Reviewerly/exHarmony huggingface dataset:
Query-Efficient Black-box Adversarial Attacks

Author(s): Singh, Simranjit | Advisor(s): Hsieh, Cho-Jui | Abstract: Machine learning systems have been shown to be vulnerable to adversarial examples. We study the most practical problem setup for evaluating adversarial robustness of a machine learning system with limited access: the hard-label black-box attack setting for generating adversarial examples, where limited model queries are allowed and only the decision is provided to a queried data input. Several algorithms have been proposed for this problem but they typically require huge amount (g20,000) of queries for attacking one example. Among them, one of the state-of-the-art approaches (Cheng et al., 2019) showed that hard-label attack can be modeled as an optimization problem where the objective function can be evaluated by binary search with additional model queries, thereby a zeroth order optimization algorithm can be applied. In this thesis, we adopt the same optimization formulation but propose to directly estimate the sign of gradient at any direction instead of the gradient itself, which enjoys the benefit of single query. Using this single query oracle for retrieving sign of directional derivative, we develop a novel query-efficient Sign-OPT approach for hard-label black-box attack. We provide a convergence analysis of the new algorithm and conduct experiments on several models on MNIST, CIFAR-10 and ImageNet. We find that Sign-OPT attack consistently requires 5X to 10X fewer queries when compared to the current state-of-the-art approaches and usually converges to an adversarial example with smaller perturbation.
Low-viewpoint forest depth dataset for sparse rover swarms

Rapid progress in embedded computing hardware increasingly enables on-board image processing on small robots. This development opens the path to replacing costly sensors with sophisticated computer vision techniques. A case in point is the prediction of scene depth information from a monocular camera for autonomous navigation. Motivated by the aim to develop a robot swarm suitable for sensing, monitoring, and search applications in forests, we have collected a set of RGB images and corresponding depth maps. Over 100k images were recorded with a custom rig from the perspective of a small ground rover moving through a forest. Taken under different weather and lighting conditions, the images include scenes with grass, bushes, standing and fallen trees, tree branches, leafs, and dirt. In addition GPS, IMU, and wheel encoder data was recorded. From the calibrated, synchronized, aligned and timestamped frames about 9700 image-depth map pairs were selected for sharpness and variety. We provide this dataset to the community to fill a need identified in our own research and hope it will accelerate progress in robots navigating the challenging forest environment. This paper describes our custom hardware and methodology to collect the data, subsequent processing and quality of the data, and how to access it.
A Review on Library Automation Using Artificial Intelligence

Artificial Intelligence is a broad, complex area of study, which can be difficult for no specialists to understand. Yet, its ultimate promise is to create computer systems that rival human intelligence, and this clearly has major implications for librarianship. If we want to make progress in the area of intelligent systems, we must have a well-developed understanding of AI technologies. Through the existing application of AI technologies, numerous prototype intelligent library systems has been created for cataloging, indexing, information retrieval, reference, and other purposes; however, relatively few of these systems have evolved into product or systems that are used in the day-to-day operations of libraries. This proposed system automates the task of librarian and the users, using AI concepts like Natural language processing (NLP), data mining and speech recognition system. This system helps user to search and locate (path to reach to book location) books from library. Motivation behind implementing AI is, Data mining simplifies data processing and provides optimal results. NLP and speech recognition simplify system interaction for librarian and users. The system helps simplifying book issuing process and record maintenance as complete solution to conventional library management system.
Electronic books usage at school libraries in Zimbabwe

The research study was carried out with the aim of assessing the usage of electronic books in school libraries of Zimbabwe. The study sought to ascertain the types of electronic books used at the school libraries in Zimbabwe, their extent of usage and the challenges faced when using the electronic books. Data for the research study was gathered by means of interviews during an annual pre-conference workshop of Zimbabwean school librarians held in the city of Mutare. This study reaffirms findings from past studies that school libraries are embracing the electronic books despite facing various challenges. The study recommends that school librarians be upskilled to be able to competently promote electronic books usage. Central government is also strongly recommended to put in place a policy on establishment of a library and employ professionally trained school librarians. Keywords: Electronic books usage, School libraries, Zimbabwe
CMOS Annealing Mashine

A new computing architecture, an annealing machine, which is specialized to solve combinatorial optimization problems, is proposed. The annealing machine maps combinatorial optimization problems to an Ising model and solves the problems by its own convergence property. We proposed a CMOS annealing machine, which is a CMOS implementation of the annealing machines. The CMOS annealing machine has an in-memory computing architecture for a high scalability. We constructed prototypes of the CMOS annealing machine. The 1st generation prototype with ASIC implementation, the 2nd generation prototype with FPGA implementation and the card-size prototype with 2-chip ASIC implementation are used to confirm its ability to solve combinatorial optimization problems and its high energy efficiency. The 2nd and card-size prototypes also confirm its multiple-chip operation, which enables a higher scalability of the CMOS annealing machine solving larger size optimization problems.
ANALISIS LAJU PERBAIKAN KONDISI KLINIS PASIEN KANKER PAYUDARA MENGGUNAKAN REGRESI ACCELERATED FAILURE TIME WEIBULL (Studi Kasus: RSUD Tugurejo Semarang)

Breast cancer is a disease caused by abnormal growth from the body’s cells that’s turned into cancer cells. It can spread to other parts of the body so that it can lead to death. Breast cancer is the highest prevalence of cancer in Indonesian women as well as in Semarang City. In an effort to reduce mortality from breast cancer, an analysis of breast cancer patients in Tugurejo General Hospital Semarang can be done by knowing the factors that influence the rate of improvement of breast cancer patient’s clinical conditions. Survival analysis is one of the statistical methods that can be used, the method for which the outcome variable of interest is time until an event occurs. Based on Weibull distributed survival time, this study uses Weibull Accelerated Failure Time (AFT) regression method. Dependent variables used is length of time treated and the independent variables used are age, body mass index, anemia status, type of treatment, and congenital diseases. The results showed the factors that influence that improvement of breast cancer patient’s clinical condition are age, body mass index, anemia status, and type of treatment. Keywords: Breast Cancer, Improvement of Breast Cancer Patient’s Clinical Condition, Survival Analysis, Weibull Accelerated Failure Time (AFT) Regression
Cross-modal Multi-task Learning for Graphic Recognition of Caricature Face

Face recognition of realistic visual images has been well studied and made a significant progress in the recent decade. Unlike the realistic visual images, the face recognition of the caricatures is far from the performance of the visual images. This is largely due to the extreme non-rigid distortions of the caricatures introduced by exaggerating the facial features to strengthen the characters. The heterogeneous modalities of the caricatures and the visual images result the caricature-visual face recognition is a cross-modal problem. In this paper, we propose a method to conduct caricature-visual face recognition via multi-task learning. Rather than the conventional multi-task learning with fixed weights of tasks, this work proposes an approach to learn the weights of tasks according to the importance of tasks. The proposed multi-task learning with dynamic tasks weights enables to appropriately train the hard task and easy task instead of being stuck in the over-training easy task as conventional methods. The experimental results demonstrate the effectiveness of the proposed dynamic multi-task learning for cross-modal caricature-visual face recognition. The performances on the datasets CaVI and WebCaricature show the superiority over the state-of-art methods.
Depth Edge Guided CNNs for Sparse Depth Upsampling

Guided sparse depth upsampling aims to upsample an irregularly sampled sparse depth map when an aligned high-resolution color image is given as guidance. Many neural networks have been designed for this task. However, they often ignore the structural difference between the depth and the color image, resulting in obvious artifacts such as texture copy and depth blur at the upsampling depth. Inspired by the normalized convolution operation, we propose a guided convolutional layer to recover dense depth from sparse and irregular depth image with an depth edge image as guidance. Our novel guided network can prevent the depth value from crossing the depth edge to facilitate upsampling. We further design a convolution network based on proposed convolutional layer to combine the advantages of different algorithms and achieve better performance. We conduct comprehensive experiments to verify our method on real-world indoor and synthetic outdoor datasets. Our method produces strong results. It outperforms state-of-the-art methods on the Virtual KITTI dataset and the Middlebury dataset. It also presents strong generalization capability under different 3D point densities, various lighting and weather conditions.
A method to identify geochemical mineralization on linear transect

Mineral exploration in biogeochemistry is related to the detection of anomalies in soil, which is driven by many factors and thus a complex problem. Mikšová, Rieser, and Filzmoser (2019) have introduced a method for the identification of spatial patterns with increased element concentrations in samples along a linear sampling transect. This procedure is based on fitting Generalized Additive Models (GAMs) to the concentration data, and computing a curvature measure from the pairwise log-ratios of these fits. The higher the curvature, the more likely one or both elements of the pair indicate local mineralization. This method is applied on two geochemical data sets which have been collected specifically for the purpose of mineral exploration. The aim is to test the technique for its ability to identify pathfinder elements to detect mineralized zones, and to verify whether the method can indicate which sampling material is best suited for this purpose. Reference: Mikšová D., Rieser C., Filzmoser P. (2019). "Identification of mineralization in geochemistry along a transect based on the spatial curvature of log-ratios." arXiv, (1912.02867).
THE EFFECT OF TASK_BASED LANGUAGE TEACHING ON STUDENTS’ READING COMPREHENSION OF TENTH GRADE OF SMK PUTRA ANDA BINJAI STUDENTS IN ACADEMIC 2018/2019

This study deals with the effect of applying task based language teaching on the students’ reading comprehension. The population of this research was the tenth grade students of SMK Putra Anda Binjai. The sample of research were two classes which were chosen by random sampling. Twenty eight students were taken as the samples in each class. The experimental group was taught by using task based language teaching, while the control class was taught without using task based language teaching. The data were analyzed by using t-test. The calculation shows that t-observed (2.49) was higher than t-table (1.67) at the level of significance (α) 0.05 with the degree of freedom (df) 54. The result is hypothesis stated that there is significantly effect of applying task based language teaching on the tenth grade students’ reading comprehension is accepted. Key words : Task based language teaching , reading comprehension.
Catalogue Card Drawer WA_WY - Drawer - Index to the AMA archive

Catalogue Card Drawer WA_WY - Drawer - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED
Robust Plan Execution with Unexpected Observations

In order to ensure the robust actuation of a plan, execution must be adaptable to unexpected situations in the world and to exogenous events. This is critical in domains in which committing to a wrong ordering of actions can cause the plan failure, even when all the actions succeed. We propose an approach to the execution of a task plan that permits some adaptability to unexpected observations of the state while maintaining the validity of the plan through online reasoning. Our approach computes an adaptable, partially-ordered plan from a given totally-ordered plan. The partially-ordered plan is adaptable in that it can exploit beneficial differences between the world and what was expected. The approach is general in that it can be used with any task planner that produces either a totally or a partially-ordered plan. We propose a plan execution algorithm that computes online the complete set of valid totally-ordered plans described by an adaptable partially-ordered plan together with the probability of success for each of them. This set is then used to choose the next action to execute.
Pengembangan Model Kampung Literasi untuk Meningkatkan Motivasi Pendidikan dan Minat Membaca Masyarakat Desa Kolam Kabupaten Deli Serdang

The Village Literacy Model Development Program to Increase Motivation in Education and Community Reading Interest was carried out in Kolam Village, Percut Sei Tuan District, Deli Serdang Regency, North Sumatra Province. Pond Village has been a major partner since the 2017 program and is supported by other partners such as Posyandu, Karang Taruna, schools in Kolam Village. By building a broad partnership, it is hoped that during the three years the program runs, UPBJJ Open University in Medan will be able to turn the Village Pool into a Literacy Village where the community becomes literate and knowledge literate, thereby increasing the motivation of educating to tertiary institutions. Thus the development and development of human resources (HR) in the Village Pool will increase in the future. This program continues the program achievements in 2017 and 2018 ago. During the implementation of the 2019 National PkM, various activities have been carried out, including providing assistance for renovation of the Pool Village library space, providing mobile assistance for Pool Village library equipment, and providing bookshelves, building information media and literacy campaigns, facilitating book publishing a collection of children's stories, and the launch of the Village Pool as a Village of Literacy.
“By Using Image Inpainting Technique Restoring Occluded Images for Face Recognition”

Facial recognition is also known as the face recognition it describes to predict and to establish individual identity by using face. Face recognition system is used to identify people in real time system. In image processing, hair, moustache, beards, sunglasses on face by scarf and other clothing accessories, etc are some reasons for occlusion. This is refers to as facade of the facial image. For past years, face recognition has been researched in the picture in controlled environment. Typically, partial occlusion is concerned with the identifying under uncontrolled condition. In this research paper, main aim to solve the challenges for recognition face occurs due to the presence of partial occlusion on the basis of classification made literature review and analysis are done. The image inpainting technique-based method such as exemplar-based inpainting technique, extraction of features, fast-weighted principal component analysis, etc., is used in face recognition. In this project express the restores occluded part of image or the removed occlusion of images using exemplar-based image inpainting technique, combination of fast-weighted principal component analysis (FW-PCA) and extraction of feature.
BiCANet: Bi-directional Contextual Aggregating Network for Image Semantic Segmentation

Exploring contextual information in convolution neural networks (CNNs) has gained substantial attention in recent years for semantic segmentation. This paper introduces a Bi-directional Contextual Aggregating Network, called BiCANet, for semantic segmentation. Unlike previous approaches that encode context in feature space, BiCANet aggregates contextual cues from a categorical perspective, which is mainly consist of three parts: contextual condensed projection block (CCPB), bi-directional context interaction block (BCIB), and muti-scale contextual fusion block (MCFB). More specifically, CCPB learns a category-based mapping through a split-transform-merge architecture, which condenses contextual cues with different receptive fields from intermediate layer. BCIB, on the other hand, employs dense skipped-connections to enhance the class-level context exchanging. Finally, MCFB integrates multi-scale contextual cues by investigating short- and long-ranged spatial dependencies. To evaluate BiCANet, we have conducted extensive experiments on three semantic segmentation datasets: PASCAL VOC 2012, Cityscapes, and ADE20K. The experimental results demonstrate that BiCANet outperforms recent state-of-the-art networks without any postprocess techniques. Particularly, BiCANet achieves the mIoU score of 86.7%, 82.4% and 38.66% on PASCAL VOC 2012, Cityscapes and ADE20K testset, respectively.
Evolutionary Multi-Objective Optimization Framework for Mining Association Rules

In this paper, two multi-objective optimization frameworks in two variants (i.e., NSGA-III-ARM-V1, NSGA-III-ARM-V2; and MOEAD-ARM-V1, MOEAD-ARM-V2) are proposed to find association rules from transactional datasets. The first framework uses Non-dominated sorting genetic algorithm III (NSGA-III) and the second uses Decomposition based multi-objective evolutionary algorithm (MOEA/D) to find the association rules which are diverse, non-redundant and non-dominated (having high objective function values). In both these frameworks, there is no need to specify minimum support and minimum confidence. In the first variant, support, confidence, and lift are considered as objective functions while in second, confidence, lift, and interestingness are considered as objective functions. These frameworks are tested on seven different kinds of datasets including two real-life bank datasets. Our study suggests that NSGA-III-ARM framework works better than MOEAD-ARM framework in both the variants across majority of the datasets.
The Power of a Single Qubit: Two-way Quantum Finite Automata and the Word Problem

The two-way finite automaton with quantum and classical states (2QCFA), defined by Ambainis and Watrous, is a model of quantum computation whose quantum part is extremely limited; however, as they showed, 2QCFA are surprisingly powerful: a 2QCFA, with a single qubit, can recognize, with bounded error, the language $L_{eq}=\{a^m b^m :m \in \mathbb{N}\}$ in expected polynomial time and the language $L_{pal}=\{w \in \{a,b\}^*:w \text{ is a palindrome}\}$ in expected exponential time. We further demonstrate the power of 2QCFA by showing that they can recognize the word problems of many groups. In particular 2QCFA, with a single qubit and algebraic number transition amplitudes, can recognize, with bounded error, the word problem of any finitely generated virtually abelian group in expected polynomial time, as well as the word problems of a large class of linear groups in expected exponential time. This latter class (properly) includes all groups with context-free word problem. We also exhibit results for 2QCFA with any constant number of qubits. As a corollary, we obtain a direct improvement on the original Ambainis and Watrous result by showing that $L_{eq}$ can be recognized by a 2QCFA with better parameters. As a further corollary, we show that 2QCFA can recognize certain non-context-free languages in expected polynomial time. In a companion paper, we prove matching lower bounds, thereby showing that the class of languages recognizable with bounded error by a 2QCFA in expected $\mathit{subexponential}$ time is properly contained in the class of languages recognizable with bounded error by a 2QCFA in expected $\mathit{exponential}$ time.
Balanced Alignment for Face Recognition: A Joint Learning Approach

Face alignment is crucial for face recognition and has been widely adopted. However, current practice is too simple and under-explored. There lacks an understanding of how important face alignment is and how it should be performed, for recognition. This work studies these problems and makes two contributions. First, it provides an in-depth and quantitative study of how alignment strength affects recognition accuracy. Our results show that excessive alignment is harmful and an optimal balanced point of alignment is in need. To strike the balance, our second contribution is a novel joint learning approach where alignment learning is controllable with respect to its strength and driven by recognition. Our proposed method is validated by comprehensive experiments on several benchmarks, especially the challenging ones with large pose.
Ordered Functional Decision Diagrams

Several BDD variants were designed to exploit special features of Boolean functions to achieve better compression rates.Deciding a priori which variant to use is as hard as constructing the diagrams themselves and the conversion between variants comes in general with a prohibitive cost.This observation leads naturally to a growing interest into when and how one can combine existing variants to benefit from their respective sweet spots. In this paper, we introduce a novel framework, termed λDD (LDD), that revisits BDD from a purely functional point of view.The framework allows to classify the already existing variants, including the most recent ones like ChainDD and ESRBDD, as implementations of a special class of ordered models. We enumerate, in a principled way, all the models of this class and isolate its most expressive model.This new model, termed λDD-O-NUCX, is suitable for both dense and sparse Boolean functions, and, unlike ChainDD and ESRBDD, is invariant by negation.The canonicity of λDD-O-NUCX is formally verified using the Coq proof assistant.We furthermore provide experimental evidence corroborating our theoretical findings: more expressive λDD models achieve, indeed, better memory compression rates.
A termination criterion for stochastic gradient descent for binary classification

We propose a new, simple, and computationally inexpensive termination test for constant step-size stochastic gradient descent (SGD) applied to binary classification on the logistic and hinge loss with homogeneous linear predictors. Our theoretical results support the effectiveness of our stopping criterion when the data is Gaussian distributed. This presence of noise allows for the possibility of non-separable data. We show that our test terminates in a finite number of iterations and when the noise in the data is not too large, the expected classifier at termination nearly minimizes the probability of misclassification. Finally, numerical experiments indicate for both real and synthetic data sets that our termination test exhibits a good degree of predictability on accuracy and running time.