Reviewerly/exHarmony huggingface dataset:
Treatment of Saline Water Using Electrocoagulation Process with Monopolar Connection of Electrodes

For the availability of drinking water, saline water treatment has become exceedingly necessary. The purpose of this research was to determine how efficient electrocoagulation (EC) with monopolar iron electrodes was for desalinating water from Iraq’s Sawa Lake. Absolute dissolved solids (TDS), chloride (Cl), bromine (Br), and sulfate (SO4) are some of the salty water forms that are being targeted. The impacts of five considerations on treatment efficiency were studied, including current (I), detention time (RT), pH, speed of mixing stirring (Mrpm), and inter-electrode distance (IED). I=0.8A, RT=80 minutes, pH=8, IED=1cm, and Mrpm=500 were found to be the best values. TDS, Cl, Br and SO4 removal efficiencies were 91 percent, 93 percent, 92 percent, and 90 percent, respectively, under ideal conditions. It can be inferred that the EC system used in this research was operative in removing salts from the water of Sawa lake.
Benchmarking the MAX78000 artificial intelligence microcontroller for deep learning applications

Hardware used for AI/ML applications has trended towards more powerful and more power hungry devices. Currently, GPUs and some FPGA datacenter accelerator cards can consume 200-300W at full load. This makes using these devices impractical in many edge-computing applications. Some semiconductor manufacturers are beginning to build AI-accelerated silicon to improve issues relating to not only power consumption, but also form factor and cost. We examine one such device - the MAX78000 Artificial Intelligence Microcontroller. With synthesis software provided by the manufacturer, this microcontroller can perform inference with models trained with high level software such as Pytorch or Tensorflow. Before synthesis, quantization is performed on the model weights, which allows the model to occupy a much smaller memory footprint and perform more efficient calculations, but decreases model accuracy. We attempt to measure the reduction in performance and accuracy degradation that should be expected for this device by benchmarking CNN (Convolutional Neural Network) inference on datasets such as MNIST,1 a dataset consisting of handwritten digits, and CIFAR-10,2 a dataset containing images divided into ten classes. We benchmark inference using models such as SimpleNet and models found through NAS (Neural Architecture Search) by adding batch processing of test data sets to code generated by the AI8X synthesis from the MAX78000 SDK. Using the performance and accuracy results from the testing of the aforementioned datasets and neural network models, we attempt to predict the feasibility of performing inference for such CNN use cases such as real-time image recognition and object detection. For each case we examine which commonly used algorithms are or are not feasible with the resources limitations of the MAX78000 SoC.
Work in Progress: Do Students Benefit From Using VR as a Tool in Engineering Exercises?

Current engineering students have to be prepared for their future work environment including the possible use of Virtual Reality (VR) in product development processes. The introduction of VR-support in engineering exercises at a university is accompanied by the question of how the use of the technology helps students with their engineering projects. Comparing student groups working on the same exercises with and without the help of VR leads to results, as presented in this paper, to further improve the teaching concept in future semesters.
EpiDepth: a real-time monocular dense-depth estimation pipeline using generic image rectification

Because of weight, power, and cost constraints, most unmanned aircraft systems (UAS) contain monocular camera systems. Real-time structure from motion (SfM) algorithms are required for monocular UAS systems to sense and autonomously navigate 3D environments. The SfM algorithm must be designed to work near real-time and handle the wide variety of possible extrinsic parameters produced by UAS image pairs. Common rigid epipolar rectification techniques (homography-based rectification) do not accurately model epipolar geometries with epipoles close to or contained within the image bounds. Common UAS movement types, translation along lens axis, tight radial turns, circular patterns with GPS locked camera focus, can all produce epipolar geometries with epipoles inside the camera frame. Using a generalized epipolar rectification technique, all extrinsic UAS movement types can be handled, and optimized image block matching techniques can be used to produce disparity/depth maps. The majority of UASs contain GPS/IMU/magnetometer modules. These modules provide absolute camera extrinsic parameters for every image. The essential and fundamental matrices between image pairs can be calculated from these absolute extrinsics. SfM is performed, depth images are produced, and the camera space pixel values can be projected as point clouds in three-dimensional space. These point clouds provide scene understanding that can be used for autonomous reasoning systems.
Deep learning architecture search for real-time image denoising

Deep learning has been widely used in recent years to accomplish many tasks such as image classification, natural language processing, and image denoising amongst others. However, the process to create deep neural networks by trial and error can often be very repetitive and time consuming and it is not clear if the entire network architecture space is explored towards finding an optimum architecture. This paper presents a systematic and automatic way to design or find an optimal architecture of deep neural networks. First, a sensitivity analysis is carried out on the parameters of interest of a network in order to identify those parameters which are most influential to the performance of the network. A search space is defined based on these parameters. Reinforcement learning is then used to find an optimal architecture within this search space. In this paper, our developed method of finding an optimal network architecture is applied to the problem of image denoising. In particular, the emphasis is placed on the Densely Connected Hierarchical Network (DHDN). A resulting network, named ENAS-DHDN, is shown to marginally outperform the original network suggesting that the original network is close to optimal. After finding an optimal network, it is used to estimate the time to process Standard Definition (SD) and High Definition (HD) videos with a frame rate of 30fps indicating that real-time video denoising at the SD resolution is achievable.
A holistic machine learning approach to identify performance anomalies in enterprise WiFi deployments

In large-scale enterprise WiFi deployments, performance anomalies need to be detected autonomously without human intervention. The state of the art in detection and diagnosis of faults is a manual and slow process. Experts with domain-knowledge use alarms, traces, and Key Performance Indicator (KPI) data to understand the anomalies. In large-scale deployments, this is becoming an increasingly inefficient approach. The objective of this study is to determine anomalies in a WiFi network by exploiting big data, machine learning (ML) and deep learning techniques. Our work comprises of setting up WiFi access points and retrieving the KPI of client devices which are connected to access points. The metrics for client devices, access points and channel nodes are periodically logged on the cloud, and collected through an API. We develop a multivariate time series data which undergoes data preprocessing, such as data cleaning and data transformation prior to using ML algorithms. To detect anomalous instances, an unsupervised algorithm, Density Based Spatial Clustering of Applications with Noise (DBSCAN) is used. Clusters obtained through DBSCAN are analyzed under dimensionality reduction technique t-Distributed Stochastic Neighbor Embedding. While DBSCAN helps to obtain the outliers, the output of the dimensionality reduction technique enable us to coherently comprehend the dynamics of clusters. Our methodology involves a thorough analysis of the outliers from the dataset through legacy supervised and self-supervised ML models, as well as ensemble learning techniques.
Fully convolutional network for breast lesion segmentation in ultrasound image: towards false positive reduction

Ultrasound scan (US) imagery is an important tool for radiologists to make a fast and reliable diagnosis decision about breast lesion status (benign or malignant). Accurate and automatic segmentation of breast lesion is critical for annotating the lesion characteristics such as margin smoothness and regularity in support of the diagnosis decision. Fully convolutional network (FCN) is one of the commonly used deep learning neural network methods for semantic segmentation. This paper is concerned with effective adaptation of the FCN solutions for segmenting breast lesions from 2D ultrasound images. The paper aims to first evaluate the existing FCN solution for solving the problem at hand and compare its performance with another popular method using U-Net. The paper then highlights one key issue with the FCN, i.e. false positive pixels near the boundary of a lesion and false positive pixels forming false lesions. The paper then investigates several methods in reducing such false positive pixels, including the use of data augmentation in training the classification model and use of loss functions in training the models. Experimental results using several data sets collected from various sources show that our adapted FCN method outperforms U-Net-based solutions in general and the false positive reduction methods we attempted have reduced the false positive pixels in both regions close to lesion boundary and separate from true lesion regions.
Expert System of Dengue Disease Using Artificial Neural Network Classifier

Abstract – Expert systems can be applied to the classification of dengue fever. Dengue is a serious disease that can be fatal if not diagnosed and treated properly. Headache, muscle aches, fever, and rash are some of the most prevalent symptoms. Dengue fever is a disease that is endemic in various South Asian and Southeast Asian nations. Dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome are the three types of dengue (DSS). Currently, these diseases may be classified using a machine learning approach with dengue symptoms as the input data. This study proposes implementing an Artificial Neural Network (ANN) with the Backpropagation (BPNN) algorithm as the classifier to categorize dengue types, divided into three categories: DF, DHF, and DSS. The dengue symptoms are represented by 21 attributes in the dataset. It was gathered from 110 patients. Cross-validation with k-fold 3, 5, and 10 were applied as the evaluation method. Three parameters were obtained to evaluate the ANN classification method: precision, recall, and accuracy. These were used to justify the most optimal performance. Cross-validation using k-fold 3 produced the best evaluation results, with precision, recall, and accuracy values of 97.3%, 97.3%, and 97.27%, respectively.
Towards Automated Screening of Literature on Artificial Intelligence in Nursing

We evaluate the performance of multiple text classification methods used to automate the screening of article abstracts in terms of their relevance to a topic of interest. The aim is to develop a system that can be first trained on a set of manually screened article abstracts before using it to identify additional articles on the same topic. Here the focus is on articles related to the topic "artificial intelligence in nursing". Eight text classification methods are tested, as well as two simple ensemble systems. The results indicate that it is feasible to use text classification technology to support the manual screening process of article abstracts when conducting a literature review. The best results are achieved by an ensemble system, which achieves a F1-score of 0.41, with a sensitivity of 0.54 and a specificity of 0.96. Future work directions are discussed.
A gaze-based study design to explore how competency evolves during a photo manipulation task

Share on A gaze-based study design to explore how competency evolves during a photo manipulation task Authors: Nora Castner Human-Computer Interaction/ Wilhelm-Schickard-Institute, University of Tübingen, Germany Human-Computer Interaction/ Wilhelm-Schickard-Institute, University of Tübingen, GermanyView Profile , Bela Umlauf Human - Computer Interaction Group, University of Tübingen, Germany Human - Computer Interaction Group, University of Tübingen, GermanyView Profile , Ard Kastrati Computer Engineering and Networks Laboratory, ETH Zurich, Switzerland Computer Engineering and Networks Laboratory, ETH Zurich, SwitzerlandView Profile , Martyna Beata Płomecka Methods of Plasticity Reasearch, University of Zurich, Switzerland Methods of Plasticity Reasearch, University of Zurich, SwitzerlandView Profile , William Schaefer University of Texas at San Antonio, United States University of Texas at San Antonio, United StatesView Profile , Enkelejda Kasneci University of Tubingen, Germany University of Tubingen, GermanyView Profile , Zoya Bylinskii Adobe Research, United States Adobe Research, United StatesView Profile Authors Info & Claims ETRA '22: 2022 Symposium on Eye Tracking Research and ApplicationsJune 2022 Article No.: 37Pages 1–3https://doi.org/10.1145/3517031.3531634Online:08 June 2022Publication History 0citation30DownloadsMetricsTotal Citations0Total Downloads30Last 12 Months30Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Novel L1 PCA informed K-means color quantization

In recent years, the computational power of handheld devices has increased rapidly to the point of parity with computers of only a generation ago. The multiple tools integrated into these devices and the progressive expansion of cloud storage have created a need for novel compressing techniques for both storage and transmission. In this work, a novel <i>L</i><sub>1</sub> principal component analysis (PCA) informed K-means approach is proposed. This new technique seeks to preserve the color definition of images through the application of K-means clustering algorithms. Assessment of the efficacy is carried out utilizing the structural similarity index (SSIM).
MediaRec: A Hybrid Media Recommender System

Abstract: This paper discusses about a hybrid recommendation platform for Movies, Books and Songs in one roof. A recommender system is a subgroup of information filtering systems that helps in predicting the “rating” or “Preference” that a user would give to any item. It also helps users to get media of their choice based on their experiences of self and other users in a productive and efficacious manner without wasting time in useless browsing. Previous approaches in recommender system (RS) include Content based filtering and Collaborative filtering. These approaches have a particular limitation as like the necessity of the user history as they visit. So as to overcome such dependencies, the Hybrid Recommendation System is introduced. It uses both Collaborative based filtering system and Content based filtering system for recommending media. In this way, the system performance will be greatly improved through the integration of these two. Keywords: Media Recommender System, Movies, Books, Songs, Recommender, TFIDF, Cosine Similarity, Pearson Correlation, KNN, K-Means Clustering.
Digital Line Segment Detection for Table Reconstruction in Document Images

Table detection is often involved in many applications of document analysis as tables are frequently used to present structured information. In this context, we are interested in extracting table regions in document images. More precisely, we propose a method for table detection based on a recent edge line detector which is developed in the context of digital geometry and it allows to handle noisy document images. The extracted lines are then used to reconstruct the tables contained in the image. The method has been evaluated and compared to other state-of-the-art methods and shown a very competitive result.
Sustainable Development Analysis Model Using Multi-objective Optimization and Regional Efficiency Evaluation

Regional efficiency refers to the ratio of the effective total output and the total input of regional factor resources under certain production technology conditions. At present, China’s traditional economic growth mode is based on the mode of sacrificing the environment for economic growth. At the same time, the influence and restriction of natural resources and natural environment on human social and economic development are more obvious than ever before. The contradiction between social and economic development, resources, and environmental preservation has become increasingly prominent. Problems such as resource shortage, environmental pollution, and ecological imbalance have exposed the disadvantages of the traditional development model, and so human beings have begun to seek new high-quality development models. Therefore, the research on regional efficiency in this paper is based on the idea of coordinated development of resources, environment and social economy, and multi-objective optimization algorithm, which constructs the evaluation index system of regional efficiency, comprehensively measures the regional efficiency, and inspects the development status. Evaluating the regional efficiency is conducive to analyzing the problems existing in regional development, saving resources, and promoting the sustainable development of the region.
Faster, Better Blink Detection through Curriculum Learning by Augmentation

Blinking is a useful biological signal that can gate gaze regression models to avoid the use of incorrect data in downstream tasks. Existing datasets are imbalanced both in frequency of class but also in intra-class difficulty which we demonstrate is a barrier for curriculum learning. We thus propose a novel curriculum augmentation scheme that aims to address frequency and difficulty imbalances implicitly which are are terming Curriculum Learning by Augmentation (CLbA).
Deep adaptive convolutional neural network for near infrared and thermal face recognition

Deep Learning algorithms have been widely used for different surveillance tasks in recent years, including people monitoring and counting, abnormal behavior identification, and video segmentation. In most situations, it is assumed that the input images are of high visual quality to provide good performance. When the input data is degraded by variables such as high noise or poor lighting conditions accuracy may degrade. We address the illumination issue in this paper by adapting a face recognition algorithm to near-infrared and thermal images. In this study, we propose a fine-tuning approach to allow deep CNN models to be applied to infrared face recognition (NIR and thermal spectrum). The obtained results with the proposed architecture and infrared images show promising results in deep face recognition with a VAR of 96.68% for the NIR dataset and a VAR of 94.57% for the thermal dataset.
Content-filtering AI systems–limitations, challenges and regulatory approaches

Online service providers, and even governments, have increasingly relied on Artificial Intelligence (‘AI’) to regulate content on the internet. In some jurisdictions, the law has incentivised, if not obligated, service providers to adopt measures to detect, track, and remove objectionable content such as terrorist propaganda. Consequently, service providers are being pushed to use AI to moderate online content. However, content-filtering AI systems are subject to limitations that affect their accuracy and transparency. These limitations open the possibility for legitimate content to be removed and objectionable content to remain online. Such an outcome could endanger human well-being and the exercise of our human rights. In view of these challenges, we argue that the design and use of content-filtering AI systems should be regulated. AI ethics principles such as transparency, explainability, fairness, and human-centricity should guide such regulatory efforts.
Memory Offloading for Remote Attestation of Multi-Service IoT Devices

Remote attestation (RA) is an effective malware detection mechanism that allows a trusted entity (Verifier) to detect a potentially compromised remote device (Prover). The recent research works are proposing advanced Control-Flow Attestation (CFA) protocols that are able to trace the Prover’s execution flow to detect runtime attacks. Nevertheless, several memory regions remain unattested, leaving the Prover vulnerable to data memory and mobile adversaries. Multi-service devices, whose integrity is also dependent on the integrity of any attached external peripheral devices, are particularly vulnerable to such attacks. This paper extends the state-of-the-art RA schemes by presenting ERAMO, a protocol that attests larger memory regions by adopting the memory offloading approach. We validate and evaluate ERAMO with a hardware proof-of-concept implementation using a TrustZone-capable LPC55S69 running two sensor nodes. We enhance the protocol by providing extensive memory analysis insights for multi-service devices, demonstrating that it is possible to analyze and attest the memory of the attached peripherals. Experiments confirm the feasibility and effectiveness of ERAMO in attesting dynamic memory regions.
Unsupervised Learning for Maximum Consensus Robust Fitting: A Reinforcement Learning Approach

Robust model fitting is a core algorithm in several computer vision applications. Despite being studied for decades, solving this problem efficiently for datasets that are heavily contaminated by outliers is still challenging: due to the underlying computational complexity. A recent focus has been on learning-based algorithms. However, most of these approaches are supervised (which require a large amount of labelled training data). In this paper, we introduce a novel unsupervised learning framework: that learns to directly (without labelled data) solve robust model fitting. Moreover, unlike other learning-based methods, our work is agnostic to the underlying input features, and can be easily generalized to a wide variety of LP-type problems with quasi-convex residuals. We empirically show that our method outperforms existing (un)supervised learning approaches, and also achieves competitive results compared to traditional (non-learning-based) methods. Our approach is designed to try to maximise consensus (MaxCon), similar to the popular RANSAC. The basis of our approach, is to adopt a Reinforcement Learning framework. This requires designing appropriate reward functions, and state encodings. We provide a family of reward functions, tunable by choice of a parameter. We also investigate the application of different basic and enhanced Q-learning components.
Adaptive 5G systems for interactive volumetric sports analysis in augmented reality

Remote coaching for sports is challenged by the lack of 3D spatial communication. While athletes send live or recorded videos to their coaches, these 2D representations fail to capture the spatial relationships of the body, limiting the ability to understand timing, weight distribution, and smoothness in an athletic movement. This demonstration presents Augmented Coach, an AR sports coaching platform for coaches to remotely view, manipulate, and annotate athletic movements in 3D augmented space. Also, this demonstration provides an adaptive platform to study real-time efficient volumetric data transmission between remotely connected devices, including over 5G cellular networks.