Reviewerly/exHarmony huggingface dataset:
Comparative performance of self-supervised 3D-ResNet-GAN for electronic cleansing in single- and dual-energy CT colonography

CT colonography (CTC) uses abdominal CT scans to examine the colon for cancers and polyps. To visualize the complete region of the colon without the obstructing residual materials inside the colon, an orally administered contrast agent is used to opacify the residual fecal materials on CT images, followed by virtual cleansing of the opacified materials from the images. We developed a self-supervised 3D generative adversarial network model based on residual blocks (ResBlocks), called 3D-ResNet-GAN, for performing electronic cleansing (EC) in CTC. In this model, the convolution layers of the generator network are implemented with ResBlocks for enhancing the EC performance of the generator network over that of our previously developed U-net-based 3D-GAN EC model. We compared the performance of the proposed selfsupervised 3D-ResNet-GAN EC scheme with that of the previous 3D-GAN EC scheme by the use of an anthropomorphic phantom and a clinical CTC case with submerged polyps in single-energy CTC and dual-energy CTC. Our preliminary quantitative evaluation based on the phantom indicated that the proposed 3D-ResNet-GAN EC scheme can yield a statistically significant improvement over the EC performance of our previous 3D-GAN EC scheme in both single-energy and dual-energy CTC. Our preliminary visual evaluation based on the clinical CTC case indicated that the use of ResBlocks and dual-energy CTC yields the best EC result.
Sistem pakar untuk Mendeteksi Kerusakan Forklift Menggunakan Forward Chaining

A forklift is a lift aircraft that functions to move, lower and lift goods from different heights that may not be accessible to humans. In the operation of a forklift if given load will definitely experience fatigue so that the machine will break down at any time. The problem that occurs is that many forklift operators cannot repair a forklift used when experiencing problems. Many forklift operators who do not know about the sign of damage. So that when the damage is minor, it can still be repaired by itself, but if the damage is already severe, at least the operator knows which parts are damaged and can be protected from fraud by irresponsible workshop mechanics. Expert system is one branch of artificial intelligence that defines how to move the ability of an expert in thinking and reasoning in solving problems and making decisions and conclusions from a number of facts.The method used in this research is forward chaining. In this method, the data is used to determine which rules will be run, then the rules will be run. It is used for the process of adding data to working memory. The process is then repeated until a result is found.
LibGuides: Online Library Orientation: Home

This guide contains resources to introduce students to the library and the research process.
A Framework for Monitoring Data from a Smart Home Environment

This master thesis presents the design and implementation of a framework for monitoringdata related to activities of daily living (ADL) in a smart home environment, conducted for theHuman Health an ...
The Help of Modern Technology and Applications for the Better Quality of Lessons and Greater Students' Motivation

Without modern information and communication technology (ICT), schools cannot be imagined today. ICT is embedded in all walks of life and with prudent use it can be used as a motivational tool in teaching, since it is close to young people. Schools are already well equipped with modern ICT gadgets. Different types of computers, tablets, interactive tables, etc. enable modern teaching. Even in physical education, ICT can help to increase adolescents' awareness of the importance of a healthy lifestyle, to greater motivation and proper performance. This contributes to faster learning and more interesting lessons. Repeatable video displays allow students to watch the exercises as many times as needed to be mastered. Heart rate monitors get them to exercise more effectively. Smart watches direct/encourage them to move regularly. The fact that young people spend more and more time with modern devices can be used as a motivating factor by linking it to quality applications. The use of modern ICT applications that promote step counting, drinking water, etc. bring students to a healthy lifestyle, in the way that is closest to them. This will make it easier to achieve the long-term goal - health and exercise. Experience shows that it is best to start using ICT right at the beginning of school year and to regularly use it, as well as to check on their activity/use, as this is how we create habits for young people for life.
Hypertext rersus intext

The article presents some insights on the special hypertext: in-text advertising. The author draws from her research on electronic hypertext. The way they dealt with the construction of nodal sites, resources, and text coherence nonlinear terms of the perception of a homogeneous non-linear style of the text. Intext is characterized as a conglomerate of texts with quite different target. Coherence of and cohesion of the original text is disturbed by highlighting the trigger words. Text ads can be easily activated - just zoom cursor. The address is then exposed to Information unrelated to the text that a Web page opened. Information technology is not so advanced as to avoid absurd connections with shape homonymy, polysemy wrong, ethical textual errors. It is noted that the idea of hypertext as non-linear arrangement of information, which goes toward divergent thinking is exploited by intext advertising.
Attention-based Domain Adaptation for Time Series Forecasting.

Recent years have witnessed deep neural networks gaining increasing popularity in the field of time series forecasting. A primary reason of their success is their ability to effectively capture complex temporal dynamics across multiple related time series. However, the advantages of these deep forecasters only start to emerge in the presence of a sufficient amount of data. This poses a challenge for typical forecasting problems in practice, where one either has a small number of time series, or limited observations per time series, or both. To cope with the issue of data scarcity, we propose a novel domain adaptation framework, Domain Adaptation Forecaster (DAF), that leverages the statistical strengths from another relevant domain with abundant data samples (source) to improve the performance on the domain of interest with limited data (target). In particular, we propose an attention-based shared module with a domain discriminator across domains as well as private modules for individual domains. This allows us to jointly train the source and target domains by generating domain-invariant latent features while retraining domain-specific features. Extensive experiments on various domains demonstrate that our proposed method outperforms state-of-the-art baselines on synthetic and real-world datasets.
Requirement Management in Product Development in B2B : A Study on the Process of Capturing Customer Requirements

Our world is ever-changing hence the surroundings ought to develop accordingly. This applies particularly to the product development process and therefore, it is of high priority to adapt to the ch ...
Piscopo: Ocasio-Cortez's fake accent ripe for 'SNL' parody

Saturday Night Live should take on AOC's adopted accent during Al Sharpton's National Action Network, said Joe Piscopo.
CAP-GAN: Towards_Adversarial_Robustness_with_Cycle-consistent_Attentional_Purification

Adversarial attack is aimed at fooling the target classifier with imperceptible perturbation. Adversarial examples, which are carefully crafted with a malicious purpose, can lead to erroneous predictions, resulting in catastrophic accidents. To mitigate the effects of adversarial attacks, we propose a novel purification model called CAP-GAN. CAP-GAN takes account of the idea of pixel-level and feature-level consistency to achieve reasonable purification under cycle-consistent learning. Specifically, we utilize the guided attention module and knowledge distillation to convey meaningful information to the purification model. Once a model is fully trained, inputs would be projected into the purification model and transformed into clean-like images. We vary the capacity of the adversary to argue the robustness against various types of attack strategies. On the CIFAR-10 dataset, CAP-GAN outperforms other pre-processing based defenses under both black-box and white-box settings.
Task-based support in search engines

Web search has become a key technology on which people rely daily for getting information about almost everything. The evolution of the search experience has also shaped the expectations of people about it. Many users seem to expect today's web search engines to behave like a kind of "wise interpreter," capable of understanding the meaning behind a search query, realizing its current context, and responding to it directly and appropriately. Search by meaning, or semantic search, encompasses a large portion of information retrieval (IR) research devoted to study more meaningful representations of the information need expressed by the user query. Entity cards, direct displays, and verticals are examples of how major commercial search engines have indeed responded to user expectations, capitalizing on query understanding. Search is usually performed with a specific goal underlying the query. In many cases, this goal consists of a nontrivial task to be completed. Current search engines support a small set of basic tasks, and most of the knowledge-intensive workload for supporting more complex tasks is left to the user. Task-based search can be viewed as an information access paradigm that aims to enhance search engines with functionalities for recognizing the underlying tasks in searches and providing support for task completion. The research presented in this thesis focuses on utilizing and extending methods and techniques from semantic search in the next stage of the evolution of search engines, namely, to support users in achieving their tasks. Our work can be grouped in three grand themes: (1) Entity type information for entity retrieval : we conduct a systematic evaluation and analysis of methods for type-aware entity retrieval, in terms of three main dimensions. Also, we revisit the problem of hierarchical target type identification, present a state-of-the-art supervised learning method, and analyze the usage of automatically identified target entity types for type-aware entity retrieval; (2) Entity-oriented search intents : we propose a categorization scheme for entity-oriented search intents, and study the distributions of entity intent categories per entity type. We further develop a method for constructing a knowledge base of entity-oriented search intents; and (3) Task-based search : we design a probabilistic generative framework for task-based query suggestion, and principledly estimate each of its components. Furthermore, we introduce the problems of query-based task recommendation and mission-based task recommendation, and establish respective methods as suitable baselines.
CIGMO: Learning categorical invariant deep generative models from grouped data

Images of general objects are often composed of three hidden factors: category (e.g., car or chair), shape (e.g., particular car form), and view (e.g., 3D orientation). While there have been many disentangling models that can discover either a category or shape factor separately from a view factor, such models typically cannot capture the structure of general objects that the diversity of shapes is much larger across categories than within a category. Here, we propose a novel generative model called CIGMO, which can learn to represent the category, shape, and view factors at once only with weak supervision. Concretely, we develop mixture of disentangling deep generative models, where the mixture components correspond to object categories and each component model represents shape and view in a category-specific and mutually invariant manner. We devise a learning method based on variational autoencoders that does not explicitly use label information but uses only grouping information that links together different views of the same object. Using several datasets of 3D objects including ShapeNet, we demonstrate that our model often outperforms previous relevant models including state-of-the-art methods in invariant clustering and one-shot classification tasks, in a manner exposing the importance of categorical invariant representation.
Learning inverse consistent 3D groupwise registration with deforming autoencoders

Robust groupwise registration methods are important for the analysis of large medical image datasets. We build upon the concept of deforming autoencoders that decouples shape and appearance to represent anatomical variability in a robust and plausible manner. In this work we propose a deep learning model that is trained to generate templates and deformation fields. It employs a joint encoder block which provides latent representations for both shape and appearance and is followed by two independent shape and appearance decoder paths. The model achieves image reconstruction by warping the template provided by the appearance decoder with the estimated warping field provided by the shape encoder. By restricting the embedding to a low-dimensional latent code, we are able to obtain meaningful deformable templates. Our objective function ensures smooth and realistic deformation fields. It contains an invertibility loss term, which is novel for deforming autoencoders and induces backward consistency. This should ensure that warping the reconstructed image with the deformation field ideally results in in the template. In addition, warping the template with the reversed deformation field should ideally produce the reconstructed image. We demonstrate the potential of our approach for application to two- and three-dimensional medical image data by training and evaluating it on labeled MRI brain scans. We show that adding the inverse consistency penalty to the objective function leads to improved and more robust registration results. When evaluated on unseen data with expert labels for accuracy estimation our three-dimensional model achieves substantially increased Dice scores by 5 percentage points.
The geometry of integration in text classification RNNs

Despite the widespread application of recurrent neural networks (RNNs), a unified understanding of how RNNs solve particular tasks remains elusive. In particular, it is unclear what dynamical patterns arise in trained RNNs, and how those pat-terns depend on the training dataset or task. This work addresses these questions in the context of text classification, building on earlier work studying the dynamics of binary sentiment-classification networks (Maheswaranathan et al., 2019). We study text-classification tasks beyond the binary case, exploring the dynamics ofRNNs trained on both natural and synthetic datasets. These dynamics, which we find to be both interpretable and low-dimensional, share a common mechanism across architectures and datasets: specifically, these text-classification networks use low-dimensional attractor manifolds to accumulate evidence for each class as they process the text. The dimensionality and geometry of the attractor manifold are determined by the structure of the training dataset, with the dimensionality reflecting the number of scalar quantities the network remembers in order to classify.In categorical classification, for example, we show that this dimensionality is one less than the number of classes. Correlations in the dataset, such as those induced by ordering, can further reduce the dimensionality of the attractor manifold; we show how to predict this reduction using simple word-count statistics computed on the training dataset. To the degree that integration of evidence towards a decision is a common computational primitive, this work continues to lay the foundation for using dynamical systems techniques to study the inner workings of RNNs.
A Multi-Modal and Multitask Benchmark in the Clinical Domain

Healthcare represents one of the most promising application areas for machine learning algorithms, including modern methods based on deep learning. Modern deep learning algorithms perform best on large datasets and on unstructured modalities such as text or image data; advances in deep learning have often been driven by the availability of such large datasets. Here, we introduce Multi-Modal Multitask MIMIC-III (M3) — a dataset and benchmark for evaluating machine learning algorithms in the healthcare domain. This dataset contains multi-modal patient data collected from intensive care units — including physiological time series, clinical notes, ECG waveforms, and tabular inputs — and defines six clinical tasks — including predicting mortality, decompensation, readmission, and other outcomes — which serve as benchmarks for comparing algorithms. We introduce new multi-modal and multitask models for this dataset, and show that they outperform previous state-of-the-art results that only rely on a subset of all tasks and modalities. This highlights the potential of multitask and multi-modal learning to improve the performance of algorithms in the healthcare domain. More generally, we envision M3 as a general resource that will help accelerate research in applying machine learning to healthcare.
Multi-scale Recalibration with Advanced Geometry Constraints for 3D Human Pose Estimation

Estimating 3D human poses from a single RGB image is a challenging task in computer vision due to depth ambiguity and lack of unconstrained 3D datasets. Poor exploiting of image cues will lead to inaccurate pose predictions. Previous works try to constrain illegal spatial relations and exploit 2D annotations of in-the-wild images as weak labels. In this paper, we propose to use multi-scale recalibration with stronger geometry constraints to regress 3D pose. The overall network is end-to-end which consists of a 2D pose estimation sub-network and a depth regression subnetwork. Firstly, by adding the residual multi-scale recalibration module to the depth regression sub-network, our approach works better on exploiting the structural information of human poses. Secondly, the advanced geometry constraints are introduced to restrict the human pose from the bone length and joint angle orientation, which shows a better result. Experimental results on the Human3.6m and in-the-wild MPII datasets show the effectiveness and robustness of our method.
Chaotic Encryption-Based Video Steganography Using Multiple Object Tracking

Steganography is the method of hiding secret information in another file, message, image, or video. The method of secretly embedding data into another medium and transmitting the data for security purposes has gained utmost importance in various walks of life. Data in the wrong hands may produce adverse effects on our society impacting peace and security. This paper aims at the implementation of a secure video steganography algorithm, wherein secret data is hidden within the frames in the video, using the principle of Multiple Object Tracking (MOT) and Chaotic Encryption. The motion-based MOT algorithm is implemented on the desired videos, in order to obtain the 'regions of interest' where data needs to be hidden. The data concealing process is carried out using a chaotic encryption algorithm, on the moving objects of interest in order to embed the secret data within the frames. The data extraction from the frames is done by the reversal of the process carried out for data embedding. The algorithm shows a good data hiding ratio, Peak Signal to Noise Ratio and no trace of the secret information hidden within the video is identified.
On the Robustness of Sentiment Analysis for Stock Price Forecasting

Machine learning (ML) models are known to be vulnerable to attacks both at training and test time. Despite the extensive literature on adversarial ML, prior efforts focus primarily on applications of computer vision to object recognition or sentiment analysis to movie reviews. In these settings, the incentives for adversaries to manipulate the model's prediction are often unclear and attacks require extensive control of direct inputs to the model. This makes it difficult to evaluate how severe the impact of vulnerabilities exposed is on systems deploying ML with little provenance guarantees for the input data. In this paper, we study adversarial ML with stock price forecasting. Adversarial incentives are clear and may be quantified experimentally through a simulated portfolio. We replicate an industry standard pipeline, which performs a sentiment analysis of Twitter data to forecast trends in stock prices. We show that an adversary can exploit the lack of provenance to indirectly use tweets to manipulate the model's perceived sentiment about a target company and in turn force the model to forecast price erroneously. Our attack is mounted at test time and does not modify the training data. Given past market anomalies, we conclude with a series of recommendations for the use of machine learning as input signal to trading algorithms.
Evaluation of the XRecorder Application via Video on Learning Al-Islam and Muhammadiyah in the Middle of the Covid-19 Pandemic

The purpose of the research is to evaluate E-learning in Al-Islam and Muhammadiyah 1 academic year 2020/2021 courses, where the website application provides learning data and information that can be accessed by students or students, without having to face the teacher. E-learning forms of Al-Islam and Muhammadiyah learning through XRecorder on WhatsApp at the Muhammadiyah University of Enrekang. Learning Al-Islam and Kemuhammadiyahan uses an Online learning system (E-learning) that is centralized and integrated into an information system based on the Moodle LMS (Learning Management System) website, to help students studying at the University of Muhammadiyah Enrekang in the lecture process and make it easier to get material and information related to Al-Islam and Muhammadiyah courses, in making the XRecoreder system on WhatsApp. The research method collects data and information about lecture materials as well as students and lecturers from the recorder via WA and the You Tube link. The results of the learning evaluation research found that XRecorder on WhatsApp supports the E-learning learning process, so it can be concluded that with the XRecorder application via Video it can make it easier for students to access information and lecture materials and lecturers in carrying out the lecture process at Muhammadiyah of  Enrekang University.
An Investigation on a Low-cost Machine Vision Measuring System for Precision Improvement

In this paper, we describe the investigation on a machine vision size measuring system to improve its precision on the basis of the inexpensive devices from the viewpoint of industrial applications. The uniformity and stability of the system were analyzed. The results showed the maximum gray value standard deviation of the edge as 2.6 pixels, and the maximum error of edge detection results was approximately 9 pixels (0.279 mm). The traditional noise reduction algorithms were applied to reduce random noise and dark current noise, and a novel uniform-background algorithm was proposed to improve the uniformity of image background. In addition, a calibration method based on the average gray value of the specified areas was developed to correct gray value errors of the left and right edges. A large number of experiments were carried out using the combined methods, the results showed that the measuring speed was approximately 1 piece per second, and the maximum error of lengths measured by the proposed method was within 1 μm, whereas the maximum error of uncalibrated results was about 0.25 mm. The measuring precision and speed of the proposed methods can meet the requirement of industrial applications.