Reviewerly/exHarmony huggingface dataset:
Intra-unikernel isolation with Intel memory protection keys

Unikernels are minimal, single-purpose virtual machines. This new operating system model promises numerous benefits within many application domains in terms of lightweightness, performance, and security. Although the isolation between unikernels is generally recognized as strong, there is no isolation within a unikernel itself. This is due to the use of a single, unprotected address space, a basic principle of unikernels that provide their lightweightness and performance benefits. In this paper, we propose a new design that brings memory isolation inside a unikernel instance while keeping a single address space. We leverage Intel's Memory Protection Key to do so without impacting the lightweightness and performance benefits of unikernels. We implement our isolation scheme within an existing unikernel written in Rust and use it to provide isolation between trusted and untrusted components: we isolate (1) safe kernel code from unsafe kernel code and (2) kernel code from user code. Evaluation shows that our system provides such isolation with very low performance overhead. Notably, the unikernel with our isolation exhibits only 0.6% slowdown on a set of macro-benchmarks.
Design and Evaluation of Conceptual Ontologies for Electrochemistry as a Domain

Knowledge Representation and Reasoning is the most important objective of the Semantic Web. The Web of Data visualizes its contents as prospective, structured, organized constituent Domains. Although several real-life domains have been represented and modeled, Chemical Informatics have been neglected owing to minimalistic contributions in the Domain. However, Chemical Informatics can pave a way for improving education ontologies and course modeling to facilitate several real-time applications. In this paper, Electrochemistry is visualized and modeled as a Domain where static classifications and dynamic equations have been modeled. This work focuses on representing structured and light weight ontologies in electrochemistry. Finally, the domain ontologies have been qualitatively and quantitatively evaluated with the best in class reuse ratio.
Radar Signal Modulation Recognition Based on Deep Joint Learning

The development of integrated avionics systems and electromagnetic spectrum technology has attracted widespread attention.It has further increased the performance requirements for modulation signal recognition technology in complex electromagnetic environments.Therefore, this paper proposes a deep joint learning technique, including deep representation and low-dimensionality discrimination, to enhance feature stability and environmental adaptability.Specifically, we design a feature learning network based on AlexNet to extract in-depth features and optimize it through parameter-based transfer learning techniques, promote multi-level representation capabilities of features and reduce the sample size requirements.Moreover, we propose a classification algorithm based on kernel collaborative representation and discriminative projection to enhance the ability of low-dimensionality representation and between-class discrimination, which optimized using the mini-batch random gradient descent method.As shown in the simulation, the overall average recognition success rate of this method aiming at twelve radar signal modulation types reaches 97.58% at SNR of -6dB.The results of simulation and analysis demonstrate the superiority of the proposed model in terms of robustness, timeliness, and adaptability to small samples.
Dive Into Deep Learning

Perhaps while chopping vegetables or engaged in another activity, you too have exploited the convenience of a cloud-based smart speaker to perform your task while getting a hands-free answer to a question you’d otherwise put off until later (or simply forget). Using natural language processing (NLP) technology, voice-activated virtual assistants such as Amazon’s Alexa can execute commands such as retrieving information from a search engine.
Fast Adaptive Gradient RBF Networks For Online Learning of Nonstationary Time Series

For a learning model to be effective in online modeling of nonstationary data, it must not only be equipped with high adaptability to track the changing data dynamics but also maintain low complexity to meet online computational restrictions.Based on these two important principles, in this paper, we propose a fast adaptive gradient radial basis function (GRBF) network for nonlinear and nonstationary time series prediction.Specifically, an initial compact GRBF model is constructed on the training data using the orthogonal least squares algorithm, which is capable of modeling variations of local mean and trend in the signal well.During the online operation, when the current model does not perform well, the worst performing GRBF node is replaced by a new node, whose structure is optimized to fit the current data.Owing to the local one-step predictor property of GRBF node, this adaptive node replacement can be done very efficiently.Experiments involving two chaotic time series and two real-world signals are used to demonstrate the superior online prediction performance of the proposed fast adaptive GRBF algorithm over a range of benchmark schemes, in terms of prediction accuracy and real-time computational complexity.
Adversarial Disentanglement Spectrum Variations and Cross-Modality Attention Networks for NIR-VIS Face Recognition

Near-infrared and visual (NIR-VIS) matching task refers to the face recognition between the two images of different modalities, which remains a challenging task in the field of machine vision. The main problems of NIR-VIS Heterogeneous Face Recognition (HFR) tasks include two aspects: large intra-class differences caused by cross-modal data, and insufficient paired training samples. In this paper, an effective Adversarial Disentanglement spectrum variations and Cross-modality Attention Networks (ADCANs) is proposed for VIS-NIR matching task. Three key components are introduced to the ADCANs for reducing the gap of cross-modal images: Advanced Scatter Loss (ASL), Modality-adversarial Feature Learning (MaFL) and Cross-modality Attention Block (CmAB). The proposed ASL loss captures between- and within-class information of the data and embeds them to the network for more effective training, and it focuses on categories with small between-class distance and increases the distance between them. The MaFL consists of an Identity-Discriminative Feature Learning Network (IDFLN) and a Modality-Adversarial Disentanglement Network (MADN), which can enhance the identity-discriminative feature representations as well as disentangling spectrum variations via an adversarial learning. The IDFLN built by an end-to-end CNNs aims at learning identity-discriminative feature. While the MADN built by a discriminator D and a generator G focuses on removing modality-related information. Furthermore, to increase representation power as well as disentangling spectrum variations effectively, a CmAB block is introduced to the network, which sequentially applies spatial and channel attention modules to both the IDFLN and MADN. Since the channel attention module focuses on `what' features to suppress or emphasize, an orthogonality constraint is introduced to the two channel attention modules, which allows MADN and IDFLN to focus on learning modality-related features and identity-related features, respectively. In particular, the ADCANs consists of multiple CmAB blocks to learn discriminative features and disentangle spectrum variations. A large number of experiments on three challenging HFR datasets indicate that the proposed ADCANs is effective for VIS-NIR HFR task.
Quantum interactions with pulses of radiation

This paper presents a general master equation formalism for the interaction between traveling pulses of quantum radiation and localized quantum systems. Traveling fields populate a continuum of free-space radiation modes and the Jaynes-Cummings model, valid for a discrete eigenmode of a cavity, does not apply. We develop a complete input-output theory to describe the driving of quantum systems by arbitrary incident pulses of radiation and the quantum state of the field emitted into any desired outgoing temporal mode. Our theory is applicable to the transformation and interaction of pulses of radiation by their coupling to a wide class of material quantum systems. We discuss the most essential differences between quantum interactions with pulses and with discrete radiative eigenmodes and present examples relevant to quantum information protocols with optical, microwave, and acoustic waves.
Convolutional Neural Networks for Continuous QoE Prediction in Video Streaming Services

In video streaming services, predicting the continuous user's quality of experience (QoE) plays a crucial role in delivering high quality streaming contents to the user.However, the complexity caused by the temporal dependencies in QoE data and the non-linear relationships among QoE influence factors has introduced challenges to continuous QoE prediction.To deal with that, existing studies have utilized the Long Short-Term Memory model (LSTM) to effectively capture such complex dependencies, resulting in excellent QoE prediction accuracy.However, the high computational complexity of LSTM, caused by the sequential processing characteristic in its architecture, raises a serious question about its performance on devices with limited computational power.Meanwhile, Temporal Convolutional Network (TCN), a variation of convolutional neural networks, has recently been proposed for sequence modeling tasks (e.g., speech enhancement), providing a superior prediction performance over baseline methods including LSTM in terms of prediction accuracy and computational complexity.Being inspired of that, in this paper, an improved TCN-based model, namely CNN-QoE, is proposed for continuously predicting the QoE, which poses characteristics of sequential data.The proposed model leverages the advantages of TCN to overcome the computational complexity drawbacks of LSTM-based QoE models, while at the same time introducing the improvements to its architecture to improve QoE prediction accuracy.Based on a comprehensive evaluation, we demonstrate that the proposed CNN-QoE model can provide a high QoE prediction performance on both personal computers and mobile devices, outperforming the existing approaches.
Intra‐active entanglements: What posthuman and new materialist frameworks can offer the learning sciences

Abstract This paper examines what new materialist and posthumanist frameworks can offer learning science research in diverse maker learning environments. We explore what is gained by grappling with the entanglements between humans, non‐humans and more‐than‐humans. To do this, we draw on Karen Barad's ethico‐onto‐epistemology and agential realism where she redefines connections to the shared world by attuning to the entangled matter that is created within intra‐actions. We use this framework across four international cases: digital media camps, a university‐level classroom‐based makerspace, a Saturday outdoor makerspace workshop and a classroom‐based museum makerspace. Each case study attends to how intra‐actions enact agential forces in maker education research—forces that posthuman and new materialist frameworks help us see. In so doing, these case studies challenge many of the assumptions prevalent in the learning sciences about mattering and its implications in research sites.
Breast Cancer Histopathology Image Classification using AlexNet

Deep learning has achieved high performance in many fields like image classification, object detection etc. Recently many researchers have tried to carry out deep learning in medical image analysis. Convolutional Neural Network (CNN) has been set as a profound class of models in this field. It is a deep learning model which extracts the features of an image and then classify it. In this study, an analysis of Breast Cancer (BC) histopathology images is done using famous CNN architecture “AlexNet”. Histopathology images are the gold measure for the breast cancer diagnosis. Using deep learning for predicting breast cancer can prove to be very much effective in near future. Here a dataset, which consists 7909 images of 82 patients is used to train our model and later the image is successfully being classified. Impressive results and analysis are also achieved using this approach.
Image Feature Detectors for Deepfake Video Detection

Detecting DeepFake videos are one of the challenges in digital media forensics. This paper proposes a method to detect deepfake videos using Support Vector Machine (SVM) regression. The SVM classifier can be trained with feature points extracted using one of the different feature-point detectors such as HOG, ORB, BRISK, KAZE, SURF, and FAST algorithms. A comprehensive test of the proposed method is conducted using a dataset of original and fake videos from the literature. Different feature point detectors are tested. The result shows that the proposed method of using feature-detector-descriptors for training the SVM can be effectively used to detect false videos.
Estimating missing data using novel correlation maximization based methods

The accurate estimation of missing data plays a vital role in ensuring a high level of data quality. The missing values should be imputed before performing data mining, machine learning, and other data processing tasks. Ten correlation-based imputation methods are proposed in this paper. All of these methods try to maximize the correlation between a missing feature and other features. The maximization is achieved by selecting segments of data that have strong correlations. The proposed approach involves the following main steps to impute each missing instance. First, a base set is selected from complete instances. Second, data segments with strong correlations are generated using the base set and the rest of the complete instances. Finally, each missing value is imputed by applying linear models to the discovered segments of data. This study considers seven real datasets from different fields with different missing rates. The imputation quality of the proposed methods is compared to those of seven other imputation approaches in terms of three well-known evaluation criteria. The experimental results reveal that the proposed approach has better imputation performance than competing imputation techniques in most cases.
Automatic Hate Speech Detection on Social Media: A Brief Survey

Due to the advancement in technology and the explosion of the information age, people communicate with each other indirectly via using the online social networks (OSNs), such as Facebook Snapchat, Instagram, and Twitter. Users of OSNs can post anything without any control or constraint of the content, which leads to increase in spreading of hateful and offensive speech among users, thus resulting in an increase in crimes, murder, and terrorism. Hence, this paper provides a survey and state of the art natural language processing (NLP) technique that is used in automatic detection of the hate speech on OSNs, such as dictionaries, bag-of-words, N-gram etc.
All-relevant feature selection using multidimensional filters with exhaustive search

This paper describes a method for the identification of informative variables in an information system with discrete decision variables. It is targeted specifically towards the discovery of variables that are non-informative when considered alone, but informative when the synergistic interactions between multiple variables are considered. To this end, mutual entropy of all possible k-tuples of variables with a decision variable is computed. Then, for each variable, the maximum information gain due to interactions with other variables is obtained. For non-informative variables, this quantity conforms to well-known statistical distributions. This allows for discerning the truly informative variables from the non-informative ones. To demonstrate this approach, the method is applied to several synthetic datasets that involve complex multidimensional interactions between variables. The performance of the method is also validated on a real-world dataset. It is shown that the method is capable of identifying the most important informative variables, even when the dimensionality of the analysis is smaller than the true dimensionality of the problem. What is more, the high sensitivity of the algorithm allows for the detection of the influence of nuisance variables on the response variable.
Skeleton-Based Dynamic Hand Gesture Recognition Using a Part-Based GRU-RNN for Gesture-Based Interface

Recent improvements in imaging sensors and computing units have led to the development of a range of image-based human-machine interfaces (HMIs). An important approach in this direction is the use of dynamic hand gestures for a gesture-based interface, and some methods have been developed to provide real-time hand skeleton generation from depth images for dynamic hand gesture recognition. Towards this end, we propose a skeleton-based dynamic hand gesture recognition method that divides geometric features into multiple parts and uses a gated recurrent unit-recurrent neural network (GRU-RNN) for each feature part. Because each divided feature part has fewer dimensions than an entire feature, the number of hidden units required for optimization is reduced. As a result, we achieved similar recognition performance as the latest methods with fewer parameters.
How chatbots can be involved in the education process

Chatbots become more and more popular in the human-machine interactions, because they enable to communicate with the system by the human language, which is a very intuitive and user-friendly. Moreover, chatbots can provide information without time-demanding searching and hide its complexity. Three chatbots were developed for supporting education on the Department of electronics and multimedia telecommunications. First one is the KEMTbot, which is a bot located on the department web page. It provides information from the web and about department staff. The second chatbot is a bot that supports students during the exercises from the subject “Databases”. The last one is a chatbot-like Amazon Alexa skill, which answers questions about the department on Amazon Echo devices.
Data-driven and tool-supported elicitation of quality requirements in agile companies

Quality requirements (QRs) are a key artifact needed to ensure the quality and success of a software system. Despite their importance, QRs rarely get the same degree of attention as their functional counterpart in agile software development (ASD) projects. Moreover, crucial information that can be obtained from software development repositories (e.g., JIRA, GitHub) is not fully exploited, or is even neglected, in QR elicitation activities. In this work, we present a data-driven tooled approach for the semi-automatic generation and documentation of QRs in the context of ASD. The approach is based on the declaration of thresholds over quality-related issues, whose violation triggers user-defined alerts. These alerts are used to browse a catalog of QR patterns that are presented to the ASD team by means of a dashboard that implements several analysis techniques. Once selected, the patterns generate the QRs, which are documented and stored in the product backlog. The full approach is implemented via a configurable platform. Over the course of 1 year, four companies differing in size and profile followed this approach and deployed the platform in their premises to semi-automatically generate QRs in several projects. We used standardized measurement instruments to elicit the perception of 22 practitioners regarding their use of the tool. The quantitative and qualitative analyses yielded positive results; i.e., the practitioners' perception with regard to the tool's understandability, reliability, usefulness, and relevance was positive. We conclude that the results show potential for future adoption of data-driven elicitation of QRs in agile companies and encourage other practitioners to use the presented tool and adopt it in their companies.
Enhancing VVC Through Cnn-Based Post-Processing

This paper presents a new Convolutional Neural Network (CNN) based post-processing approach for video compression, which is applied at the decoder to improve the reconstruction quality. This method has been integrated with the Versatile Video Coding Test Model (VTM) 4.0.1, and evaluated using the Random Access (RA) configuration using the Joint Video Exploration Team (JVET) Common Test Conditions (CTC). The results show coding gains on all tested sequences at various spatial resolutions over different quantisation parameter ranges, with average bit rate savings (based on Bjøntegaard Delta measurements) of 3.90% and 4.13%, when PSNR and VMAF are used as quality metrics respectively. The computational complexities of different CNN architecture variants have also been investigated.
Multi-Resolution Intrinsic Texture Geometry-Based Local Binary Pattern for Texture Classification

In this paper, we propose a new hybrid Local Binary Pattern (LBP) based on Hessian matrix and Attractive Center-Symmetric LBP (ACS-LBP), called Hess-ACS-LBP.The Hessian matrix provides the directional derivative information of different texture regions, while ACS-LBP reveals the local texture features efficiently.To obtain the macro-and micro-structure textural changes, Hessian matrix is calculated in a multiscale schema.Multiscale Hessian matrix presents the intrinsic local geometry of the texture changes.The magnitude information of the Hessian matrix is used in the ACS-LBP method.A cross-scale joint coding strategy is used to construct Hess-ACS-LBP descriptor.Finally, histogram concatenation is carried out.Extensive experiments on eight texture databases of CUReT, USPTex, KTH-TIPS2b, MondialMarmi, OuTeX TC_00013, XU HR, ALOT and STex validate the efficiency of the proposed method.The proposed Hess-ACS-LBP method achieves about 20% improvement over the original LBP method and 1%-11% improvement over the other state-of-the-art hand-crafted LBP methods in terms of classification accuracy.Besides, the experimental results show that the proposed method achieves up to 32% better results than the state-of-the-art deep learning based methods.Especially, the performance of the proposed method on ALOT and STex datasets containing many classes is remarkable.
Vehicle Counting System using Deep Learning and Multi-Object Tracking Methods

Using deep learning technology and multi-object tracking method to count vehicles accurately in different traffic conditions is a hot research topic in the field of intelligent transportation. In this paper, first, a vehicle dataset from the perspective of highway surveillance cameras is constructed, and the vehicle detection model is obtained by training using the You Only Look Once (YOLO) version 3 network. Second, an improved multi-scale and multi-feature tracking algorithm based on a kernel correlation filter (KCF) algorithm is proposed to avoid the KCF extracting single features and single-scale defects. Combining the intersection over union (IoU) similarity measure and the row-column optimal association criterion proposed in this paper, matching strategy is used to process the vehicles that are not detected and wrongly detected, thereby obtaining complete vehicle trajectories. Finally, according to the trajectory of the vehicle, the traveling direction of the vehicle is automatically determined, and the setting position of the detecting line is automatically updated to obtain the vehicle count result accurately. Experiments were conducted in a variety of traffic scenes and compared with published data. The experimental results show that the proposed method achieves high accuracy of vehicle detection while maintaining accuracy and precision in tracking multiple objects, and obtains accurate vehicle counting results which can meet real-time processing requirements. The algorithm presented in this paper has practical application for vehicle counting in complex highway scenes.