Reviewerly/exHarmony huggingface dataset:
Data modeling to obtain a classifier for recognizing the output feature

This article discusses the application of machine learning in the medical field, namely, the classification of acute pancreatitis. This disease was chosen as a priority in the solution for a number of reasons: the presence of subjective indicators on the part of the patient, the requirement for a high speed of provision of the necessary treatment in accordance with the set severity, the lack of the necessary equipment for the correct diagnosis of the severity. Thus, in the course of the study, a classifier was built that recognizes severe and mild severity. With the help of the selection of the model's hyper-parameters, it happened that at each stage of model optimization, the leading kernel was the radial-basis function. Based on the results of the selection of the feature space by the thinning method for each core, sets of features that are different from each other, both in quality and quantity, were identified. Thus, the radial basis function selected for itself the average in quantity and quality between the polynomial kernel and the hyperbolic tangent. At the end of the study, to check the accuracy of the adjusted regressions, a rolling exam was conducted, during which certain indicators were established for the 1vR and 3vR regressions, which led to the conclusion that the first and third class labels are linearly separable, but the second class label cannot be distinguished. Moreover, the results of the two regressions in the aggregate clearly distinguish three clusters.
Two-color Kerr microscopy of two-dimensional materials with sub-picosecond time resolution

We present a two-color Kerr microscopy system based on two electronically synchronized erbium-fiber laser oscillators with independently tunable emission energies spanning most of the visible spectrum. Combining a spatial resolution below 2 $\mu$m and sub-ps time resolution with high sensitivity and cryogenic sample temperatures, it is ideally suited for studying spin and valley dynamics in a wide range of two-dimensional materials. We illustrate its capabilities by studying a monolayer of the common semiconducting transition metal disulfide MoS$_2$.
Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation

Emerging 3D-related technologies such as augmented reality, virtual reality, mixed reality, and stereoscopy have gained remarkable growth due to their numerous applications in the entertainment, gaming, and electromedical industries. In particular, the 3D television (3DTV) and free-viewpoint television (FTV) enhance viewers’ television experience by providing immersion. They need an infinite number of views to provide a full parallax to the viewer, which is not practical due to various financial and technological constraints. Therefore, novel 3D views are generated from a set of available views and their depth maps using depth-image-based rendering (DIBR) techniques. The quality of a DIBR-synthesized image may be compromised for several reasons, e.g., inaccurate depth estimation. Since depth is important in this application, inaccuracies in depth maps lead to different textural and structural distortions that degrade the quality of the generated image and result in a poor quality of experience (QoE). Therefore, quality assessment DIBR-generated images are essential to guarantee an appreciative QoE. This paper aims at estimating the quality of DIBR-synthesized images and proposes a novel 3D objective image quality metric. The proposed algorithm aims to measure both textural and structural distortions in the DIBR image by exploiting the contrast sensitivity and the Hausdorff distance, respectively. The two measures are combined to estimate an overall quality score. The experimental evaluations performed on the benchmark MCL-3D dataset show that the proposed metric is reliable and accurate, and performs better than existing 2D and 3D quality assessment metrics.
Edit - Run Behavior in Programming and Debugging

As developers program and debug, they continuously edit and run their code, a behavior known as edit-run cycles. While techniques such as live programming are intended to support this behavior, little is known about the characteristics of edit-run cycles themselves. To bridge this gap, we analyzed 28 hours of programming and debugging work from 11 professional developers which encompassed over three thousand development activities. We mapped activities to edit or run steps, constructing 581 debugging and 207 programming edit-run cycles. We found that edit-run cycles are frequent. Developers edit and run the program, on average, 7 times before fixing a defect and twice before introducing a defect. Developers waited longer before again running the program when programming than debugging, with a mean cycle length of 3 minutes for programming and 1 minute for debugging. Most cycles involved an edit to a single file after which a developer ran the program to observe the impact on the final output. Edit-run cycles which included activities beyond edit and run, such as navigating between files, consulting resources, or interacting with other IDE features, were much longer, with a mean length of 5 minutes, rather than 1.5 minutes. We conclude with a discussion of design recommendations for tools to enable more fluidity in edit-run cycles.
Implementation of Hololens2-Based Augmented Reality Technology in Non-automated Warehouse Picking

To solve the problem of low efficiency of picking in non-automated logistics warehousing, this paper proposes an augmented reality application ‐‐ logistics warehousing picking system based on HoloLens 2. In this application, the administrator operates the background management system to manage the background data, and the staff completes the picking according to the visual picking function based on the HoloLens 2 device. In order to verify the effectiveness of the method, the method is implemented and an experiment is carried out. The experimental results show that the picking operation based on HoloLens augmented reality technology is more efficient than that based on traditional paper orders.
Anomaly Detection with Unknown Anomalies: Application to Maritime Machinery

We present a framework for deriving anomaly detection algorithms on timeseries data when the time and expression of anomalous behaviour is unknown. The framework is suited for problems in which individual machine learning paradigms cannot be directly implemented: supervised learning is not applicable due to the lack of labelled data, unsupervised learning is not effective since the normal operations are insufficiently defined and take complex and diverse forms, and deep learning risks confusing problematic behaviours for expected ones due to the possible repetitiveness of similar anomalies. The proposed approach is comprised of two phases: unsupervised discovery of anomalies, and semi-supervised construction and tuning of the anomaly detection algorithm. By leveraging data exploration methods and expert knowledge, the resulting algorithms are interpretable and detect a wide range of anomalous behaviours. The approach is applied to the early detection of wear and tear of maritime propulsion and manoeuvring machinery. We show that the final algorithm is able to detect different types of anomalies, including an actual internal leakage in a thruster which is otherwise overlooked by the present rule-based alarm system.
Selecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score

Cluster analysis requires many decisions: the clustering method and the implied reference model, the number of clusters and, often, several hyper-parameters and algorithms' tunings. In practice, one produces several partitions, and a final one is chosen based on validation or selection criteria. There exist an abundance of validation methods that, implicitly or explicitly, assume a certain clustering notion. Moreover, they are often restricted to operate on partitions obtained from a specific method. In this paper, we focus on groups that can be well separated by quadratic or linear boundaries. The reference cluster concept is defined through the quadratic discriminant score function and parameters describing clusters' size, center and scatter. We develop two cluster-quality criteria called quadratic scores. We show that these criteria are consistent with groups generated from a general class of elliptically-symmetric distributions. The quest for this type of groups is common in applications. The connection with likelihood theory for mixture models and model-based clustering is investigated. Based on bootstrap resampling of the quadratic scores, we propose a selection rule that allows choosing among many clustering solutions. The proposed method has the distinctive advantage that it can compare partitions that cannot be compared with other state-of-the-art methods. Extensive numerical experiments and the analysis of real data show that, even if some competing methods turn out to be superior in some setups, the proposed methodology achieves a better overall performance.
A Machine Learning Approach for Item Recommendation Using SVD Technique

Today recommender system plays a very prominent role in suggesting and offering products to their customers on the basis of their interests and choices. We encounter these systems in our day to day life while interacting with several online based services. There are several models employed in various e-commerce based systems like amazon, flipkart etc. Our proposed architecture in this paper deploys a machine learning approach for item recommendation making use of collaborative based model. We are following the model based approach using the concept of singular value decomposition (SVD) for getting the ratings of unrated items and minimizing the sparsity problem. Our work also focus on calculating the Root Mean Square Error (RMSE) of our proposed system to check it's accuracy.
Privacy Preserving Location-based Content Distribution Framework for Digital Rights Management Systems

Advancement in network technology provides an opportunity for e-commerce industries to sell digital content. However, multimedia content has the drawback of easy copy and redistribution, which causes rampant piracy. Digital rights management (DRM) systems are developed to address content piracy. Basically, DRM focuses to control content consumption and distribution. In general, to provide copyright protection, DRM system loses flexibility and creates a severe threat to users’ privacy. Moreover, traditional DRM systems are client-server architecture, which cannot handle strategies geographically. These disadvantages discourage the adoption of DRM systems. At the same time, multi-distributor DRM (MD-DRM) system provides a way to facilitate content distribution more effectively. Most of the existing multi-distributor DRM systems are privacy encroaching and do not discuss the useful content distribution framework. To overcome the drawbacks of existing schemes, we propose a privacy-preserving MD-DRM system, which is flexible enough to support location-based content distribution. The proposed scheme maintains a flexible and transparent content distribution without breaching consumer privacy. Besides, the proposed scheme does not violate accountability parameters. This mechanism makes traitor identification possible without violating the privacy rights of authorized consumers.
Hypergraph Ontology Sparse Vector Representation and Its Application to Ontology Learning

Data representation is the first step in computer science modeling, and the performance of the algorithm directly affects the efficiency of the algorithm. For structural data, graphs and hypergraph representations are often ideal choices, which can effectively represent the connections between data. The edge weight function can also be used to express the strength of the connection, and the adjacency matrix and the Laplacian matrix describe a kind of motion, and the eigenvalues of the matrix reveal the motion trajectory of the vector. In this paper, hypergraph framework is used to characterize the internal structure of the ontology sparse vector, and use the hypergraph theory to define a new hypergraph Laplacian matrix, and thus obtain the control items on the sparsity of the ontology sparse vector learning model. Then, a new algorithm is proposed. Finally, two specific implement tests are used to verify the effectiveness of our new proposed hypergraph based ontology learning algorithm.
Hierarchical Regression and Classification for Accurate Object Detection

Accurate object detection requires correct classification and high-quality localization. Currently, most of the single shot detectors (SSDs) conduct simultaneous classification and regression using a fully convolutional network. Despite high efficiency, this structure has some inappropriate designs for accurate object detection. The first one is the mismatch of bounding box classification, where the classification results of the default bounding boxes are improperly treated as the results of the regressed bounding boxes during the inference. The second one is that only one-time regression is not good enough for high-quality object localization. To solve the problem of classification mismatch, we propose a novel reg-offset-cls (ROC) module including three hierarchical steps: the regression of the default bounding box, the prediction of new feature sampling locations, and the classification of the regressed bounding box with more accurate features. For high-quality localization, we stack two ROC modules together. The input of the second ROC module is the output of the first ROC module. In addition, we inject a feature enhanced (FE) module between two stacked ROC modules to extract more contextual information. The experiments on three different datasets (i.e., MS COCO, PASCAL VOC, and UAVDT) are performed to demonstrate the effectiveness and superiority of our method. Without any bells or whistles, our proposed method outperforms state-of-the-art one-stage methods at a real-time speed. The source code is available at https://github.com/JialeCao001/HSD.
Explicit Non-normal Modal Logic

Faroldi argues that deontic modals are hyperintensional and thus traditional modal logic cannot provide an appropriate formalization of deontic situations. To overcome this issue, we introduce novel justification logics as hyperintensional analogues to non-normal modal logics. We establish soundness and completness with respect to various models and we study the problem of realization.
CRAlert: Hardware-Assisted Code Reuse Attack Detection

Code reuse attack (CRA) hijacking the control flow of programs for malicious actions without injecting any codes, has brought great concerns to system security. Most existing defenses incur high performance overhead or require the instruction set architecture (ISA) extension and compiler modification. In this brief, we present <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CRAlert</monospace> , a new hardware-assisted CRA detection scheme, targets at mitigating the root causes of such code reuse attacks. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CRAlert</monospace> is compatible with the current SW/HW environment without requiring any architectural modifications to the processor core. As the proof-of-concept, we prototype the proposed framework on a Xilinx ML605 FPGA. The experimental results demonstrate that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CRAlert</monospace> could detect CRAs with an extremely low performance overhead (geometric mean) of 0.363%.
Deep CNN ensemble for recognition of face images

The paper considers the problem of recognition of face images using an ensemble of deep CNN networks. The solution combines different feature selection methods and three types of classifiers: support vector machine, a random forest of decision trees, and softmax built into the CNN classifier. Deep learning fulfills an important role in the developed system. The numerical descriptors created in the last locally connected convolutional layer of CNN flattened to the form of a vector, are subject to four different selection mechanisms. Their results are delivered to the three classifiers which are the members of the ensemble. The developed system was tested on the problem of face recognition. The dataset was composed of 68 classes of greyscale images. The results of experiments have shown significant improvement of class recognition resulted from the application of the ensemble.
Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach

The abundance of data collected by sensors in Internet of Things devices and the success of deep neural networks in uncovering hidden patterns in time series data have led to mounting privacy concerns. This is because private and sensitive information can be potentially learned from sensor data by applications that have access to this data. In this article, we aim to examine the tradeoff between utility and privacy loss by learning low-dimensional representations that are useful for data obfuscation. We propose deterministic and probabilistic transformations in the latent space of a variational autoencoder to synthesize time series data such that intrusive inferences are prevented while desired inferences can still be made with sufficient accuracy. In the deterministic case, we use a linear transformation to move the representation of input data in the latent space such that the reconstructed data is likely to have the same public attribute but a different private attribute than the original input data. In the probabilistic case, we apply the linear transformation to the latent representation of input data with some probability. We compare our technique with autoencoder-based anonymization techniques and additionally show that it can anonymize data in real time on resource-constrained edge devices.
Radar Signal Modulation Recognition Based on Bispectrum Features and Deep learning

Signal bispectral transformation can not only suppress the influence of Gaussian white noise on signal modulation recognition, but also retain the signal amplitude and phase information. It is also used to extract the non-linear characteristics. Compared with other high-order spectra, bispectrum has a simple processing flow. However, the direct use of all bispectrum as signal features will lead to two-dimensional template matching, causing lots of calculations. Converting two-dimensional bispectrum into one-dimensional sequence, for example, extracting slice information of bispectrum, or using integral bispectrum apparently reduce the amount of data to be processed while retaining part of the bispectrum information. We input the extracted bispectral transformation of radar signals into the neural network to realize modulation recognition. The simulations validate our conclusions that our proposed methods still have a high recognition probability while SNR is low.
Ambient Assisted Living for Elderly Care and Monitoring in COVID-19 Pandemic

In late 2019, a novel Coronavirus broke out from China, which has dispersed all over the globe and has taken away countless lives. Despite the fact that every person is at risk of getting infected with the virus, older people are more likely to fall victim to the virus due to their declining immune systems. Although there has been significant development of vaccines, it is seen that the mutation of the COVID-19 has made it tough to control with the medication available. Due to an uncountable number of Coronavirus strains, many countries are now facing several waves of the pandemic. Assisted living technologies are evolving with time to give people a better life. This technology can be used for older people in Coronavirus pandemic situations as most of the older people have physical and cognitive impairments. In this paper, we have proposed an Internet of Things(IoT)-architectured system incorporated with Artificial intelligence and deep learning that can help diagnose COVID-19 in older people. The proposed architecture will collect all the data from different medical IoT sensors and relay them to the cloud, where the system will process and help us monitor the health of older people. This information could be seen from a dedicated dashboard where the user would be able to get diagnosis status of COVID-19 by our system. In order to be prepared for any future pandemic, this type of system will be beneficial.
Adaptive intelligent agent for e-learning: First report on enabling technology solutions

Because of the global COVID-19 pandemic, online learning has become the dominant teaching method. Moreover, a wide range of e-learning pedagogies are rapidly gaining importance, and in some cases emerging as the preferred approach in education over the traditional methods and techniques of classroom teaching. However much has to be done to efficiently assess student engagement and the learning curve. In this regard, we have proposed construction of an intelligent agent for personalized and adaptive assessment of learning performance based on methods for automated estimation of attention and emotion. We report on the first progress towards the development of the intelligent agent. Three classifiers were used in parallel to detect information about the progress of student engagement. Object detection in video is accomplished with YOLOv3, emotion detection from facial expressions using PAZ software library, and detection of head, arms, and upper-body orientation and position with OpenPose system. NimStim facial expression database, WIDER Attribute Dataset, and UPNA Head Pose Database were used for experimental validation of the individual classifiers. Our system attained the highest precision and recall of 79.13% and 94.15%, respectively, and the highest success rate of 59.56% in recognition of 6 discrete emotions from facial expressions.
Combinatorial Boosting of Classifiers for Moving Target Defense Against Adversarial Evasion Attacks

Adversarial evasion attacks challenge the integrity of machine learning models by creating out-of-distribution samples that are consistently misclassified by these models. While a variety of detection and mitigation approaches have been proposed, they are typically defeated by designing even more sophisticated attacks. One of the most promising group of such approaches is based on creating multiple diversified machine learning models and leveraging their ensemble properties for detection and mitigation of adversarial attacks in a dynamic "moving target'' fashion. However, an efficient implementation of such approaches imposes heavy computation cost for designing and enforcing diversity of multiple classifiers and then training a significant number of them. This paper proposes a scalable modification of dynamic ensemble approach that provides (i) a combinatorial boosting of the number of diversified classifiers which provides an exponentially expanded scope of reliable decisions for dynamic "moving target'' defense, and (ii) robust methods for fusion of ensemble decisions of the resulting classifiers and their combinations towards enhanced confidence in classification decisions in both benign and adversarial scenarios. Two versions of the approach were implemented and tested for machine learning models operating in two different modalities (network intrusion detection and color image classification). Both show significant increase of resiliency against adversarial evasion attacks with moderate to low impact on benign performance of defended machine learning model. For network modality, different versions of approach improved the benign accuracy from 98% to 100% while raising the adversarial accuracy from 0% to 90%-95%; for image modality, benign accuracy remained at the same level of 90% while the adversarial accuracy improved from 0% to about 85%.