefore, this study aimed to devise a course adopting an immersive approach guiding students to live in an authentic English Internet life as native speakers do, thereby promoting EFL acquisition in real life. This study involved 62 college students around low intermediate levels in Taiwan, simulating a realistic English Internet life. Nine theme-based Internet resources were selected and task-based learning was employed. A survey was conducted at class end. Results show that four out of the five thematic websites considered the easiest to navigate and comprehend were presented via Web 1.0. Regarding the top five thematic websites that the students reported to browse after taking this course were related to recreation (music, movie, and travel), living consumption (shopping), and online community interaction (social media). Usefulness and interest are two major reasons motivating them to browse English websites. 61.3% and 38.7% of the students reported that they would “Strongly recommend” and “Recommend” this course respectively, demonstrating the effectiveness of the course. Qualitative results and implications were elaborated in the study.