Reviewerly/exHarmony huggingface dataset:
One-Shot Video Object Segmentation Initialized with Referring Expression

One-Shot Video Object Segmentation (OSVOS) is a CNN architecture to tackle the problem of semi-supervised video object segmentation, which performs the separation of an object from the background in a frame-independent way with the aid of one manually-segmented frame. However, in the scenarios of real applications, the requirement of one manually-segmented frame would do harm to user-friendliness of a system. To tackle the problem above, we propose a video object segmentation based on referring expression (named as REVOS), which obtains the segmented frame by a referring expression (a noun phrase whose function is to identify on specific object). The main task of our method is to select the target from all candidate objects which have the highest matching score with the referring expression by using the language analysis module. Then generate the annotation of the first frame and continue to segment all the remaining frames with OSVOS. The results of experiment show that our method can achieve similar accuracy to OSVOS and more convenient and flexible for system design.
A New Trust-Based Collaborative Filtering Measure Using Bhattacharyya Coefficient

With the rapid growth of network data and demands of users, the concept of AI in recommendation system has become a hot academic topic. However, in sparse data, it is difficult for the current user to obtain his efficient neighbors and for some cold-start users, it doesn’t do anything. Therefore, we constructed a new measure of trust between users for neighborhood based on Collaborative filtering(CF) which uses a pair of users common ratings and exploits Bhattacharyya similarity to finds relevance of each pair of rated items. We also have measured the validity of the proposed model through accuracy, recall rate and F1 measures. The results show that although some recall rates will be lost, the precision is greatly improved. Overall, it achieved good results.
IMPLEMENTASI KESESUAIAN OBAT PADA PENYAKIT MENGGUNAKAN ALGORITMA APRIORI

Health is important for every living thing, especially humans. there are several things that are important in maintaining health, including eating high-energy foods and foods that have vitamins. However, sometimes the disease can not be guessed. Disease can attack our bodies, especially diabetes.Diabetes is a disease that lasts a long time or chronic, and is characterized by high blood sugar (glucose) levels or above normal values. If diabetes is not well controlled, it can cause various complications that can endanger the lives of patients. Because this disease is dangerous, so not just drugs can cure this disease. Appropriate drugs are needed to treat this disease, drugs that are suitable for this disease can be seen based on which drugs are often purchased by patients. Therefore we need a data collection algorithm / basket that has high accuracy. Apriori is one of the data collection algorithms using a basket that has a high accuracy that reaches 80%. The data we use in this study reached 1000 drug data but only 27 types of diabetes drugs that we use. and consists of 365 contractions.The purpose of this study is to recommend which drugs are most suitable for patients by finding which drugs are often purchased at Royal Prima Indonesia Hospital. The application that researchers use in this study is the WEKA application.
Local Stereo Matching Algorithm Based on CIELAB Color Space and Census Non-parametric Transformation

In order to meet the demand of stereo matching in low-texture areas and improve the matching accuracy, a stereo matching algorithm based on CIElab color space and Census non-parametric transformation is proposed in this paper. The algorithm is able to improve the traditional adaptive algorithm by selecting the absolute gray level difference in CIELab color space instead of in RGB space. Census non-parametric transformation introduced the similarity measurement method by replacing the gray value of the central pixel point with the pixel relationship between the central pixel and its neighborhood. Finally, we performed disparity post-processing for left-right consistency detection, sub-pixel enhancement and median filter. Experimental results show that the proposed algorithm can effectively improve the matching accuracy, enhance the robustness of stereo matching, and improve the blocking effect of traditional algorithms in low-texture areas.
ECEDHA and iREDEFINE: Bringing Together Academia and Industry [Pipelining: Attractive Programs for Women]

In March 2019, the 35th Electrical and Computer Engineering Department Heads Association (ECEDHA) Annual Conference and ECExpo was held in Tucson, Arizona, bringing together electrical and computer engineering (ECE) academia and industry professionals for four days of programming and networking. According to its website, this year's meeting offered department chairs and faculty a chance to discuss the most pressing issues facing ECE today, provided the Technology Innovation Track as a specialized program for lab professionals (including access to the ECE Robotics Demo Lab), joined together ECE communications to share best practices in educating the public and prospective students about the field of ECE, and catered to graduate students via targeted workshops that focused on future career paths.
Peningkatan Hasil Belajar Menggunakan Metode Discovery Learning Pada Siswa Kelas X SMA Negeri 5 Cimahi

Classroom Action Research (PTK) titled "Improving Learning Outcomes Using the Discovery Learning Method of Class X High School Students 5 Cimahi" maximizes the use of computers connected to the internet as the main learning media that aims to find out the improvement of student learning outcomes, especially in the material and work processes computer. The research subjects were students of class X IPA 7 of SMA Negeri 5 Cimahi. The instrument used in this study was a multiple choice objective test and description. The results showed that there was a significant increase in student learning outcomes after participating in learning by using the Discovery Learning method in BTIK subjects, especially in the function material and computer work processes.&#x0D; Keywords: Discovery Learning model, learning outcomes
Things at Your Desk: A Portable Object Dataset

Object Recognition has been a field in Computer Vision research, which is far from being solved when it comes to localizing the object of interest in an unconstrained environment, captured from different viewing angles. Lack of benchmark datasets clogs the progress in this field since the last decade, barring the subset of a single dataset, alias the Office dataset, which attempted to boost research in the field of pose-invariant detection and recognition of portable object in unconstrained environment. A new challenging object dataset with 30 categories has been proposed with a vision to boost the performances of the task of object recognition for portable objects, thus enhancing the study of cross domain adaptation, in conjunction to the Office dataset. Images of various hand-held objects are captured by the primary camera of a smartphone, where they are photographed under unconstrained environment with varied illumination conditions at different viewing angles. The monte-carlo object detection and recognition has been performed for the proposed dataset, facilitated by existing state-of-the-art transfer learning techniques for cross-domain recognition of objects. The baseline accuracies for existing Domain Adaptation methods, published recently, are also presented in this paper, for the kind perusal of the researchers. A new technique has also been proposed based on the activation maps of the AlexNet to detect objects, alongwith a Generative Adversarial Network (GAN) based Domain Adaptation technique for Object Recognition.
Temporal Invariant Factor Disentangled Model for Representation Learning

This paper focuses on disentangling different kinds of underlying explanatory factors from image sequences. From the temporal perspective, we divide the explanatory factors into the temporal-invariant factor and the temporal-variant factor. The temporal-invariant factor corresponds to the categorical concept of objects in an image sequence while the temporal-variant factor describes the object appearance changing. We propose a disentangled model to disentangle from an image sequence the temporal-invariant factor that is used as an object representation insensitive to appearance changes. Our model is built upon the variational auto-encoder (VAE) and the recurrent neural network (RNN) to independently approximate the posterior distributions of the factor in an unsupervised manner. Experimental results on the HeadPose image database show the effectiveness of the proposed method.
Training Object Detectors With Noisy Data.

The availability of a large quantity of labelled training data is crucial for the training of modern object detectors. Hand labelling training data is time consuming and expensive while automatic labelling methods inevitably add unwanted noise to the labels. We examine the effect of different types of label noise on the performance of an object detector. We then show how co-teaching, a method developed for handling noisy labels and previously demonstrated on a classification problem, can be improved to mitigate the effects of label noise in an object detection setting. We illustrate our results using simulated noise on the KITTI dataset and on a vehicle detection task using automatically labelled data.
Роль журналистики в развитии медиакомпетентности педагогического сообщества (на примере «Российской газеты»)

The article is devoted to the potential of journalism in the formation of media competence of teachers through the media educational activities of the media. The concept of “media education” is presented, given by the expert media community, which defines the essence and main directions of media education. An attempt is made to determine the reasons for the inefficiency of the domestic media education process. The author, exploring options for solving the problem of organizing the training process capable of forming media competencies for an audience of different categories, turns to the journalistic community, whose activities are not always a channel for a systematic and targeted media educational impact on consumers in order to develop media competence and media culture. The models of media education are analyzed: pedagogical and journalistic (interactive), their comparative characteristics are given. The author offers relevant non-traditional forms of interaction between journalistic and pedagogical communities: tutorial support for a teacher’s personal blog, participation in the creation of network pedagogical communities.
Can FDI Improve the Socio-Economic Conditions in African Countries?

Mobilising resources via foreign direct investment (FDI) has become an important channel to achieve economic progress in African countries due to a lack of domestic resource mobilisation. Along with its recent socio-economic expansion and increasing integration into the global economy, Africa has experienced major developments in inward FDI. The question then arises whether the increase in FDI inflows has contributed to the improved socio-economic conditions in African countries, in terms of providing a better quality of life and employment prospects. By empirically analysing data from the Financial Times (fDi Markets) and World Bank (Open Data) using instrumental variable (IV) estimation over 2003-2017, this study finds that FDI can contribute to improving the quality of life and employment prospects of people living in African countries. There is also evidence that the type of FDI conducted matters for job creation as well as the relative employment rates of women in Sub-Saharan Africa (SSA). Only when FDI is labour-intensive, do positive direct employment effects exist while there is no evidence of indirect spillover effects. The results from this study bring to light the skills gap that exists in the continent, in which a lack of skills prevents African countries from reaping the employment benefits associated with more skills-demanding (knowledge-intensive) FDI. Remarkably, women in SSA seem to be an exception and are actually able to gain from knowledge-intensive FDI activities brought in by multinational enterprises (MNEs).
The Effect of Using PowerPoint Toward Teaching Writing Analytical Exposition at XI Grade of SMA Negeri 3 Jambi City Academic Year 2018/2019

The purpose of this research is to find out the language learning strategies that students used and to observe their achievement based on the strategies at second grade on SMAN 8 Kota Jambi Academic Year 2018/2019. The method used in this research is descriptive qualitative. The sample of this research is XI IPS 1 consists of 36 students, in order to get the sample in this research by using simple random sampling. The data are collected by using questionnaire adapted from Oxford (1990) and documentation from English teacher. The result of this research showed that there are very high students and high students in using language learning strategies. Based on the data, the most frequency strategies that students used are metacognitive strategies are used by 13 students (36%) with the average score very high (84,3), affective strategies are used by 9 students (25%) with the average score very high (84,1),social strategies are used by 6 students (17%) with the average score very high (82,2), and cognitive strategies are used by 3 students (8%) with the average score high (78,5), while the lowest frequency strategies that students used are memory strategies used by 4 students (11%) with the average score very high (81,6). Lastly, compensation strategies are used by 1 student (3) with the score high (78). Keywords : Learning Strategies, Achievement .
Proposing an Architecture of an Intelligent Evolvable Document Generation System Based on the Normalized Systems Theory

In the current world, low evolvability of documents is a big challenge which has not been fully addressed. This paper focuses on types of documents which have mostly predefined structure, and we use them over and over. Examples of these documents are contracts, applications, legal documents or manuals. The key problem here is that the documents are not modular and evolvable. The problem of modularity and evolvability is addressed by Normalized Systems Theory. This theory is formally proven, and it has great practical results from the first application in a software area. This paper designs a way how to apply principles and recommendations from Normalized Systems Theory in the area of non-evolvable documents.
Traffic Sign Detection and Recognition for Autonomous Driving in Virtual Simulation Environment

This study developed a traffic sign detection and recognition algorithm based on the RetinaNet. Two main aspects were revised to improve the detection of traffic signs: image cropping to address the issue of large image and small traffic signs and more anchors with various scales to detect traffic signs with different sizes and shapes. The proposed algorithm was trained and tested in a series of autonomous driving front-view images in a virtual simulation environment. Results show that the algorithm performed well under good illumination and weather conditions. The drawbacks are that it sometimes failed to detect objects under bad weather conditions like snow and failed to distinguish speed limit signs with different limit values.
Research Guides: Art of STEM Competition & Exhibit: Contact Us

Homepage for the Art of STEM Competition at Kelvin Smith Library, Case Western Reserve Library.
Assembly Is Based on Logic

It’s time to rehearse some logic theory. Don’t panic, because we will look at only what we need: NOT, OR, XOR, and AND.
The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping.

Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view. Hence, Inverse Perspective Mapping (IPM) is often applied to remove the perspective effect from a vehicle's front-facing camera and to remap its images into a 2D domain, resulting in a top-down view. Unfortunately, however, this leads to unnatural blurring and stretching of objects at further distance, due to the resolution of the camera, limiting applicability. In this paper, we present an adversarial learning approach for generating a significantly improved IPM from a single camera image in real time. The generated bird'seye-view images contain sharper features (e.g, road markings) and a more homogeneous illumination, while (dynamic) objects are automatically removed from the scene, thus revealing the underlying road layout in an improved fashion. We demonstrate our framework using real-world data from the Oxford Robot-Car Dataset and show that scene understanding tasks directly benefit from our boosted IPM approach.
Improved Cluster-Minkowski Recommendation System (ICRS) for Collaborative Filtering

Recommendation systems are intelligent system which provides suggestion according to user adaptability. Recommender systems i.e., collaborative filtering and content filtering works on the basis of user profiles, extensive history of user preferences and item descriptions. This paper proposes an improved recommendation system based on clustering approach. The comparative analysis shows that the proposed system provides better results in terms of RMSE as compared to other already existing methods.
Refinement Metrics for Quantitative Information Flow

In Quantitative Information Flow, refinement ( ) expresses the strong property that one channel never leaks more than another. Since two channels are then typically incomparable, here we explore a family of refinement quasimetrics offering greater flexibility. We show these quasimetrics let us unify refinement and capacity, we show that some of them can be computed efficiently via linear programming, and we establish upper bounds via the Earth Mover’s distance. We illustrate our techniques on the Crowds protocol.
The role of eye gaze in virtual characters on their perceived believability, social presence and the application of Theory of Mind

Making virtual characters seem sentient is the main goal of the study of believable virtual characters. Unbelievable behaviour, especially eye gaze behaviour, could make the whole virtual character seem less believable. In human-human social interactions eye gaze is used to interact non-verbally. People also apply Theory of Mind (ToM) to reason about other people's mental states. If a virtual character were to respond to a person's gazing behaviour with its own gazing behaviour, will it seem more believable and socially present to this person? Do people look at- and follow the gaze of such virtual characters more? Do people apply ToM to reason about the mental states of such virtual characters? This study employed a within-subject design (n=24) with two conditions that were given at the same moment. The conditions were represented by two virtual characters where one had gaze aware behaviour and the other did not. Using a think-aloud procedure during and questionnaires after the Virtual Reality experience the believability, social presence and the application of ToM were assessed. Gaze behaviour with respect to the virtual characters was assessed as well using the onboard eye-tracker of the Virtual Reality Head Mounted Display. The results suggest that virtual characters were more believable and socially present when they respond to people's gaze behaviour through their own gaze behaviour compared to a virtual character without such gaze behaviour. Results also showed that in some situations people looked more at the virtual character without gaze aware behaviour than at the virtual character with such behaviour. However, gaze following behaviour and whether or not people apply ToM were inconclusive. In conclusion, virtual characters that respond to people's gaze behaviour through their own gaze behaviour are perceived as more believable and more socially present than virtual characters without such gaze aware behaviour.
Breast Cancer Screening (BCS) by Mammography Tomosynthesis

Abstract Issue Breast cancer is the 2nd cause of death for women in Portugal and 6000 new cases are detected annually. The prognosis is better for women whose cancers are detected earlier. Description Algarve BCS begin in 2005 using a digital mammography with 2 incidences/breast with double reading and consensus by a 3rd radiologist. Each 2 years women age 50 to 69, are invited to a Mobile Unit to do the mammography, a partnership with Algarve Oncologic Association. In 2017, direct digital mammography with Tomosynthesis(DDMT)was implemented. This technique increase cancer detection, reduction of false positives, false negatives and radiation dose. Diagnostic accuracy is better and also improves accessibility to the most vulnerable population groups to this technology. Results BCS covers 100% of the region and the mobile unit is user friendly for women with mobility impairment. Adhesion rate is &amp;gt; 62%. Is the 1st screening using Tomosynthesis in Portugal.Preliminary results show that the confirmation rate increased (10 %), the detection rate per 1.000 women also increased and the positive predictive value &amp;gt;95%. Costs associated with the program decrease and less number of women called for false positives is observed. The number of positive mammograms increased, due to diagnostic acuity, with better visualization of the breast, namely in the patterns of greater mammary density; which leads to an improvement in further assessment rates. Lessons The technique increases the adhesion rate, with the same human resources. The decrease of the false positives improves the number of women send to further assessment and Senology Center. Earlier diagnoses, less aggressive treatments, and a higher survival rate are also expected. Messages- Using this technology is expected that cancer diagnosis will be earlier, more reliable and wiht higher survival rate for this disease. The possibility of a population based screening with DDMT is an opportunity to reduce inequalities in the region. Key messages Mammography Tomosynthesis is expected that cancer diagnosis will be earlier, more reliable and wiht higher survival rate for this disease. The possibility of a population based screening with Mammography Tomosynthesis is an opportunity to reduce inequalities in the region.
Jumping and Looping

You will agree that a visual debugger such as DDD is quite useful, especially for investigating large programs. In this chapter, we will introduce SASM (for SimpleASM). It is an open source, cross-platform integrated development environment (IDE). It features syntax highlighting and graphical debugging. It’s a fantastic tool for an assembler programmer!
SmartBoard Activities for College Writing Classes

The “SmartBoard (SB) Activities for College Writing Classes” was created for Leeward CC composition courses. The activities incorporate the use of the SmartBoard Interactive Display and Learning Suite Software.
LibGuides: #eBooksForAll Petition: Resources for Libraries

Tell Macmillan Publishers that you demand #eBooksForAll Is your local library interested in getting involved in this movement? Check out the steps below to see how we have taken action.