Miracle-dz/UniFAR-FTUs huggingface dataset:
Supplemental Materials Hierarchy Denoising Recursive Autoencoders for 3 D Scene Layout Prediction

In this supplemental material, we provide more details on the network architecture (Section 1), present additional results of our method on 3D point cloud instance segmentation (Section 2), additional qualitative comparisons of our results against baselines (Section 3), a visualization of the learned 3D scene layout embedding (Section 4), and we discuss the convergence of iterative application of the VDRAE for 3D scene layout refinement (Section 5).
PENGARUH PENGGUNAAN VIDEO PEMBELAJARAN TERHADAP PENINGKATAN HASIL BELAJAR MAPEL IPA DI MIN KROYA CIREBON

ABSTRAK Penelitian ini bertujuan untuk mengetahui “Pengaruh Penggunaan Video Dalam Meningkatkan Hasil Belajar Siswa Mata Pelajaran IPA Di MI Negeri Kroya Kecamatan Panguragan Kabupaten Cirebon”. Serta untuk mengetahui respon siswa berkaitan dengan penggunaan video dalam proses belajar mengajar. Pengumpulan data dalam penelitian ini menggunakan beberapa teknik, yaitu observasi, wawancara, studi dokumentasi dan penyebaran angket, dengan populasi berjumlah 27 orang siswa sebagai responden. Kemudian data yang terkumpul dianalisis dengan menggunakan pendekatan statistik dengan perhitungan prosentase dan korelasi Product Moment. Penggunaan video pembelajaran IPA umumnya sangat diminati oleh semua siswa MIN Kroya, sebagaimana hasil penelitian yang dilakukan terhadap 27 responden yang menjadi sampel penelitian ternyata didapat 79,634% yang mengatakan sangat setuju bila pembelajaran alat pencernaan manusia dilakukan dengan menggunakan video pembelajaran, Hasil belajar siswa dengan menggunakan video pembelajaran pada materi alat pencernaan manusia di kelas V A MIN Kroya Panguragan Cirebon terhadap 27 responden yang dijadikan sampel penelitian ternyata diperoleh rata-rata hasil belajarnya mencapai 80,63. Berdasarkan perhitungan data yang dihubungkan dengan angka indeks korelasi (r) product moment yakini berada diantara 0,800-1 yang berarti dalam kategori korelasi yang sangat tinggi, sedangkan hasil perhitungan korelasi antara penggunaan video dengan hasil belajar siswa adalah 1,03 yang berarti memiliki hubungan yang sangat tinggi. Keyword : Video Pembelajaran, Respon siswa, Hasil Belajar
Proposed Approach for Enhancement of QoE to Support e-Society

In recent years, multimedia services and social media applications have become extremely popular as they have been accessed by laptop computers and mobile devices. These can be considered as basic communications functionalities required for building e-society. The heterogeneity of hardware capabilities and network environments result in big challenges for service providers in always providing users with a satisfying experience. The meaning of Quality of Experience (QoE) reflects the degree of a user’s subjective satisfaction, which can be different from the Quality of Service (QoS) concept as QoS is solely related to objective system performance metrics, such as the bandwidth, delay, and loss rate. In this work, we propose a general approach for improving QoE through QoS such as to support various e-society applications and services. Various models and parameters related to this issue are reviewed. The proposal represents a framework that considers different aspects such as classification, marking, routing, and queuing to achieve better controlling of relative QoS parameters.
Learning to Play Stratego with Convolutional Neural Networks

A considerable amount of progress has been made, especially in the last few years, on developing neural networkbased AIs for a number of strategy board games. A large subset of strategy board games have an aspect of chance or hidden information, but to date the effectiveness of convolutional neural networks on such games is mostly unexplored. In this paper we will investigate applications of CNNs to the game Stratego, a popular incomplete information game of this type, and we will compare the effectiveness of two different approaches to using neural networks within a game engine for Stratego.
Capital Leakage, House Prices, and Consumer Spending: Evidence from Asset Purchase Restriction Spillovers

This paper studies the consequences of capital leakage across the housing markets, from large rich cities to small poor cities, on house prices and consumer spending. Using exogenous policy changes Purchase Restrictions that restrict housing asset purchases in certain large Chinese cities and comparing cities with different exposures to asset purchase restriction spillovers, we find that capital leakage from large regulated cities causes a significant rise in housing prices in the neighboring small non-regulated cities. These plausibly exogenous housing boom in the nonregulated cities lead to a substantial increase in household automobile spendings. The spending responses are highly heterogeneous across household types, suggesting redistributive effects of housing market capital leakage. We find largest spending responses for locals born in the cities they reside in, no significant average responses among migrants, and a U-shaped spending profile across age groups, consistent with predictions from Favilukis and Van Nieuwerburgh (2017). Quasi-experiment estimates of MPC from housing wealth is larger than the OLS estimate because housing prices are in part driven by savings propensities in China, introducing a negative omitted variable bias in the OLS estimate. This suggests that sources of house price variations matters in understanding housing wealth effects. JEL classification: D12, D14, E21, E31, E65, G12, G18, H23, H70, R10, R20, R28, R51
Difficulties of Student Teachers in the Engineering Graphics and Design Course at a South African University: Snapshot on Sectional Drawing.

Background:Engineering Graphics and Design (EGD) is a course that teaches a medium of communication in the form of drawings. EGD is a courses taken by student teachers in the technology teacher education programmes.Materials and methods:Purposive sampling was used to select 40 students, enrolled for the Bachelor of Education: 15 students were in their third year and 25 in their second year. Questionnaires method to student teachers, classroom observation and focus group interviews with students were carried out.Results:The results showed that students have a poor EGD background; experience difficulties in understanding sectional drawing; lack understanding of sectional drawing principles; and lack knowledge of 2D/3D sectional drawing. Students perform poorly in the spatial visualization test, which is the basis for competence in EGD course. Lecturers lack adequate pedagogic practices to effect adequate understanding of sectional drawing among student teachersConclusions:It is recommended that more attention be paid to line-work and that spatial visualization exercises be emphasized during teaching, using Piaget’s perception of imagery theories. Specific subject didactics of technology subjects should be strengthened with these topics in order to prepare efficient and quality teachers of EGD and related subjects.
Supplementary Material for CVPR submission #4406

For the WDC dataset, we acquired the aerial images and the building footprints of Washington D.C. area from open data repository hosted by the Government of District of Columbia [6]. We used the aerial images taken in 1995, 1999, 2002, 2005, 2008, 2010, 2013 and 2015. The images have 16 cm resolution, and covers over 200km for each year. We automatically annotated changes in buildings by comparing the building footprints produced at different years. Specifically, for each building, we computed IoU between footprints of different years, and annotated the buildings as newly constructed or destructed if the IoU < 0.01. We conducted the annotation using the footprint pairs of 1999 and 2005, and of 2010 and 2015. Although the building footprints are provided for other years, we find that the difference between them includes many buildings that are missed in the previous year’s survey. Because they are not the true change, we decided not to use the footprints.
Integrating AR services for the masses: geotagged POI transformation platform

Purpose The purpose of this paper is to propose the platform for effective transformation of points of interests (POIs) into augmented reality (AR), specifically into the three major software tools – Junaio, Layar and Wikitude. The objective is to facilitate the creation of POIs for common users of these programs and, thus, encourage the general public to participate in the formation of a new concept of applications using AR and location-based services. Design/methodology/approach The subject of this study was analysis of methods used for POI dynamisation under the context of location-based services. This paper suggests methodology based on database format transformation. It is focused on the creation of platform for automated geotagged POI transformation into AR. Findings The research results in prototype of online platform which is capable to automatically transform geotagged POI to three major AR applications. It discusses also the model implementation of this platform in Czech national tourist authority. Research limitations/implications The paper presents a proof-of-concept of dynamisation and transformation of an unspecified number of POIs stored in a simple table database and their transformation into the AR. Practical implications Services of AR are brought for the masses to effectively dynamise tourist information. Social implications Results could make the process of multimedialising data (POIs) more suitable for masses. Originality/value This paper presents a proof-of-concept of dynamisation and transformation of an unspecified number of POIs stored in a simple table database and their transfer into the three major AR applications.
2 . 1 Earth Course Modules Description 2 . 1 . 1 Water Cycle in Nature Application

This paper describes the set-up and assessment of the Earth Course Large-Scale Technology Enhanced Learning (TEL) STEM education pilot, which is part of the European NEWTON Horizon 2020 project (www.newtonproject.eu). The pilot is carried out in two separate primary schools in Dublin, Ireland: Saint Patrick’s Boys’ National School and Corpus Christi Girls’ National School. The Earth Course Large-Scale Pilot (LSP) includes a set of educational applications, developed as part of the NEWTON project in an effort to attract students to STEM subjects, which cover a set of topics as part of four main areas: Atmosphere, Geosphere, Biosphere and Astronomy. The developed applications use various technologies and innovative pedagogical methods to achieve the learning objectives of the course and improve user learning satisfaction, including Augmented and Virtual Reality, gamification, game-based learning and problem-based learning. The modules are also suitable for students with disabilities, specifically hearing impairment. This paper describes the set-up for the Earth Course LSP, its assessment procedure and results on Demographics and Motivation and Affective State of participating learners.
THE IMPACT OF FOREIGN CAPITAL INFLOWS ON ECONOMIC GROWTH : POOLED MEAN GROUP ANALYSIS FOR DEVELOPING COUNTRIES

This study attempts to include all foreign capital inflow variables to analyze their impact on economic growth of 21 developing countries for the period of 1990 to 2013. Modern econometric techniques are applied for data analysis including panel unit root test and pooled mean group (PMG) estimation for short-run and long-run analysis. The results indicate that inflows including net external debt and net official development assistance have significantly negative impact on economic growth of developing countries, while net foreign direct investment and net remittances have positive and significant impact on economic growth in the long-run. The negative sign of error correction term shows the convergence of the variables towards equilibrium in the long-run. The study highlights the need of allocation of foreign resources effectively and efficiently.
Improved Guarantees for Correlated-PCA ( PCA when Data and Noise are Correlated )

P is an n × r matrix with orthonormal columns and r n; `t is the true data vector that lies in a low (r) dimensional subspace of R, range(P ); at is its projection into this subspace; wt is the data-dependent (correlated) noise; and vt is the uncorrelated noise component with E[`tvt] = 0. The matrices Mt are unknown. We need to estimate range(P ). We assume the following about `t, Mt. Asssumption 1. `t = Pat with at’s being r-length, zero mean, mutually independent, bounded r.v. ’s, with diagonal covariance Λ. Define λ− := λmin(Λ), λ := λmax(Λ) and f := λ + λ− . Since the at’s are bounded, we can also define a finite constant η := maxj=1,2,. rmaxt (at) 2 j
Computer Aided Detection and Classifications of Liver Cancer Using SVM

Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer Aided Diagnosis (CAD) systems plays a major role in early detection of liver disease and in reducing liver cancer death rate. This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. The liver lesions are classified as malignant and benign based on feature difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and setting.
CLASSIFICATION BASED ON THE DATASET WITH DEEP CONVOLUTIONAL GENERATED AGAINST NETWORK Technical Report

As is known to us all, Convolutional Neural Networks have been the most excellent solution for image classification challenges. From the results of DCASE 2018 [1], the Convolutional Neural Network has also achieved excellent results in the classification of acoustic scenes. Therefore, our team also adopted Convolutional Neural Network for DCASE 2019 Task 1a. In order to make the audio features are exposed more, our team used multiple Mel-spectrograms to characterize the audio, trained multiple classifiers, and finally weighted the prediction results of each classifier to make results ensemble. The performance of classifier is largely limited by the quality and quantity of the data. From the results of the technical report [2], the use of GAN to augment the data set can play a vital role in the final performance, and our team also introduced Deep Convolution GANs (DCGAN) [3] to our solution to Task 1a Challenge, Our model ultimately achieved an accuracy of 0.846 on the development set and an accuracy of 0.671 on the leaderboard dataset.
DNA SEQUENCING COMPLEMENTS MORPHOLOGICAL IDENTIFICATION OF CHIROPSOIDES FROM NAM BOR BAY, PHUKET, THAILAND

This study was conducted by collecting samples of the jellyfish Chiropsoides from Nam Bor Bay, Phuket, in southwest Thailand. Specimens were examined and classified using both morphological and molecular methods. Morphological study revealed that all specimens were Chiropsoides buitendijki. Tissues from the gastric saccule, pedalia, exumbrella and rhopalia were extracted and sequenced using nuclear 18s rRNA. The sequences were submitted to the National Center for Biotechnology Information (NCBI). The accession number is KJ135023.
A pairwise learning strategy for video-based face recognition

In recent years, large-scale datasets together with the emergence of deep learning have led to the immense success of face recognition. However, face recognition in surveillance scenarios is still challenging due to severe blur, dramatic occlusion, richer pose, and illuminations. Meanwhile, owing to the source of data and cleaning strategies, existing large-scale datasets are inevitably affected by label noise. In this paper, a pairwise learning strategy is proposed to overcome the challenges of abundant variants in video-based face recognition (VFR). In addition, an online effective example mining (OEEM) method is designed to eliminate noisy samples to force the model focus more on effective examples during training. Experimental results on LFW, COX and one selfie dataset validate the effectiveness of the proposed approach.
Visual Tracking via Reliable Memories 1

In this paper, we propose a novel visual tracking framework that intelligently discovers reliable patterns from a wide range of video to resist drift error for long-term tracking tasks. First, we design a Discrete Fourier Transform (DFT) based tracker which is able to exploit a large number of tracked samples while still ensures real-time performance. Second, we propose a clustering method with temporal constraints to explore and memorize consistent patterns from previous frames, named as “reliable memories”. By virtue of this method, our tracker can utilize uncontaminated information to alleviate drifting issues. Experimental results show that our tracker performs favorably against other stateof-the-art methods on benchmark datasets. Furthermore, it is significantly competent in handling drifts and able to robustly track challenging long videos over 4000 frames, while most of others lose track at early frames.
CLASSIFICATION OF FIELDS HAVING DIFFERENT SOIL MOISTURE CONTENT BY SVM TECHNIQUE USING BISTATIC SCATTEROMETER

In the present paper, the classification of four different fields having different soil moisture contents is done by Support vector machine (SVM) technique. The bistatic X-band scatterometer measurement were carried out for different soil moisture fields at HHand VVpolarization. The scattering coefficient was found to increase with the soil moisture content, whereas, the nature of variation was observed opposite in HHand VVpolarizations keeping soil roughness constant. The X-band bistatic scatterometer datasets are used for training and testing of the SVM. Our results show higher classification accuracy of SVM, even for the small size of the training data set. The present work confirms the classification ability of SVM technique to classify the fields having different soil moisture contents using bistatic scatterometer data.
Device based gesture recognition

Today, there is a growing interest in research and development of new human-machine interaction systems that are more natural and ergonomic for the users. The gesture recognition plays an important part of human-machine interaction systems. In this paper, we propose a survey on device based gesture recognition. The focus is done in systems that are based on accelerometers, on optical/magnetic tracking and on glove based equipments. First, we try to give a clear definition for posture and for (static/dynamic) gesture. Then, based on several papers, we describe the process for different types of approach and then we give an evaluation of them. Some applications of these technologies are also presented. Finally, as conclusion, we expose the oportunities and the issues related to these different approaches of gesture recognition.
Post-tabletting NIR analysis : A look at PAT applicability to the output end of solid dosage forms

PAT goes far beyond process analyzers and chemical analysis techniques. Indeed, PAT embodies a holistic approach to analytics to develop an understanding of the inputs and outputs of a process, and everything in between. However, much of the discussion in PAT centers on a particular analytical technique: near-infrared. A majority of current discussion with NIR's applicability to pharmaceutical manufacturing is focused on such preparative processes as granulation, drying, and blending. In this discussion, we will take a look at different considerations for NIR for the output of the manufacturing line: finished tablet analysis.
arm characteristics and farmer perceptions associated with bovine uberculosis incidents in areas of emerging endemic spread

While much is known about the risk factors for bovine tuberculosis (bTB) in herds located in high incidence areas, the drivers of bTB spread in areas of emerging endemicity are less well established. Epidemiological analysis and intensive social research identified natural and social risk factors that may prevent or encourage the spread of disease. These were investigated using a case–control study design to survey farmers in areas defined as recently having become endemic for bTB (from or after 2006). Telephone surveys were conducted for 113 farms with a recent history of a bTB incident where their officially tuberculosis free status had been withdrawn (OTFW) (cases) and 224 controls with no history of a bTB incident, matched on location, production type and the rate of endemic bTB spread. Farmers were questioned about a range of farm management strategies, farm characteristics, herd health, wildlife and biosecurity measures with a focus on farmer attitudes and behaviours such as farmers’ perception of endemicity and feelings of control, openness and social cohesion. Data generated in the telephone surveys was supplemented with existing herd-level data and analysed using conditional logistic regression. Overall, herd size (OR 1.07), purchasing an animal at a cattle market compared to purchasing outside of markets (OR 2.6), the number of contiguous bTB incidents (2.30) and the number of inconclusive reactors detected in the 2 years prior to the case incident (OR 1.95) significantly increased the odds of a bTB incident. Beef herds using a field parcel more than 3.2 km away from the main farm and dairy herds reporting Johne’s disease in the previous 12 months were 3.0 and 4.7 times more likely to have a recent history of a bTB incident, respectively. Beef herds reporting maize growing near, but not on, their farm were less likely to be case herds. Operating a closed farm in the two years prior to the case breakdown did not reduce the odds of a bTB incident. Farmers that had recently experienced a bTB incident were more likely to have implemented badger biosecurity in the previous year, but no more likely than control farms to have implemented cattle biosecurity. Case farmers felt significantly less likely to be influenced by government, vets or other farmers compared to those with no history of bTB. This suggests that alternative methods of engaging with farmers who have recently had a breakdown may need to be developed.
Object-Oriented Analysis And Design Of Research Seminar Online Registration In Higher Education

Nowadays the quality aspects of information technology (IT) implementation plays an imperative role in the case of supporting services to its related stakeholders, mainly more on the issue of an effective and efficient process. To provide a more reliable, understandable, reusable, and maintainable academic information systems, Graduate School of IPB needs to improve the quality of its IT. Development of information systems (IS) through the proper stages of analysis and design expected to create a reliable and qualified IS. This research will evaluate the effectiveness of Academic Management IS. An evaluation conducted by observing the current system, using questionnaires, and measurement using a Likert scale. Questionnaire instrument tested for its validity (Product Moment) and reliability (Cronbach's Alpha) using SPSS version 19. This study adopts an integrated database schema that IPB has developed and designing research seminar online registration prototypes using object-oriented approach for a better IS future development. Keywords : Object-orientedapproach, prototypes, Reliability Alpha Cronbach’s, Skala Likert, SPSS, Validity Product Momen