Reviewerly/exHarmony huggingface dataset:
Automatic Breast Lesion Segmentation Using Continuous Max-Flow Algorithm in Phase Preserved DCE-MRIs

In this work, we propose a framework for the automatic and accurate segmentation of breast lesions from the Dynamic Contrast Enhanced (DCE) MRI. The framework is built using max flow and min cut problems in the continuous domain over phase preserved denoised images. The proposed method is achieved via three steps. First, post-contrast and pre-contrast images are subtracted, followed by image registrations that benefit to enhancing lesion areas. Second, a phase preserved denoising and pixel-wise adaptive Wiener filtering technique is used, followed by max flow and min cut problems in a continuous domain. A denoising mechanism clears the noise in the images by preserving useful and detailed features such as edges. Then, lesion detection is performed using continuous max flow. Finally, a morphological operation is used as a post-processing step to further delineate the obtained results. The efficiency of the proposed method is verified with a series of qualitative and quantitative experiments carried out on 21 cases with two different MR image resolutions. Performance results demonstrate the quality of segmentation obtained from the proposed method.
Proper Value Equivalence

One of the main challenges in model-based reinforcement learning (RL) is to decide which aspects of the environment should be modeled. The value-equivalence (VE) principle proposes a simple answer to this question: a model should capture the aspects of the environment that are relevant for value-based planning. Technically, VE distinguishes models based on a set of policies and a set of functions: a model is said to be VE to the environment if the Bellman operators it induces for the policies yield the correct result when applied to the functions. As the number of policies and functions increase, the set of VE models shrinks, eventually collapsing to a single point corresponding to a perfect model. A fundamental question underlying the VE principle is thus how to select the smallest sets of policies and functions that are sufficient for planning. In this paper we take an important step towards answering this question. We start by generalizing the concept of VE to order-$k$ counterparts defined with respect to $k$ applications of the Bellman operator. This leads to a family of VE classes that increase in size as $k \rightarrow \infty$. In the limit, all functions become value functions, and we have a special instantiation of VE which we call proper VE or simply PVE. Unlike VE, the PVE class may contain multiple models even in the limit when all value functions are used. Crucially, all these models are sufficient for planning, meaning that they will yield an optimal policy despite the fact that they may ignore many aspects of the environment. We construct a loss function for learning PVE models and argue that popular algorithms such as MuZero and Muesli can be understood as minimizing an upper bound for this loss. We leverage this connection to propose a modification to MuZero and show that it can lead to improved performance in practice.
ORGANIZATION AND MEANS FOR ASSESSING THE INDEPENDENT WORK OF STUDENTS IN JALAL-ABAD STATE UNIVERSITY

The article discusses the organization of independent activities of students at Jalal-Abad State University. Specifically, five tasks are indicated by the faculty during the organization of independent work. The types of control for checking the quality of the organization of independent work of students are considered: corrective, ascertaining, self-checking, current, intermediate. The technology of automated module-rating education organized in Jalal-Abad State University is described - AVN, which allows to make the transition from a five-point grading system to a 100-point grading and a range of points for translation into grades.
Cultivated Random Forests: Robust Decision TreeLearning through Tree Structured Ensembles

We propose a robust decision tree induction method that mitigates the problemsof instability and poor generalization on unseen data. In the spirit of modelimprecision and robust statistics, we generalize decision trees by replacing internalnodes with two types of ensemble modules that pool together a set of decisions intoa soft decision: (1) option modules consisting of all reasonable variable choices ateach step of the induction process, (2) robust split modules including all elementsof a neighbourhood of an optimal split-point as reasonable alternative split-points.We call the resulting set of trees cultivated random forest as it corresponds to anensemble of trees which is centered around a single tree structure, alleviating the lossof interpretability of traditional ensemble methods. The explicit modelling of nonprobabilistic uncertainty about the tree structure also provides an estimate of thereliability of predictions, allowing to abstain from predictions when the uncertaintyis too high. On a variety of benchmark datasets, we show that our method is oftencompetitive with random forests, while being structurally substantially simpler andeasier to interpret.
Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels

Optimization is a key component for training machine learning models and has a strong impact on their generalization. In this paper, we consider a particular optimization method -- the stochastic gradient Langevin dynamics (SGLD) algorithm -- and investigate the generalization of models trained by SGLD. We derive a new generalization bound by connecting SGLD with Gaussian channels found in information and communication theory. Our bound can be computed from the training data and incorporates the variance of gradients for quantifying a particular kind of sharpness of the loss landscape. We also consider a closely related algorithm with SGLD, namely differentially private SGD (DP-SGD). We prove that the generalization capability of DP-SGD can be amplified by iteration. Specifically, our bound can be sharpened by including a time-decaying factor if the DP-SGD algorithm outputs the last iterate while keeping other iterates hidden. This decay factor enables the contribution of early iterations to our bound to reduce with time and is established by strong data processing inequalities -- a fundamental tool in information theory. We demonstrate our bound through numerical experiments, showing that it can predict the behavior of the true generalization gap.
توظيف الاتصال الرقمي في الإبداع والابتكار بالقيادة العامة لشرطة الشارقة : دراسة تطبيقية على عينة من العاملين في شرطة الشارقة = Utilizing Digital Communication in Innovation and Creativity within Sharjah Police G.H.Q. : A Study Applied on a Sample of Sharjah Police Personnel

الاتصال الرقمي | التوظيف | التفكير | الإبداع | الابتكار | التفكير النقدي | التفكير الإبداعي | التميز | الأداء | التفكير الابتكاري | Digital Communication | Utilization | Thinking | Innovation | Creativity | Critical Thinking | Innovative Thinking | Excellence | Performance | Creative Thinking
SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency

In this paper, we explore how we can build upon the data and models of Internet images and use them to adapt to robot vision without requiring any extra labels. We present a framework called Self-supervised Embodied Active Learning (SEAL). It utilizes perception models trained on internet images to learn an active exploration policy. The observations gathered by this exploration policy are labelled using 3D consistency and used to improve the perception model. We build and utilize 3D semantic maps to learn both action and perception in a completely self-supervised manner. The semantic map is used to compute an intrinsic motivation reward for training the exploration policy and for labelling the agent observations using spatio-temporal 3D consistency and label propagation. We demonstrate that the SEAL framework can be used to close the action-perception loop: it improves object detection and instance segmentation performance of a pretrained perception model by just moving around in training environments and the improved perception model can be used to improve Object Goal Navigation.
Penerapan Algoritma Gost Dan Mode Output Feedback Untuk Tingkat Keamanan Citra Digital

Digital images that are private and confidential are very vulnerable to wiretapping by irresponsible parties. Especially if it is distributed over the internet such as chat-based applications, WhatsApp and e-mail media. The image that is sent is sometimes an image that is confidential and must be protected. In order to maintain the security of digital images, cryptographic techniques can be used. Cryptographic techniques can secure digital images by changing the pixel values ??of the digital image so that it produces a different pixel value from the original image to be secured. In this study, the algorithm used is the GOST algorithm by adding the OFB technique to optimize the GOST key, and the encryption results can be sent via an application that will be designed with a client-server model. Creating a client-server-based encryption and decryption software application, aimed at transferring digital data in the form of a plain image that has been encrypted using the GOST algorithm, and can be distributed locally using an IP address connected to the same network on the sending and receiving computers (client-server)
OverIT: An Interactive Overlay for Touchscreen-based UI Customization by Demonstration

Smartphones have been widely used for various purposes and stay connected with people at all times. However, the use of such touchscreen devices can be physically restricted depending on users' context where only one hand is available to interact with the device. Even major smartphone manufacturers (e.g., Apple, Samsung) offer one handed mode, they still lack functions in the third-party applications, and the process is also complicated. We propose OverIT, a system that enables users to customize interfaces by adding new buttons on an interactive overlay which can be positioned anywhere on the touchscreen where each button serves the same functionality as an existing one. It is designed to support users to map a certain button event freely and easily to a newly created button by performing a demonstration of a button tap. We expect our system to improve the overall user experience of one-handed interaction with touchscreen devices.
Study on the Big Data Processing for Generic Clarification of Heterogeneous Images

Most industries around the globe make use of image processing to improve their productions. On the other hand Big Data Processing is a big dataset; this required fast method to processing irrespective of Generic nature, therefore Clarification of heterogeneous images can improve the integrity of any system design. To avoid waste of time and energy, it is necessary to classify images. In a clear perspective, the objective of image classification is complex characteristic modalities to categories and label group of pixies or vector within an image based on specific rules. Big Data Processing for Generic Clarification of heterogeneous images provides fast, accurate and objectives results. In this study, the researchers classified into three category using resnet50 techniques for training dataset images. The outcome of the research is analyzing these techniques and comparison analysis on different existing image data sets as pre-trained data and test data as sample images for decision making based on their limitations and strengths.
Intelligent Recognition Method of Athlete Wrong Movement Based on Image Vision

Current methods of human body movement recognition neglect the depth denoising and edge restoration of movement image, which leads to great error in athletes’ wrong movement recognition and poor application intelligence. Therefore, an intelligent recognition method based on image vision for sports athletes’ wrong actions is proposed. The basic principle, structure, and 3D application of computer image vision technology are defined. Capturing the human body image and point cloud data, the three-dimensional dynamic model of sports athletes action is constructed. The color camera including CCD sensor and CMOS sensor is selected to collect the wrong movement image of athlete and provide image data for the recognition of wrong movement. Wavelet transform coefficient and quantization matrix threshold are introduced to denoise the wrong motion images of athletes. Based on this, the feature of sports athlete’s motion contour image is extracted in spatial frequency domain, and the edge of the image is further recovered by Canny operator. Experimental results show that the proposed method can accurately identify the wrong movements of athletes, and there is no redundancy in the recognition results. Image denoising effect is good and less time-consuming and can provide a reliable basis for related fields.
Chill and Jazz at the Lake: : Record Package Design for Roya's "Breath & Being"

This project is an album design for Roya's Breath & Being, created for the Typography II course (DESN 231). This Canadian artist's musical identity is an eclectic mix of styles that makes her songs cheerful and approachable while also alluding to her family roots. The design uses a casual aesthetic to transmit Roya's self-expressive and lively music. One lonely figure sits on the edge of the lake, looking at their reflection in the water, as a metaphor for one's self-observation and desire for connecting with people. Illustrations and text were hand-rendered to communicate self-expression. Elements such as brush strokes and gestural imprints reinforce the idea of a personal mark. These physical textures are also symbolic of musical textures, a fundamental aspect of Roya's work. The use of organic shapes and bright colours make the design whimsical. A touch of orange in the palette provides coziness to counterbalance the cool-toned areas. Ripples in the water are dynamic and perform as visual representations of melody and harmony. They also appear in the center labels as concentric waves emanating from the record. The waves embody the dialogue between the singer and her audience: as the music plays, sound propagates to reach the listeners. The final piece is an engaging scenery that invites listeners to enjoy Roya's art, combining visual rhythm and emotion in allusion to her music.Department: Design Studies Faculty Mentor: Constanza Pacher
Estimating Image Depth in the Comics Domain

Estimating the depth of comics images is challenging as such images a) are monocular; b) lack ground-truth depth annotations; c) differ across different artistic styles; d) are sparse and noisy. We thus, use an off-the-shelf unsupervised image to image translation method to translate the comics images to natural ones and then use an attention-guided monocular depth estimator to predict their depth. This lets us leverage the depth annotations of existing natural images to train the depth estimator. Furthermore, our model learns to distinguish between text and images in the comics panels to reduce text-based artefacts in the depth estimates. Our method consistently outperforms the existing state-ofthe-art approaches across all metrics on both the DCM and eBDtheque images. Finally, we introduce a dataset to evaluate depth prediction on comics.
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks

Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks, i.e., an imperceptible perturbation to the input can mislead DNNs trained on clean images into making erroneous predictions. To tackle this, adversarial training is currently the most effective defense method, by augmenting the training set with adversarial samples generated on the fly. Interestingly, we discover for the first time that there exist subnetworks with inborn robustness, matching or surpassing the robust accuracy of the adversarially trained networks with comparable model sizes, within randomly initialized networks without any model training, indicating that adversarial training on model weights is not indispensable towards adversarial robustness. We name such subnetworks Robust Scratch Tickets (RSTs), which are also by nature efficient. Distinct from the popular lottery ticket hypothesis, neither the original dense networks nor the identified RSTs need to be trained. To validate and understand this fascinating finding, we further conduct extensive experiments to study the existence and properties of RSTs under different models, datasets, sparsity patterns, and attacks, drawing insights regarding the relationship between DNNs' robustness and their initialization/overparameterization. Furthermore, we identify the poor adversarial transferability between RSTs of different sparsity ratios drawn from the same randomly initialized dense network, and propose a Random RST Switch (R2S) technique, which randomly switches between different RSTs, as a novel defense method built on top of RSTs. We believe our findings about RSTs have opened up a new perspective to study model robustness and extend the lottery ticket hypothesis.
An Iterative Morphological Fuzzy Rule Based Classifier For Moving Vehicle Recognition

A novel fuzzy rule based classifier to detect and recognize the type of moving vehicle from video frame using iterative morphological image processing operations is presented in this research work. A traffic video for a time period is captured, converted into still frames, pre-processed by iterative morphological filter, foreground objects are extracted by Background Subtractiontechnique,boundaries of the vehicles are extracted by morphological operation and the detected vehicles are isolated by Bounding Box method. Fuzzy Rule based classifier is constructed to categorize and recognize the vehicles into different types(Car, Bike, Bus, Container&amp; Truck) based on the structural features Height, Width and Area of the bounding boxes. Finally the proposed method is evaluated with the classification metrics confusion matrix, precision and accuracy and the experimental results show that the performance of the proposed system goes beyond that of the existing video-based vehicle classification techniques yielding 84% of accuracy.
Securing the organization documents in content management system with two levels of security using encryption and block chain technology

The word “security” is a very important mechanism when it is related to an enterprise documents or our real life materials. To safeguard our house or precious items like gold, money we use bank locker services or underground hidden lockers at home. In the same way, IT industries invest huge amount of capital in increasing security to its business and the documents. Organizations use cryptography techniques to secure their data using advanced encryption algorithms like SHA-256, SHA-512, RSA-1024, RSA-2048 bits key encryption and Elliptic Curve Cryptography (ECC) algorithms. These industry standard algorithms are not easy to break. For example, to break RSA-2048 bit encryption key, a classical computer needs around 300 trillion years.  According to the ongoing research, a quantum computer can break it in 10seconds, but such a quantum computer doesn’t exist yet. Even though these cryptographic algorithms promises very good level of security, there will be always a room for breaking the security. Hackers will try new methods to break the security. Hence the organization also should move on to employ new methods to increase level and quality of the security.This paper explains a two level of security to guard the documents maintained in content management system. Content management system, in short can be called as CMS is a computer software used to maintain the creation and changes of digital content. A CMS can take any form like enterprise CMS (ECMS), web CMS (WCMS), component CMS (CCMS), and document CMS (DCMS) and digital asset management system. The proposed method can be used in any of the form of CMS. The first step of security is attained using encryption and second level of security is attained using block chain technology. For each document which has to be stored in the CMS, a unique hash id will be generated using Ethereum solidity program. This hash id will be stored in block of each block chain as entire content of the document cannot be stored on block chain. This will be the first level of security. As an additional and second level of security, the document content will be encrypted and stored on the IPFS cloud. The addition of block chain technology gives an efficient level of security to the documents of the organization.
What Can a Generative Language Model Answer About a Passage

Generative language models trained on large, diverse corpora can answer questions about a passage by generating the most likely continuation of the passage followed by a question/answer pair. However, accuracy rates vary depending on the type of question asked. In this paper we keep the passage fixed, and test with a wide variety of question types, exploring the strengths and weaknesses of the GPT-3 language model. We provide the passage and test questions as a challenge set for other language models.
CLASSIFICATION OF MALARIA INFECTED ERYTHROCYTES USINGIMAGE PROCESSING

Malaria parasites are known to have caused deaths worldwide, especially in African regions where resources are limited. Currently, malaria diagnoses are still done manually by trained experts. Hence, the use of computer-aided detection systems for malaria detection oridentification in erythrocytes is a valuable approach in reducing the need for human resources. This project aims to use image processing to extract the features of infected erythrocytes and study three commonly used machine learning techniques in order to compare their performance in classifying malaria infected erythrocytes.
When Are Solutions Connected in Deep Networks

The question of how and why the phenomenon of mode connectivity occurs in training deep neural networks has gained remarkable attention in the research community. From a theoretical perspective, two possible explanations have been proposed: (i) the loss function has connected sublevel sets, and (ii) the solutions found by stochastic gradient descent are dropout stable. While these explanations provide insights into the phenomenon, their assumptions are not always satisfied in practice. In particular, the first approach requires the network to have one layer with order of $N$ neurons ($N$ being the number of training samples), while the second one requires the loss to be almost invariant after removing half of the neurons at each layer (up to some rescaling of the remaining ones). In this work, we improve both conditions by exploiting the quality of the features at every intermediate layer together with a milder over-parameterization condition. More specifically, we show that: (i) under generic assumptions on the features of intermediate layers, it suffices that the last two hidden layers have order of $\sqrt{N}$ neurons, and (ii) if subsets of features at each layer are linearly separable, then no over-parameterization is needed to show the connectivity. Our experiments confirm that the proposed condition ensures the connectivity of solutions found by stochastic gradient descent, even in settings where the previous requirements do not hold.