Reviewerly/exHarmony huggingface dataset:
Backdoor Attacks on Facial Recognition in the Physical World

Backdoor attacks embed hidden malicious behaviors inside deep neural networks (DNNs) that are only activated when a specific is present on some input to the model. A variety of these attacks have been successfully proposed and evaluated, generally using digitally generated patterns or images as triggers. Despite significant prior work on the topic, a key question remains unanswered: can backdoor attacks be physically realized in the real world, and what limitations do attackers face in executing them? In this paper, we present results of a detailed study on DNN backdoor attacks in the physical world, specifically focused on the task of facial recognition. We take 3205 photographs of 10 volunteers in a variety of settings and backgrounds and train a facial recognition model using transfer learning from VGGFace. We evaluate the effectiveness of 9 accessories as potential triggers, and analyze impact from external factors such as lighting and image quality. First, we find that triggers vary significantly in efficacy and a key factor is that facial recognition models are heavily tuned to features on the face and less so to features around the periphery. Second, the efficacy of most trigger objects is. negatively impacted by lower image quality but unaffected by lighting. Third, most triggers suffer from false positives, where non-trigger objects unintentionally activate the backdoor. Finally, we evaluate 4 backdoor defenses against physical backdoors. We show that they all perform poorly because physical triggers break key assumptions they made based on triggers in the digital domain. Our key takeaway is that implementing physical backdoors is much more challenging than described in literature for both attackers and defenders and much more work is necessary to understand how backdoors work in the real world.
A Comprehensive Review Towards Segmentation and Detection of Cancer Cell and Tumor for Dynamic 3D Reconstruction

Automated cancer cell and tumor segmentation and detection for 3D modeling are still an unsolved research problem in computer vision, image processing and pattern recognition research domains. Human body is complex three-dimensional structure where numerous types of cancer and tumor may exist regardless of shape or position. A three-dimensional (3D) reconstruction of cancer cell and tumor from body parts does not lead to loss of information like 2D shape visualization. Various research methodologies for segmentation and detection for 3D reconstruction of cancer cell and tumor were performed by previous research. However, the pursuit for better methodology for segmentation and detection for 3D reconstruction of cancer cell and tumor are still unsolved research problem due to lack of efficient feature extraction for details surface information, misclassification during training phases and low tissue contrast which causes low detection and precision rate with high computational complexity during detection and segmentation. This research addresses comprehensive and critical review of various segmentation and detection research methodologies for cancer affected cell and tumor in human body in the basis of three-dimensional reconstruction from MRI or CT images. At first, core research background is illustrated highlighting various aspects addressed by this research. After that, various previous methods with advantages and disadvantages followed by various phases used as frameworks exist in the previous research demonstrated by this research. Then, extensive experimental evaluations done by previous research are demonstrated by this research with various performance metrics. At last, this research summarized overall observation on previous research categorized into two aspects, i.e. observation on common research methodologies and recommended area for further research. Overall reviews proposed in this paper have been extensively studied in various research papers which can significantly contribute to computer vision research and can be potential for future development and direction for future research.
Ordering-Based Causal Structure Learning in the Presence of Latent Variables.

We consider the task of learning a causal graph in the presence of latent confounders given i.i.d.~samples from the model. While current algorithms for causal structure discovery in the presence of latent confounders are constraint-based, we here propose a score-based approach. We prove that under assumptions weaker than faithfulness, any sparsest independence map (IMAP) of the distribution belongs to the Markov equivalence class of the true model. This motivates the \emph{Sparsest Poset} formulation - that posets can be mapped to minimal IMAPs of the true model such that the sparsest of these IMAPs is Markov equivalent to the true model. Motivated by this result, we propose a greedy algorithm over the space of posets for causal structure discovery in the presence of latent confounders and compare its performance to the current state-of-the-art algorithms FCI and FCI+ on synthetic data.
Bagian 1: Kombinasi Metode Klastering dan Klasifikasi (Kasus Pandemi Covid-19 di Indonesia)

The purpose of this research is to combine the classification and classification methods that are part of data mining. The case raised was the number of the spread of the Covid-19 pandemic in Indonesia as of July 7, 2020 with 34 records. Data sources were obtained from Ministry of Health Data, sampled and processed from covid19.go.id and bnpb.go.id. The variables used in the study are the number of positive cases (x1), number of cases cured (x2) and number of deaths (x3) by province. The classification and classification methods used are k-medoids and C4.5. The k-medoids method works to map clusters of regions in Indonesia by province. The mapping labels used are 3 clusters: high cluster (C1 = red zone), alert cluster (C2 = yellow zone), low cluster (C3 = green zone). The results of the mapping are continued using the C4.5 method to see the rules in the form of a decision tree. The analysis process is assisted with the RapidMiner software. Determination of the number of clusters (k) is determined by using the Davies Bouldin Index (DBI) parameter to optimize the cluster results obtained. For k = 3 has an optimal value of 0.740. The mapping results obtained 9 provinces are in the high cluster (C1 = red zone), 3 provinces are in the alert cluster (C2 = yellow zone) and 22 provinces are in the low cluster (C3 = green zone). The value obtained from the decision tree for cluster height (C1 = red zone) based on C4.5 is if the number of positive cases is smaller than 9524 and greater than 4329 (4329&gt; x1 &lt;9524). The nine provinces included in the high cluster (C1 = red zone) are Aceh, Bali, DKI Jakarta, West Java, Central Java, East Java, South Kalimantan, South Sumatra and South Sulawesi. The results of the combination of these methods can be applied and provide knowledge in the form of new information about mapping in the form of clusters to the distribution of the Covid-19 pandemic in Indonesia
Study of the Freeze Casting Process by Artificial Neural Networks

Freeze casting technology has experienced vast development since the early 2000s due to its versatility and simplicity for producing porous materials. A linear relationship between the final porosity and the initial solid material fraction in the suspension was reported by many researchers. However, the linear relationship cannot well describe the freeze casting for various samples. Here, we proposed an artificial neural network (ANN) to analyze the influence of critical parameters on freeze-cast porous materials. After well training the ANN model on experimental data, a porosity value can be predicted from four inputs, which describe the most influential process conditions. Based on the constructed model, two improvements are also successfully added on to infer more information. By involving big data from real experiments, this method effectively summarizes a general rule for diverse materials in one model, which gives a new insight into the freeze casting process. The good convergence and accuracy prove that our ANN model has the potential to be developed for solving more complicated issues of freeze casting. Finally, a user-friendly mini-program based on a well-trained ANN model is also provided to predict the porosity for customized freeze-casting experiments.
Video Object Detection With Two-Path Convolutional LSTM Pyramid

One of the major challenges in video object detection is drastic scale changes of objects due to camera motion.In this paper, we propose a two-path Convolutional Long Short-Term Memory (convLSTM) pyramid network designed to extract and convey multi-scale temporal contextual information in order to handle object scale changes efficiently.The proposed two-path convLSTM pyramid consists of a stack of multi-input convLSTM modules.It is updated in top-down and bottom-up pathways so that the temporal contextual information for small-to-large and large-to-small scale changes is exploited.The proposed multi-input convLSTM module uses two input feature maps of different resolutions to store and exchange temporal contextual information of different scales between neighboring convLSTM modules.The outputs of the proposed convLSTM pyramid network constitute a feature pyramid where each feature map contains multi-scale temporal contextual information from earlier frames.The proposed convLSTM pyramid can be combined with various still-image object detectors to improve the performance of video object detection.Experimental results on ImageNet VID dataset show that the proposed method achieves state-of-the-art performance and can handle scale changes efficiently in video object detection.
The Benefits of Close-Domain Fine-Tuning for Table Detection in Document Images

A correct localisation of tables in a document is instrumental for determining their structure and extracting their contents; therefore, table detection is a key step in table understanding. Nowadays, the most successful methods for table detection in document images employ deep learning algorithms; and, particularly, a technique known as fine-tuning. In this context, such a technique exports the knowledge acquired to detect objects in natural images to detect tables in document images. However, there is only a vague relation between natural and document images, and fine-tuning works better when there is a close relation between the source and target task. In this paper, we show that it is more beneficial to employ fine-tuning from a closer domain. To this aim, we train different object detection algorithms (namely, Mask R-CNN, RetinaNet, SSD and YOLO) using the TableBank dataset (a dataset of images of academic documents designed for table detection and recognition), and fine-tune them for several heterogeneous table detection datasets. Using this approach, we considerably improve the accuracy of the detection models fine-tuned from natural images (in mean a 17%, and, in the best case, up to a 60%).
Fully Automated Detection and Classification of White Blood Cells

The measure of White Blood Cells (WBC) in the blood is an important indicator of pathological conditions. Computer vision based methods for differential counting of WBC are increasing due to their advantages over traditional methods. However, most of these methods are proposed for single WBC images which are pre-processed, and do not generalize for raw microscopic images with multiple WBC. Moreover, they do not have the capability to detect the absence of WBC in the images. This paper proposes an image processing algorithm based on K-Means clustering to detect the presence of WBC in raw microscopic images and to localize them, and a VGG-16 classifier to classify those cells with a classification accuracy of 95.89%.
CcNav: Understanding Compiler Optimizations in Binary Code

Program developers spend significant time on optimizing and tuning programs. During this iterative process, they apply optimizations, analyze the resulting code, and modify the compilation until they are satisfied. Understanding what the compiler did with the code is crucial to this process but is very time-consuming and labor-intensive. Users need to navigate through thousands of lines of binary code and correlate it to source code concepts to understand the results of the compilation and to identify optimizations. We present a design study in collaboration with program developers and performance analysts. Our collaborators work with various artifacts related to the program such as binary code, source code, control flow graphs, and call graphs. Through interviews, feedback, and pair-analytics sessions, we analyzed their tasks and workflow. Based on this task analysis and through a human-centric design process, we designed a visual analytics system Compilation Navigator (CcNav) to aid exploration of the effects of compiler optimizations on the program. CcNav provides a streamlined workflow and a unified context that integrates disparate artifacts. CcNav supports consistent interactions across all the artifacts making it easy to correlate binary code with source code concepts. CcNav enables users to navigate and filter large binary code to identify and summarize optimizations such as inlining, vectorization, loop unrolling, and code hoisting. We evaluate CcNav through guided sessions and semi-structured interviews. We reflect on our design process, particularly the immersive elements, and on the transferability of design studies through our experience with a previous design study on program analysis.
Teaching Vlsi Design At The Korea University Of Technology &amp; Education

an International Invitation Lecture Program under which it has invited experts from universities around the world to offer one or two week intensive courses in
CORPORATE SOCIAL RESPONSIBILITY AND HUMAN RIGHTS

This article aims at the study of corporate social responsibility concerning the human rights of stakeholders. It analyses the relationship between public and private law and what has been done to ensure that the interests of stakeholders, in particular human rights, are protected in business transactions. Using the hypothetical-deductive method and bibliographic research, international covenants and domestic legislation are analyzed, and we found that there is a movement in the international community to regulate the subject, moving towards the creation of an international instrument that links transnational operations to human rights and that some countries have recently taken internal initiatives to make their implementation and protection more effective, whose purpose is to add universal values and principles to corporate responsibility.
The litany of suffering is still heard but not louder: A case study of clinical supervision to school libraries in Indonesia’s four provinces

From 2002 through 2004, the National Library of Indonesia distributed block grants to 250 school libraries in the province of Central Java scattered in 10 districts, 150 school libraries in West Nusa Tenggara for 7 districts, 125 school libraries in South Sumatera in 5 districts while in the province of Bangka Belitung only to one municipality covering 25 school libraries. For three years, a school library which serves only one school received a block grant of 19 million rupiahs or approximately US $2000, while (multiple) school library which serves two or more schools received 29 million rupiahs or around US 3200 all for books. Beside that, there are trainings for library staffs conducted at the province capital as well as at the districts and supervision from the National Library. After three year implementation, the National Library set up two independent teams to evaluate the school library performance, block grant awardees. The first team consists of five consultant on education and librarianship while the second one is an independent consultant bureau. Among the findings are the rise of book usages among school children, rising initiatives among teacher cum teacher in charge of library to correlate the library activities with the class programme and communities’ activities, the provision of separate building for school library especially in South Sumatera. However there anxieties on the continuation of theschool libraries after the project is over, as not all grant-receiving-districts have enough budget to provide finances for the school library operations after the project is over.
PENERAPAN MODEL PEMBELAJARAN PROGRESIF UNTUK MENINGKATKAN KREATIVITAS DAN PRESTASI BELAJAR IPS

PENERAPAN MODEL PEMBELAJARAN PROGRESIF UNTUK MENINGKATKAN KREATIVITAS DAN PRESTASI BELAJAR IPS * Suryanto dan Salamah Universitas PGRI Yogyakarta Diterima: 20 Juni 2014. Disetujui: 30 Juni 2014. Dipublikasikan: Juli 2014 Abstrak Penelitian bertujuan untuk meningkatkan Kreativitas dan Prestasi Belajar IPS melalui model Pembelajaran  Progresif . Jenis penelitian yang digunakan adalah penelitian tindakan kelas ( Classroom Action Research ). Subjek dalam penelitian ini adalah Siswa  kelas V SDN 3 Tunjungseto, Sempor, Kebumen Tahun Pelajaran 2013/2014  yang berjumlah 15 siswa.  Prosedur penelitian meliputi empat tahapan yaitu perencanaan, pelaksanaan, observasi dan refleksi. Metode pengumpulan data menggunakan observasi, angket, dan  tes.  Analisis data dalam penelitian ini menggunakan teknik deskriptif kuantitatif. Hasil Penelitian menunjukkan bahwa 1) Kreativitas belajar IPS dapat ditingkatkan melalui  pembelajaran progresif . Hal tersebut dibuktikan pada pra siklus persentase kreativitas siswa sebesar  60,6%, meningkat pada siklus I dengan mean (68,5) dan pada siklus II sebesar 86,5. 2) Hasil belajar IPS dapat ditingkatkan melalui  pembelajaran progresif . Hal ini dibuktikan pada pra siklus dengan nilai rata-rata 61,66% ,  pada siklus I meningkat menjadi 72,33% dan pada siklus II meningkat menjadi 85,00%. Model  pembelajaran progresif dapat meningkatkan kreativitas dan prestasi belajar siswa. Kata kunci: model pembelajaran progresif,  kreativitas, prestasi belajar Abstract Research aims to improve Creativity and Learning Achievement IPS through the Progressive Learning Model. This type of research is action research (Classroom Action Research). Subjects in this study were Grade 3 Tunjungseto SDN, Sempor, Kebumen Academic Year 2013/2014, amounting to 15 students. Research procedure includes four phases: planning, implementation, observation and reflection. Data collection methods used observation, questionnaires, and tests. Analysis of the data in this study using quantitative descriptive techniques. Research results show that 1) social studies Creativity can be enhanced through progressive learning. This is evidenced in the pre-cycle students' creativity percentage of 60.6%, an increase in the first cycle to the mean (68.5) and the second cycle of 86.5. 2) The results of social studies can be enhanced through progressive learning. This is evidenced in the pre cycle with an average value of 61.66%, in the first cycle increased to 72.33% and the second cycle increased to 85.00%. Progressive learning model can improve creativity and student achievement. Keywords: model of progressive learning, creativity, academic achievement © 2014 Prodi PGSD Universitas PGRI Yogyakarta
The Special Theory of Relativity

This advanced undergraduate physics textbook presents an accessible treatment of classical mechanics using plain language and clear examples. While comprehensive, the book can be tailored to a one-semester course. An early introduction of the Lagrangian and Hamiltonian formalisms gives students an opportunity to utilize these important techniques in the easily visualized context of classical mechanics. The inclusion of 321 simple in-chapter exercises, 82 worked examples, 550 more challenging end-of-chapter problems, and 65 computational projects reinforce students' understanding of key physical concepts and give instructors freedom to choose from a wide variety of assessment and support materials. This new edition has been reorganized. Numerous sections were rewritten. New problems, a chapter on fluid dynamics, and brief optional studies of advanced topics such as general relativity and orbital mechanics have been incorporated. Online resources include a solutions manual for instructors, lecture slides, and a set of student-oriented video lectures.
LibGuides: Technology Impact on Teen Life: Home

To prepare for a debate in Spanish 4, gather evidence from reputable sources about the impact of technology, positive or negative, on teen life.
LibGuides: Music: Books

This Library Guide will help students find the resources they need for their music research.
Contact Map Generation for the Assessment of Robot Cleaning Tasks in Partially Unkown Environments

Solar energy is one crucial resource for planetary exploration. On Mars however, solar panels are prone to be coated by dust, which reduces efficiency or leads to complete power loss. On February 13th 2019, NASA announced the end of the Opportunity Mars rover mission, after almost 15 years. Since the rover had gotten into a Martian dust storm, NASA was not able to restore contact. The last message Opportunity send to Earth revealed, that it was very dark and that the on-board batteries were running low. This indicates, that the solar panels, which provide the rover with power, were heavily covered by dust and thus unable to collect enough sunlight.Equipping robots with the capability of maintaining solar panels, in order to prevent energy loss, will be a vital contribution to allow for successful Mars missions. Thus, future space robot assistants and rover will be able to create a sustainable pathway for human exploration, which includes the maintenance of solar farms.In this thesis a comprehensive approach is proposed to provide a humanoid robot with the skills for maintaining a solar panel farm. The robot is able to perform cleaning tasks on unknown surfaces, as well as creating a contact map of the cleaned area, which serves as action assessment indicator. In particular, a perception method is used to detect unknown objects by means of segmentation and primitive shape fitting. A motion planning framework is utilized to generate wiping motions w.r.t. geometric and semantic information of the surface and the used tool. A novel technique is introduced to facilitate assessment of the cleaning task by means of autonomously generated contact maps. The robot is able to determine the next solar panel, that requires maintenance, as the map is dynamically updated over time. This capability will enable future robots to autonomously maintain a solar panel farm.