Reviewerly/exHarmony huggingface dataset:
The Effect of Different Dimensionality Reduction Techniques on Machine Learning Overfitting Problem

In most conditions, it is a problematic mission for a machine-learning model with a data record, which has various attributes, to be trained.There is always a proportional relationship between the increase of model features and the arrival to the overfitting of the susceptible model.That observation occurred since not all the characteristics are always important.For example, some features could only cause the data to be noisier.Dimensionality reduction techniques are used to overcome this matter.This paper presents a detailed comparative study of nine dimensionality reduction methods.These methods are missing-values ratio, low variance filter, highcorrelation filter, random forest, principal component analysis, linear discriminant analysis, backward feature elimination, forward feature construction, and rough set theory.The effects of used methods on both training and testing performance were compared with two different datasets and applied to three different models.These models are, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest classifier (RFC).The results proved that the RFC model was able to achieve the dimensionality reduction via limiting the overfitting crisis.The introduced RFC model showed a general progress in both accuracy and efficiency against compared approaches.The results revealed that dimensionality reduction could minimize the overfitting process while holding the performance so near to or better than the original one.
Real-time Mixed Reality Teleconsultation for Intensive Care Units in Pandemic Situations

Intensive care units (ICUs) that host patients infected by the SARS-CoV-2 Coronavirus are separated from other care units. In regular ICU ward rounds, a number of experts from different medical areas and expertise are present to discuss the situation and care of the patient. However, this procedure is contrasting COVID-19 measures such as reduced contact and personnel traffic. In this project, we demonstrate a system for mixed reality (MR) teleconsultation to support ICU wards (ARTEKMED). Through ARTEKMED, remote experts can join a local 3D reconstructed ICU visit in virtual reality (VR) and support local experts that are equipped with an augmented reality (AR) system. Our goal is to reduce personnel traffic and in turn the risk of an infection spread.
Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.
Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition

Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse.Existing models fail to fully utilize the contextual information which plays an important role in interpreting each local sentence.In this paper, we thus propose a novel graph-based Context Tracking Network (CT-Net) to model the discourse context for IDRR.The CT-Net firstly converts the discourse into the paragraph association graph (PAG), where each sentence tracks their closely related context from the intricate discourse through different types of edges.Then, the CT-Net extracts contextual representation from the PAG through a specially designed cross-grained updating mechanism, which can effectively integrate both sentence-level and token-level contextual semantics.Experiments on PDTB 2.0 show that the CT-Net gains better performance than models that roughly model the context.
Real-time embedded object detection and tracking system in Zynq SoC

Abstract With the increasing application of computer vision technology in autonomous driving, robot, and other mobile devices, more and more attention has been paid to the implementation of target detection and tracking algorithms on embedded platforms. The real-time performance and robustness of algorithms are two hot research topics and challenges in this field. In order to solve the problems of poor real-time tracking performance of embedded systems using convolutional neural networks and low robustness of tracking algorithms for complex scenes, this paper proposes a fast and accurate real-time video detection and tracking algorithm suitable for embedded systems. The algorithm combines the object detection model of single-shot multibox detection in deep convolution networks and the kernel correlation filters tracking algorithm, what is more, it accelerates the single-shot multibox detection model using field-programmable gate arrays, which satisfies the real-time performance of the algorithm on the embedded platform. To solve the problem of model contamination after the kernel correlation filters algorithm fails to track in complex scenes, an improvement in the validity detection mechanism of tracking results is proposed that solves the problem of the traditional kernel correlation filters algorithm not being able to robustly track for a long time. In order to solve the problem that the missed rate of the single-shot multibox detection model is high under the conditions of motion blur or illumination variation, a strategy to reduce missed rate is proposed that effectively reduces the missed detection. The experimental results on the embedded platform show that the algorithm can achieve real-time tracking of the object in the video and can automatically reposition the object to continue tracking after the object tracking fails.
A simulation-based evaluation of machine learning models for clinical decision support: application and analysis using hospital readmission

Abstract The interest in applying machine learning in healthcare has grown rapidly in recent years. Most predictive algorithms requiring pathway implementations are evaluated using metrics focused on predictive performance, such as the c statistic. However, these metrics are of limited clinical value, for two reasons: (1) they do not account for the algorithm’s role within a provider workflow; and (2) they do not quantify the algorithm’s value in terms of patient outcomes and cost savings. We propose a model for simulating the selection of patients over time by a clinician using a machine learning algorithm, and quantifying the expected patient outcomes and cost savings. Using data on unplanned emergency department surgical readmissions, we show that factors such as the provider’s schedule and postoperative prediction timing can have major effects on the pathway cohort size and potential cost reductions from preventing hospital readmissions.
ProcessAR: An augmented reality-based tool to create in-situ procedural 2D/3D AR Instructions

Augmented reality (AR) is an efficient form of delivering spatial information and has great potential for training workers. However, AR is still not widely used for such scenarios due to the technical skills and expertise required to create interactive AR content. We developed ProcessAR, an AR-based system to develop 2D/3D content that captures subject matter expert's (SMEs) environment-object interactions in situ. The design space for ProcessAR was identified from formative interviews with AR programming experts and SMEs, alongside a comparative design study with SMEs and novice users. To enable smooth workflows, ProcessAR locates and identifies different tools/objects through computer vision within the workspace when the author looks at them. We explored additional features such as embedding 2D videos with detected objects and user-adaptive triggers. A final user evaluation comparing ProcessAR and a baseline AR authoring environment showed that, according to our qualitative questionnaire, users preferred ProcessAR.
Laina: Dynamic Data Physicalization for Slow Exercising Feedback

The increased popularity of recreational sports, like running, led to the development of numerous technologies supporting people in their training. However, in their current form and interaction, these take a rather standardized approach focusing on quantified data tracking displayed through screens or audio. In this paper, we explore how dynamic data physicalization through a shape-changing interface can open the design space of exercise feedback. Relying on an expert study on the aesthetics of interaction (N=23), we designed Laina, a shape-changing art piece presenting physicalized running data through a slow feedback mechanism. We deployed Laina at 3 participant's home, during a series of 3-weeks field studies. Results show that Laina allows for deep reflection, anticipation and exploration of running behavior. The aim of our paper is to provide insights on the use of slow feedback mechanisms for exercise-related products, through the design of a dynamic data physicalization artefact.
Theorems for free from separation logic specifications

Separation logic specifications with abstract predicates intuitively enforce a discipline that constrains when and how calls may be made between a client and a library. Thus a separation logic specification of a library intuitively enforces a protocol on the trace of interactions between a client and the library. We show how to formalize this intuition and demonstrate how to derive "free theorems" about such interaction traces from abstract separation logic specifications. We present several examples of free theorems. In particular, we prove that a so-called logically atomic concurrent separation logic specification of a concurrent module operation implies that the operation is linearizable. All the results presented in this paper have been mechanized and formally proved in the Coq proof assistant using the Iris higher-order concurrent separation logic framework.
GhostCell: separating permissions from data in Rust

The Rust language offers a promising approach to safe systems programming based on the principle of aliasing XOR mutability : a value may be either aliased or mutable, but not both at the same time. However, to implement pointer-based data structures with internal sharing, such as graphs or doubly-linked lists, we need to be able to mutate aliased state. To support such data structures, Rust provides a number of APIs that offer so-called interior mutability : the ability to mutate data via method calls on a shared reference. Unfortunately, the existing APIs sacrifice flexibility, concurrent access, and/or performance, in exchange for safety. In this paper, we propose a new Rust API called GhostCell which avoids such sacrifices by separating permissions from data : it enables the user to safely synchronize access to a collection of data via a single permission. GhostCell repurposes an old trick from typed functional programming: branded types (as exemplified by Haskell’s ST monad), which combine phantom types and rank-2 polymorphism to simulate a lightweight form of state-dependent types. We have formally proven the soundness of GhostCell by adapting and extending RustBelt, a semantic soundness proof for a representative subset of Rust, mechanized in Coq.
Corona Virus Pandemic Paving Ways to Next Generation of Learning and Teaching: Futuristic Cloud Based Educational Model

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A Comprehensive Survey of Attacks without Physical Access Targeting Hardware Vulnerabilities in IoT/IIoT Devices, and Their Detection Mechanisms

With the advances in the field of the Internet of Things (IoT) and Industrial IoT (IIoT), these devices are increasingly used in daily life or industry. To reduce costs related to the time required to develop these devices, security features are usually not considered. This situation creates a major security concern. Many solutions have been proposed to protect IoT/IIoT against various attacks, most of which are based on attacks involving physical access. However, a new class of attacks has emerged targeting hardware vulnerabilities in the micro-architecture that do not require physical access. We present attacks based on micro-architectural hardware vulnerabilities and the side effects they produce in the system. In addition, we present security mechanisms that can be implemented to address some of these attacks. Most of the security mechanisms target a small set of attack vectors or a single specific attack vector. As many attack vectors exist, solutions must be found to protect against a wide variety of threats. This survey aims to inform designers about the side effects related to attacks and detection mechanisms that have been described in the literature. For this purpose, we present two tables listing and classifying the side effects and detection mechanisms based on the given criteria.
In Situ Visualization for 3D Ultrasound-Guided Interventions with Augmented Reality Headset

Augmented Reality (AR) headsets have become the most ergonomic and efficient visualization devices to support complex manual tasks performed under direct vision. Their ability to provide hands-free interaction with the augmented scene makes them perfect for manual procedures such as surgery. This study demonstrates the reliability of an AR head-mounted display (HMD), conceived for surgical guidance, in navigating in-depth high-precision manual tasks guided by a 3D ultrasound imaging system. The integration between the AR visualization system and the ultrasound imaging system provides the surgeon with real-time intra-operative information on unexposed soft tissues that are spatially registered with the surrounding anatomic structures. The efficacy of the AR guiding system was quantitatively assessed with an in vitro study simulating a biopsy intervention aimed at determining the level of accuracy achievable. In the experiments, 10 subjects were asked to perform the biopsy on four spherical lesions of decreasing sizes (10, 7, 5, and 3 mm). The experimental results showed that 80% of the subjects were able to successfully perform the biopsy on the 5 mm lesion, with a 2.5 mm system accuracy. The results confirmed that the proposed integrated system can be used for navigation during in-depth high-precision manual tasks.
A Hardware Accelerator for Protocol Buffers

Serialization frameworks are a fundamental component of scale-out systems, but introduce significant compute overheads. However, they are amenable to acceleration with specialized hardware. To understand the trade-offs involved in architecting such an accelerator, we present the first in-depth study of serialization framework usage at scale by profiling Protocol Buffers (“protobuf”) usage across Google’s datacenter fleet. We use this data to build HyperProtoBench, an open-source benchmark representative of key serialization-framework user services at scale. In doing so, we identify key insights that challenge prevailing assumptions about serialization framework usage.
Cross-lingual Aspect-based Sentiment Analysis with Aspect Term Code-Switching

Many efforts have been made in solving the Aspect-based sentiment analysis (ABSA) task.While most existing studies focus on English texts, handling ABSA in resource-poor languages remains a challenging problem.In this paper, we consider the unsupervised crosslingual transfer for the ABSA task, where only labeled data in the source language is available and we aim at transferring its knowledge to the target language having no labeled data.To this end, we propose an alignment-free label projection method to obtain high-quality pseudolabeled data of the target language with the help of the translation system, which could preserve more accurate task-specific knowledge in the target language.For better utilizing the source and translated data, as well as enhancing the cross-lingual alignment, we design an aspect code-switching mechanism to augment the training data with code-switched bilingual sentences.To further investigate the importance of language-specific knowledge in solving the ABSA problem, we distill the above model on the unlabeled target language data which improves the performance to the same level of the supervised method.
A Hybrid Deep Learning Model for Automatic Modulation Classification

Automatic modulation classification (AMC) is one of the major challenges for cognitive radio (CR), which can enhance the spectrum utilization efficiency. In this study, a hybrid signal and image-based deep learning model is designed for AMC in CR. A convolutional neural network (CNN) is applied in both the deep learning models. The signal-based CNN (SBCNN) is designed with the optimal filter size for the prediction accuracy, which is used as a pre-training deep learning network to extract features with size <inline-formula> <tex-math notation="LaTeX">$24\times 1$ </tex-math></inline-formula>. The features extracted by SBCNN are converted into heat map images, which showed RGB images in the scale range of &#x2212;30 to &#x002B;30. Finally, the images are utilized for training and testing the image-based CNN (IBCNN). The dataset used for the experiment is DeepSig: RADIOML2018.01A, which is the latest version. For the IBCNN, the prediction accuracy is 1.96&#x0025;, 7.99&#x0025;, and 4.63&#x0025; higher at signal-to-noise ratio (SNR) 10 dB, and 3.26&#x0025;, 6.4&#x0025;, and 4.13&#x0025; higher at SNR 0 dB as compared to conventional models: ECNN, SCGNet, and LCNN, respectively.
Egocentric videoconferencing

We introduce a method for egocentric videoconferencing that enables hands-free video calls, for instance by people wearing smart glasses or other mixed-reality devices. Videoconferencing portrays valuable non-verbal communication and face expression cues, but usually requires a front-facing camera. Using a frontal camera in a hands-free setting when a person is on the move is impractical. Even holding a mobile phone camera in the front of the face while sitting for a long duration is not convenient. To overcome these issues, we propose a low-cost wearable egocentric camera setup that can be integrated into smart glasses. Our goal is to mimic a classical video call, and therefore, we transform the egocentric perspective of this camera into a front facing video. To this end, we employ a conditional generative adversarial neural network that learns a transition from the highly distorted egocentric views to frontal views common in videoconferencing. Our approach learns to transfer expression details directly from the egocentric view without using a complex intermediate parametric expressions model, as it is used by related face reenactment methods. We successfully handle subtle expressions, not easily captured by parametric blendshape-based solutions, e.g., tongue movement, eye movements, eye blinking, strong expressions and depth varying movements. To get control over the rigid head movements in the target view, we condition the generator on synthetic renderings of a moving neutral face. This allows us to synthesis results at different head poses. Our technique produces temporally smooth video-realistic renderings in real-time using a video-to-video translation network in conjunction with a temporal discriminator. We demonstrate the improved capabilities of our technique by comparing against related state-of-the art approaches.
An Improved multi-objective a-star algorithm for path planning in a large workspace: Design, Implementation, and Evaluation

Improved path planning algorithms should minimize algorithm processing time, increase path smoothness, and shorten path length, all of which will be extremely beneficial for mobile robot traversal in large workspaces. As a result, an improved multi-objective A-star (IMOA-star) algorithm for mobile robot path planning in a large workspace was designed and implemented in Python 3.8.3 in this study. In four test cases, the proposed IMOA-star is evaluated in a large workspace with dimensions of 7120 cm × 9490 cm, and its performance is compared to the traditional A-star. When compared to the traditional A-star, the results showed that IMOA-star reduced the algorithm process time by 99.98%, improved path smoothness by 45%, reduced path length by 1.58%, and reduced the number of random points by 83.45%. Finally, the IMOA-star outperforms the traditional A-star in terms of algorithm processing time, path smoothness, path length, and the number of random points. As a result, it should be considered a viable alternative to the traditional A-star for mobile robot path planning in a large workspace.
A BERT-Based Hybrid Short Text Classification Model Incorporating CNN and Attention-Based BiGRU

Short text classification is a research focus for natural language processing (NLP), which is widely used in news classification, sentiment analysis, mail filtering and other fields. In recent years, deep learning techniques are applied to text classification and has made some progress. Different from ordinary text classification, short text has the problem of less vocabulary and feature sparsity, which raise higher request for text semantic feature representation. To address this issue, this paper propose a feature fusion framework based on the Bidirectional Encoder Representations from Transformers (BERT). In this hybrid method, BERT is used to train word vector representation. Convolutional neural network (CNN) capture static features. As a supplement, a bi-gated recurrent neural network (BiGRU) is adopted to capture contextual features. Furthermore, an attention mechanism is introduced to assign the weight of salient words. The experimental results confirmed that the proposed model significantly outperforms the other state-of-the-art baseline methods.