Reviewerly/exHarmony huggingface dataset:
Extreme minimal learning machine: Ridge regression with distance-based basis

The extreme learning machine (ELM) and the minimal learning machine (MLM) are nonlinear and scalable machine learning techniques with a randomly generated basis. Both techniques start with a step in which a matrix of weights for the linear combination of the basis is recovered. In the MLM, the feature mapping in this step corresponds to distance calculations between the training data and a set of reference points, whereas in the ELM, a transformation using a radial or sigmoidal activation function is commonly used. Computation of the model output, for prediction or classification purposes, is straightforward with the ELM after the first step. In the original MLM, one needs to solve an additional multilateration problem for the estimation of the distance-regression based output. A natural combination of these two techniques is proposed and experimented here: to use the distance-based basis characteristic in the MLM in the learning framework of the regularized ELM. In other words, we conduct ridge regression using a distance-based basis. The experimental results characterize the basic features of the proposed technique and surprisingly, indicate that overlearning with the distance-based basis is in practice avoided in classification problems. This makes the model selection for the proposed method trivial, at the expense of computational costs.
Context-Sensitive Malicious Spelling Error Correction

Misspelled words of the malicious kind work by changing specific keywords and are intended to thwart existing automated applications for cyber-environment control such as harassing content detection on the Internet and email spam detection. In this paper, we focus on malicious spelling correction, which requires an approach that relies on the context and the surface forms of targeted keywords. In the context of two applications-profanity detection and email spam detection-we show that malicious misspellings seriously degrade their performance. We then propose a context-sensitive approach for malicious spelling correction using word embeddings and demonstrate its superior performance compared to state-of-the-art spell checkers.
On the Spectre and Meltdown Processor Security Vulnerabilities

This paper first reviews the Spectre and Meltdown processor security vulnerabilities that were revealed during January–October 2018 and that allow the extraction of protected information from billions of processors in large and small systems. It then discusses short-term mitigation actions and speculates on the longer term implications to computer software and hardware. This paper expands from a keynote/panel by the authors at IEEE Hot Chips 2018.
A smartphone‐based augmented reality system for university students for learning digital electronics

Abstract The fundamental concepts of electronic devices are generally taught in the initial courses of electrical and electronics engineering. For students, it is very important to grasp the basic concepts that make computers and digital systems possible. Among these concepts, boolean algebra and logic gates stand out. In this paper, a smartphone‐based augmented reality (AR) system is proposed which helps in the adequate learning of logic gate integrated circuits (ICs). The proposed system allows the automatic identification of ICs that enables basic logic operations. It uses a smartphone and a breadboard for the identification of 7 basic logic gate ICs that are: AND gate, OR gate, NOT gate, NAND gate, NOR gate, XOR gate, and XNOR gate. The smartphone takes a picture of the electronic circuit and digitally generates a layer of virtual objects which are then mixed with the original image. The markless paradigm identifies the ICs in a typical circuit and places three objects that are: IC identification, pins information, and logic diagram information. Finally, the smartphone screen displays the image of the logic gate obtained. The entire evaluation of the AR system is presented with a technical efficiency of 97.5 and a qualitative satisfaction study.
Analysis of Movement Quality in Full-Body Physical Activities

Full-body human movement is characterized by fine-grain expressive qualities that humans are easily capable of exhibiting and recognizing in others’ movement. In sports (e.g., martial arts) and performing arts (e.g., dance), the same sequence of movements can be performed in a wide range of ways characterized by different qualities, often in terms of subtle (spatial and temporal) perturbations of the movement. Even a non-expert observer can distinguish between a top-level and average performance by a dancer or martial artist. The difference is not in the performed movements--the same in both cases--but in the “quality” of their performance. In this article, we present a computational framework aimed at an automated approximate measure of movement quality in full-body physical activities. Starting from motion capture data, the framework computes low-level (e.g., a limb velocity) and high-level (e.g., synchronization between different limbs) movement features. Then, this vector of features is integrated to compute a value aimed at providing a quantitative assessment of movement quality approximating the evaluation that an external expert observer would give of the same sequence of movements. Next, a system representing a concrete implementation of the framework is proposed. Karate is adopted as a testbed. We selected two different katas (i.e., detailed choreographies of movements in karate) characterized by different overall attitudes and expressions (aggressiveness, meditation), and we asked seven athletes, having various levels of experience and age, to perform them. Motion capture data were collected from the performances and were analyzed with the system. The results of the automated analysis were compared with the scores given by 14 karate experts who rated the same performances. Results show that the movement-quality scores computed by the system and the ratings given by the human observers are highly correlated (Pearson’s correlations r = 0.84, p = 0.001 and r = 0.75, p = 0.005).
Model-based documentation of dynamicity constraints for collaborative cyber-physical system architectures: Findings from an industrial case study

Collaborative cyber-physical systems form highly dynamic networks, consisting of collaborating individual systems at run-time. These networks provide functionality that goes beyond the functionality of the individual systems that are part of the network. The availability of such collaborative functionalities depends not only on the individual systems’ behaviors but also on the current morphology of the network (i.e., the architecture of the overall network that defines what kind of systems and how many systems are actually partaking in the collaboration at that moment). It is of importance to thoroughly consider the possible morphologies of the network of cyber-physical systems during the development of the individual systems as these are sources for requirements as well as design decisions. These requirements and design decisions must be associated to the particular morphologies they are applicable to in order to ensure traceability and comprehensibility between the morphology of the network of cyber-physical systems and the individual systems. Hence, we aim at defining an orthogonal model which allows to document dynamicity constraints and therefore restrain the potential morphologies a network can take. This paper contributes results from an exploratory case study conducted in the industry automation domain. In the case study, an existing variability modeling approach was applied to the development of a network of autonomous transport robots. Based on the variability modeling approach an orthogonal dynamicity constrained model was defined and evolved to fit industry's needs. Results show that the use of an orthogonal model for dynamicity constraints can improve the development of cyber-physical systems designed to participate in collaborative networks.
Reduced-reference quality assessment of image super-resolution by energy change and texture variation

In this paper, we propose a novel reduced-reference quality assessment metric for image super-resolution (RRIQA-SR) based on the low-resolution (LR) image information. With the pixel correspondence, we predict the perceptual similarity between image patches of LR and SR images by two components: the energy change in low-frequency regions, which can be used to capture the global distortion in SR images, and texture variation in high-frequency regions, which can be used to capture the local distortion in SR images. The overall quality of SR images is estimated by perceptual similarity calculated by energy change and texture variation between local image patches of LR and HR images. Experimental results demonstrate that the proposed method can obtain better performance of quality prediction for SR images than other existing ones, even including some full-reference (FR) metrics.
Multi-view transfer learning with privileged learning framework

In this paper, we present a multi-view transfer learning model named Multi-view Transfer Discriminative Model (MTDM) for both image and text classification tasks. Transfer learning, which aims to learn a robust classifier for the target domain using data from a different distribution, has been proved to be effective in many real-world applications. However, most of the existing transfer learning methods map across domain data into a high-dimension space which the distance between domains is closed. This strategy always fails in the multi-view scenario. On the contrary, the multi-view learning methods are also difficult to extend in the transfer learning settings. One of our goals in this paper is to develop a model which can perform better in both multi-view and transfer learning settings. On the one hand, the problem of multi-view is implemented by the paradigm of learning using privileged information (LUPI), which could guarantee the principle of complementary and consensus. On the other hand, the model adequately utilizes the source domain data to build a robust classifier for the target domain. We evaluate our model on both image and text classification tasks and show the effectiveness compared with other baseline approaches.
Robust multi-target tracking under mismatches in both dynamic and measurement models

In multi-target tracking (MTT), it is hard to guarantee that the dynamic and measurement model mismatches are described by process and measurement noises with suitable covariance matrices (CMs). Meanwhile, model changes caused by target maneuvers and disturbance-corrupted measurements may coexist in a complex environment. Therefore, we propose the problem of MTT under mismatches in both dynamic and measurement models, where the uncertain state prediction error and measurement noise CMs are considered as random variables. Then, a robust generalized labeled multi-Bernoulli (RGLMB) filter framework is derived to recursively propagate the joint posterior density of the multi-target state and the random CMs. By modeling the random CMs as inverse Wishart distributions, on-line random CMs tuning and multi-target state estimation are unified via minimizing the Kullback–Leibler divergence. Simulation results show that the proposed RGLMB filter is robust to different target motion models and noisy radar measurements corrupted by step, sinusoidal and stochastic forms of bias.
Gender identification of egyptian dialect in twitter

Despite the widespread of social media among all age groups in Arabic countries, the research directed towards Author Profiling (AP) is still in its early stages. This paper provides an Egyptian Dialect Gender Annotated Dataset (EDGAD) obtained from Twitter as well as a proposed text classification solution for the Gender Identification (GI) problem. The dataset consists of 70,000 tweets per gender. In text classification, a Mixed Feature Vector (MFV) with different stylometric and Egyptian Arabic Dialect (EAD) language-specific features is proposed, in addition to N-Gram Feature Vector (NFV). Ensemble weighted average is applied to the Random Forest (RF) with MFV and Logistic Regression (LR) with NFV. The achieved gender identification accuracy is 87.6%.
Learning Parametric Functions for Color Image Enhancement

In this work we propose a novel CNN-based method for image enhancement that simulates an expert retoucher. The method is fast and accurate at the same time thanks to the decoupling between the inference of the parameters and the color transformation. Specifically, the parameters are inferred from a downsampled version of the raw input image and the transformation is applied to the full resolution input. Different variants of the proposed enhancement method can be generated by varying the parametric functions used as color transformations (i.e. polynomial, piecewise, cosine and radial), and by varying how they are applied (i.e. channelwise or full color). Experimental results show that several variants of the proposed method outperform the state of the art on the MIT-Adobe FiveK dataset.
The Enthusiast, the Interested, the Sceptic, and the Cynic

We discuss the user study of a mobile cultural heritage game designed to stimulate reflection about a city's history. Aided by location-aware technology, the game fosters the serendipitous discovery of points of interest, historical images and stories, whilst players wander the city. This exploration differs from the typical precalculated path recommendations used by other location-based applications. It triggers reflection about the city's past that is as unique as its visitors. Ours is one of the first studies to attempt an understanding of the effects of serendipitous urban discovery and historic reflection-triggering technologies on user experience. We combined field trials with controlled experiments, analysing perceptions of the experience and value using responses expressed in open-ended questionnaire items. Using thematic coding and sentiment analysis, we observed types of emotional responses, indicating four potential profiles of their likelihood towards future technology adoption. Enthusiastic and Interested users appreciated the freedom of movement choice that created an autonomous experience that fostered a sense of personal accomplishment. The interface interactions of the game, designed to stimulate reflection, supported a feeling of connectedness to others. In contrast, Cynical and Sceptical users were less tolerant of perceived technological issues, requiring more perfection in functionality and design. These users are less likely to be the early adopters of serendipitous location-based apps. The game was developed as part of a large cultural informatics project but, unlike typical evaluations, we conducted this study midway through the project and not at its end. This approach (1) gave the team the possibility to take stock, pause, and reflect and (2) provided insight on future design improvements for increasing the perceived value of serendipitous urban discovery applications. Our results contribute towards a grounded understanding of user experience and help progress the development of cultural heritage applications that incorporate elements of reflection and/or place-based exploration into their functionalities.
Research on de-motion blur image processing based on deep learning

In recent years, with the rapid development of computer technology and network technology, computer vision has been widely used in various scientific fields. Human motion recognition, as an important branch of computer vision, is essentially to classify human motion information in motion images correctly. It has great significance in intelligent monitoring and security, human-computer interaction, motion analysis and other fields. At present, there are still some problems in human motion recognition methods. Firstly, how to extract and characterize the motion information in images has been one of the difficulties in this field; secondly, with the appearance of kinect and other depth cameras, researchers have provided the depth information of human motion images, and how to effectively use these depth information to achieve human motion recognition and classification is also an important research issue; finally, when the amount of sample data is small, how to use the deep learning network model to achieve a higher human motion recognition rate? Based on UTD-MHAD database, this paper studies the human motion recognition of RGB image and depth image captured simultaneously by kinect, and carries out relevant discussion and analysis on the above problems, using micro-inertial sensors (MTi-G-700 developed by Xsens and Android mobile phones, tablets and other personal mobile devices come with MEMS gyroscopes and accelerometers) to correct the image to motion blur, build a new mathematical model, use the inertial data obtained by MIMU in a short time to estimate the position, attitude and speed of camera motion, correct the image pixel position, perform image de-motion blur processing, and then perform image processing such as denoising to solve the image motion blur problem. A new algorithm is developed and its science is verified by MATLAB simulation.
Interpretation of Deep CNN Based on Learning Feature Reconstruction With Feedback Weights

Interpreting a deep Convolutional Neural Network (CNN) involves identifying the features in a hierarchy of layers that contribute to recognition. Although the current approaches serve as methods to interpret a deep CNN, further advancement is required for a more accurate and efficient way of understanding how a hierarchy of features formed by a deep CNN contributes to recognition. In this paper, we propose attaching a feedback CNN to a pretrained feedforward CNN as a means of learning how recognition is performed by the feedforward CNN. In other words, the features reconstructed in a hierarchy of the feedback CNN represent those learned by the feedforward CNN. By analyzing how clusters are formed in the layers of feature spaces in the feedback CNN, we can interpret which features critically contribute to recognition. It also helps to evaluate whether or not recognition is done successfully. In order to show this, we experimentally verify the capabilities of the proposed approach in terms of 1) accurately recovering the ground truth input under data corruption; 2) generating novel input data corresponding to an untrained feature vector without input data iterations; and 3) identifying incorrectly recognized input data by pinpointing the source of the error in feature spaces. The experiments conducted on the ModelNet datasets indicate that the proposed approach offers an extended capability of interpreting a deep CNN as described above with higher accuracy than the conventional approaches.
Catch Me if You Can: A Closer Look at Malicious Co-Residency on the Cloud

VM migration is an effective countermeasure against attempts at malicious co-residency. In this paper, our overarching objectives are: (a) to get an in-depth understanding of the ways and effectiveness with which an attacker can launch attacks toward achieving co-residency and (b) to design migration policies that are very effective in thwarting malicious co-residency, but are thrifty in terms of the bandwidth and downtime costs that are incurred with live migration. Toward achieving our goals, we first undertake an experimental study on Amazon EC2 to obtain an in-depth understanding of the side-channels, through which an attacker can use to ascertain co-residency with a victim. Here, in this paper, we identify a new set of stealthy side-channel attacks which we show to be more effective than the currently available attacks toward verifying co-residency. We also build a simple model that can be used for estimating co-residency times based on very few measurements on a given cloud platform, to account for varying attacker capabilities. Based on the study, we develop a set of guidelines to determine under what conditions the victim VM migrations should be triggered, given the performance costs in terms of bandwidth and downtime, which a user is willing to bear. Through extensive experiments on our private in-house cloud, we show that the migrations, using our guidelines, can limit the fraction of the time that an attacker VM co-resides with a victim VM to about 1% of the time with the bandwidth costs of a few MB and downtimes of a few seconds per day per VM migrated.
Transitions: A Protocol-Independent View of the Future Internet

Countless novel approaches to communication protocols, overlay networks, and distributed middleware are published every year, yet the adoption of such novel findings in the global Internet landscape progresses at a slow pace. Many of such new communication mechanisms excel (only) under specific deployment conditions, while user mobility and application usage patterns lead to dynamic operation conditions. This mismatch is one reason that makes a wide deployment of new specialized mechanisms particularly hard as observed, for example, for multipath transport protocol extensions until the emergence of multipath transmission control protocol (TCP). This paper formalizes the concept of Transitions, i.e., a method to instrumentalize adaptivity at runtime in communication systems. It allows to exchange communication mechanisms in a running system to optimize the communication quality. In the following, we describe the building blocks required to: 1) capture the features and relations within a communication system and 2) express and optimize the decision making process in such a system. We show how this concept maps intuitively to the Internet model which makes a protocol-independent deployment of applications feasible in the future Internet.
LA-LMRBF: Online and Long-Term Web Service QoS Forecasting

We propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> ong-term Quality of Service (QoS) forecasting approach using <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> dvertisement and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> evenberg- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> arquardt improved <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> adial <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u> asis <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> unction (LA-LMRBF)—a novel online QoS forecasting approach. LA-LMRBF aims to accurately predict QoS attributes of Web services in the form of multivariate time series via three stages. First, the phase space reconstruction theory is employed to restore multi-dimensional and nonlinear relations among the multivariate QoS attributes. Second, short-term QoS advertisement data is incorporated to enable long-term QoS forecasting. Finally, an optimized Radial Basis Function (RBF) neural network is constructed to forecast long-term multivariate QoS values, where the Affinity Propagation clustering algorithm is used to determine the number of hidden nodes and the Levenberg-Marquardt (LM) algorithm is utilized to dynamically update some parameters of the RBF neural network. A series of experiments are performed on a mixture of public and self-collected data sets. The results show that LA-LMRBF is superior to the other approaches and more suitable for long-term QoS forecasting.