Reviewerly/exHarmony huggingface dataset:
Randomized neural network based signature for dynamic texture classification

Dynamic texture analysis has been the focus of intensive research in recent years. Thus, this paper presents an innovative and highly discriminative dynamic texture analysis method, whose signature is composed of the weights of the output layer of a randomized neural network after a training procedure. This training is performed by using the pixels of slices of each orthogonal plane of the video (XY, YT, and XT) as input feature vectors and corresponding output labels. The obtained video signature provided an accuracy of 97.05%, 98.54%, 97.74% and 96.51% on the UCLA-50 classes, UCLA-9 classes, UCLA-8 classes and Dyntex++, respectively. These results, when compared to other dynamic texture analysis methods, demonstrate that our descriptors are very effective and that our proposed approach can contribute significantly to the field of dynamic texture analysis.
Power-efficient and shift-robust eye-tracking sensor for portable VR headsets

Photosensor oculography (PSOG) is a promising solution for reducing the computational requirements of eye tracking sensors in wireless virtual and augmented reality platforms. This paper proposes a novel machine learning-based solution for addressing the known performance degradation of PSOG devices in the presence of sensor shifts. Namely, we introduce a convolutional neural network model capable of providing shift-robust end-to-end gaze estimates from the PSOG array output. Moreover, we propose a transfer-learning strategy for reducing model training time. Using a simulated workflow with improved realism, we show that the proposed convolutional model offers improved accuracy over a previously considered multilayer perceptron approach. In addition, we demonstrate that the transfer of initialization weights from pre-trained models can substantially reduce training time for new users. In the end, we provide the discussion regarding the design trade-offs between accuracy, training time, and power consumption among the considered models.
Getting Started Creating Data Dictionaries: How to Create a Shareable Data Set

As researchers embrace open and transparent data sharing, they will need to provide information about their data that effectively helps others understand their data sets’ contents. Without proper documentation, data stored in online repositories such as OSF will often be rendered unfindable and unreadable by other researchers and indexing search engines. Data dictionaries and codebooks provide a wealth of information about variables, data collection, and other important facets of a data set. This information, called metadata, provides key insights into how the data might be further used in research and facilitates search-engine indexing to reach a broader audience of interested parties. This Tutorial first explains terminology and standards relevant to data dictionaries and codebooks. Accompanying information on OSF presents a guided workflow of the entire process from source data (e.g., survey answers on Qualtrics) to an openly shared data set accompanied by a data dictionary or codebook that follows an agreed-upon standard. Finally, we discuss freely available Web applications to assist this process of ensuring that psychology data are findable, accessible, interoperable, and reusable.
A Unifying Analysis of Chaos Synchronization and Consistency in Delay-Coupled Semiconductor Lasers

Consistency is a powerful concept to characterize and classify the behavior of driven dynamical systems, therefore playing an essential role for the understanding of information processing in and by complex photonic systems. Here, we discuss two different experimental configurations of semiconductor laser systems with delayed optical feedback allowing for a unifying view via the detailed characterization and comparison of consistency and synchronization properties. The first configuration comprises two unidirectionally coupled semiconductor lasers, which has found considerable interest for applications in encrypted communication. The second configuration consists of a single laser with two optical delay loops of significantly different lengths, designed such that only one loop is active at a time and that allows to study replays of injected optical signals. We show that, in terms of three characteristic correlation measures-consistency correlation, transformation correlation, and spurious correlation-the dynamical features of the two different experiments are very similar and how the two configurations are connected. Our approach shows that consistency allows to understand and relate various chaos synchronization and stability properties. We particularly discuss the experimental results in the context of a previously established analytical framework, providing a common dynamical interpretation of consistency and chaos synchronization in delay-driven laser systems.
Analyzing Cybersecurity Job Market Needs in Morocco by Mining Job Ads

Cybersecurity jobs are becoming more and more in demand for companies and governmental organizations, especially with the increasing number of cyber-threats in the world. In order to keep up with these changes, a study for the cybersecurity job market needs is required in order to help universities adapt their curricula and train more skilled graduates. However, most of prior work focuses on IT jobs and overlooks the cybersecurity field which makes the analysis of the needs of this field ambiguous. In this paper, we collect job ads from 9 Moroccan job portals from February 2017 to June 2018. After removing duplicates, we result in 409 job ads. Then, we split the data into two categories that are cybersecurity jobs and IT jobs that require cybersecurity. We use text analysis techniques to extract skills and requirements listed in these ads. More specifically, we extract the education level, programming languages, natural languages, years of experience, certifications and types of contract listed in these ads. Our paper finds that skills that are highly requested, such as risk analysis methods and certificates are not emphasized in cybersecurity-specific curriculum, while skills that are less in demand, such as a range of programming languages, are taught more regularly in universities. Additionally, more jobs require some understanding of English than of French, which was surprising given prevalence of French in Morocco. Our results can be used to reshape universities training in order to meet the demand of the job market.
Enhancing physical objects with actuated levitating particles

We describe a novel display concept where levitating particles are used to add a dynamic display element to static physical objects. The particles are actuated using ultrasound, for expressive output without mechanical constraints. We explore novel ways of using particles to add dynamic output to other objects, for new interactive experiences. We also discuss the practical challenges of combining these. This work shows how the unique capabilities of levitation can create novel displays by enhancing another form of media.
Block what? The unrealized potential of blockchain in healthcare

Mari Greenberger is the senior director of informatics, technology, and innovations at the Healthcare Information and Management Systems Society in Chicago, Ill. The author has disclosed no financial relationships related to this article.
Leveraging Kernel-Incorporated Matrix Factorization for App Recommendation

The ever-increasing number of smartphone applications (apps) available on different app markets poses a challenge for personalized app recommendation. Conventional collaborative filtering-based recommendation methods suffer from sparse and binary user-app implicit feedback, which results in poor performance in discriminating user-app preferences. In this article, we first propose two kernel incorporated probabilistic matrix factorization models, which introduce app-categorical information to constrain the user and app latent features to be similar to their neighbors in the latent space. The two models are solved by Stochastic Gradient Descent with a user-oriented negative sampling scheme. To further improve the recommendation performance, we construct pseudo user-app ratings based on user-app usage information, and propose a novel kernelized non-negative matrix factorization by incorporating non-negative constraints on latent factors to predict user-app preferences. This model also leverages user--user and app--app similarities with regard to app-categorical information to mine the latent geometric structure in the pseudo-rating space. Adopting the Karush--Kuhn--Tucker conditions, a Multiplicative Updating Rules based optimization is proposed for model learning, and the convergence is proved by introducing an auxiliary function. The experimental results on a real user-app installation usage dataset show the comparable performance of our models with the state-of-the-art baselines in terms of two ranking-oriented evaluation metrics.
Practical Causal Models for Cyber-Physical Systems

Unlike faults in classical systems, faults in Cyber-Physical Systems will often be caused by the system's interaction with its physical environment and social context, rendering these faults harder to diagnose. To complicate matters further, knowledge about the behavior and failure modes of a system are often collected in different models. We show how three of those models, namely attack trees, fault trees, and timed failure propagation graphs can be converted into Halpern-Pearl causal models, combined into a single holistic causal model, and analyzed with actual causality reasoning to detect and explain unwanted events. Halpern-Pearl models have several advantages over their source models, particularly that they allow for modeling preemption, consider the non-occurrence of events, and can incorporate additional domain knowledge. Furthermore, such holistic models allow for analysis across model boundaries, enabling detection and explanation of events that are beyond a single model. Our contribution here delineates a semi-automatic process to (1) convert different models into Halpern-Pearl causal models, (2) combine these models into a single holistic model, and (3) reason about system failures. We illustrate our approach with the help of an Unmanned Aerial Vehicle case study.
Single-channel optical color image cryptosystem using two-step phase-shifting interferometry and random modulus decomposition

We propose an optical asymmetric single-channel color image cryptosystem based on two-step phase-shifting and random modulus decomposition (RMD) method. In the proposed cryptosystem, a color image is represented by the indexed format including a data matrix and a colormap. The data matrix is regarded as the input of double random phase encoding (DRPE)-based structure while the colormap in which the number of the colors is used as a private key. A designed operator in which phase modulators (PMs) utilized as private keys are generated is introduced to the DRPE-based structure to remove its linearity. As the output of the DRPE-based structure, the complex-valued image is retrieved from interferograms recorded using two-step phase-shifting technique. In addition, to further improve the security of the cryptosystem, the RMD used as a post-processing operation is performed digitally on the retrieved complexed-valued image and then an additional private key is generated. In the decryption process, three private keys generated in the encryption process are utilized to retrieve the original color image using a modified 4-f cryptosystem. In our proposed algorithm, decryption keys are different from encryption keys and the decryption process is also different from the encryption process. Compared to the DRPE-based cryptosystem, the proposed cryptosystem is immune to various attacks. Numerical simulation is carried out to demonstrate the feasibility and effectiveness of the proposed cryptosystem.
Quantumness of ensembles via coherence

The notion of quantum ensembles is necessary for a description of quantum systems with incomplete information, which often occurs in the theory of quantum measurement and communication. Since ensembles involving non-commuting states possess intrinsic quantumness and play a central role in various quantum information processing tasks, it is desirable to quantify the quantum characteristics in ensembles from different points of view. In this paper, we define a quantifier for quantumness of quantum ensembles from the perspective of coherence, and prove this quantifier satisfies almost all natural desiderata for a measure of quantumness recently suggested by Li et al. (2017) [12]. Furthermore, this measure also possesses monotonicity under commutativity preserving operations, which intrigues a discussion on the resource theory of quantumness. We find that this measure is equivalent to the quantumness measures proposed formerly via correlation and relative entropy, by which they share these properties.
Continuous Prediction for Quality of Experience in Wireless Video Streaming

Due to the rapid development of communication technologies, the requirement of mobile video streaming services is extremely increased in recent years. However, the bandwidth limitation of the wireless network often causes video impairments, such as compression artifacts and rebuffering event, when users are watching online videos. Hence, this problem often causes the reduction of quality of experience (QoE). Predicting the QoE can provide a reference to improve resource allocation strategies, accordingly providing users with a higher quality of video streaming services. In order to predict the impact of video impairment, continuous prediction for the QoE in wireless video streaming is proposed. The input of the predicted model consists of three vectors that characterize frame quality, the state of rebuffering events, and memory effect, respectively, while the output consists of continuous predicted the QoE. The predicted model uses a block-structured nonlinear Hammerstein-Wiener model. The experimental results confirm that our proposed model can effectively predict the continuous QoE for wireless video streaming.
Symbol spotting for architectural drawings: state-of-the-art and new industry-driven developments

Abstract This review paper offers a contemporary literature survey on symbol spotting in architectural drawing images. Research on isolated symbol recognition is quite mature; the same cannot be said for recognizing a symbol in context. One important challenge is the segmentation/recognition paradox: a system should segment symbols before recognizing them, but some kind of recognition may be necessary to obtain a correct segmentation. Research has thus been recently directed toward symbol spotting, a way of locating possible symbol instances without using full recognition methods. In this paper, we thoroughly review symbol spotting methods with a focus on architectural drawings, an application domain providing the document image analysis and graphic recognition communities with an interesting set of challenges linked to the sheer complexity and density of embedded information, that have yet to be resolved. While most existing methods perform well in terms of recall, their performance is rather poor in terms of precision and false positives. In light of the review, we also propose a simple yet effective symbol spotting method based on template matching and a novel clutter-tolerant cross-correlation function that achieves state-of-the-art results with high precision, high recall, and few false positives, able to cope with “real-life clutter” found in industry-standard architectural drawings.
Quantifying the Alignment of Graph and Features in Deep Learning

We show that the classification performance of graph convolutional networks (GCNs) is related to the alignment between features, graph, and ground truth, which we quantify using a subspace alignment measure (SAM) corresponding to the Frobenius norm of the matrix of pairwise chordal distances between three subspaces associated with features, graph, and ground truth. The proposed measure is based on the principal angles between subspaces and has both spectral and geometrical interpretations. We showcase the relationship between the SAM and the classification performance through the study of limiting cases of GCNs and systematic randomizations of both features and graph structure applied to a constructive example and several examples of citation networks of different origins. The analysis also reveals the relative importance of the graph and features for classification purposes.
Implementation of Deep Reinforcement Learning

Reinforcement Learning (RL) is different from supervised learning, which is learning from a training set of labeled examples provided by a knowledgable external supervisor. RL is also different from unsupervised learning, which is typically about finding structure hidden in collections of unlabeled data. A Deep-Q-Network (DQN) RL system relied heavily on GPUs to accelerate computation. However, it is challenging to implement and deploy an RL model in an embedded system which has limited computing units and programming capacity. PYNQ with CPU-FPGA heterogeneous architecture is a platform that aims at developing embedded systems based on FPGA. This paper aims at constructing a fast FPGA prototyping framework for Cart-Pole problem on PYNQ platform.
Multi-Feature-Based Bilinear CNN for Single Image Dehazing

Image dehazing has been a great challenge in the process of adjusting haze images. In this paper, an effective and accurate dehazing method based on atmospheric scattering model is proposed. Since the dark channel is not applicable to sky areas, single-threshold segmentation combined with quad-tree partition technique is adopted to position and estimate the ambient light A* rapidly and accurately. In order to optimize transmittance, we employ a new convolutional network architecture, multi-feature-based bilinear CNN, which can mitigate the halo effect around the abrupt edges and restrain image noise, and the whole transmittance estimation process can be divided into three main parts: feature extraction, nonlinear mapping, and image reconstruction. A large number of outdoor haze image test results show that our optimized method has better experimental results than the existing methods.
An improved transfer adaptive boosting approach for mixed‐project defect prediction

Abstract Software defect prediction (SDP) has been a very important research topic in software engineering, since it can provide high‐quality results when given sufficient historical data of the project. Unfortunately, there are not abundant data to bulid the defect prediction model at the beginning of a project. For this scenario, one possible solution is to use data from other projects in the same company. However, using these data practically would get poor performance because of different distributional characteristics among projects. Also, software has more non‐defective instances than defective instances that may cause a significant bias towards defective instances. Considering these two problems, we propose an improved transfer adaptive boosting (ITrAdaBoost) approach for being given a small number of labeled data in the testing project. In our approach, ITrAdaBoost can not only employ the Matthews correlation coefficient (MCC) as the measure instead of accuracy rate but also use the asymmetric misclassification costs for non‐defective and defective instances. Extensive experiments on 18 public projects from four datasets indicate that: (a) our approach significantly outperforms state‐of‐the‐art cross‐project defect prediction (CPDP) approaches, and (b) our approach can obtain comparable prediction performances in contrast with within project prediction results. Consequently, the proposed approach can build an effective prediction model with a small number of labeled instances for mixed‐project defect prediction (MPDP).
Hyper-temporal variability analysis of solar insolation with respect to local seasons

The incoming solar radiation at any location results from the complex interactions of energy between the atmosphere and land or ocean surfaces. The available amount of solar insolation at the earth surface are spatially and temporally variable. These are varying predominantly at seasonal/daily basis which is caused by well-predictable astronomic factors (via solar altitude) and by changing atmospheric conditions (e.g. clouds, aerosols, water vapours and ozone). This variability also affects the predictability and manageability of all types of solar energy. The current study presents hyper-temporal variations analysis of solar insolation for Kalaburagi (erstwhile Gulbarga) district over the year 2004–2014 to understand the variability at a seasonal scope. The process was repeated on monthly bins of data (e.g. the eight Januaries, Februaries, etc.). The understanding of solar insolation requires a description of the temporal and spatial context to decipher a predictable, quantifiable variability at space-time continuum. The key aspects of the work introduces hyper-temporal (H-T) variability analysis of solar insolation with respect to local seasons. The accuracy of analysis at spatial or temporal solar insolation will enable us to add reliability (lower risk) and quality of the solar resources. Therefore, synergistic use of hyper-temporal data and advanced data analysis approaches will result in enhancing the potential of complex problem related to the application of solar resources for various purposes.
Modeling of Human Visual Attention in Multiparty Open-World Dialogues

This study proposes, develops, and evaluates methods for modeling the eye-gaze direction and head orientation of a person in multiparty open-world dialogues, as a function of low-level communicative signals generated by his/hers interlocutors. These signals include speech activity, eye-gaze direction, and head orientation, all of which can be estimated in real time during the interaction. By utilizing these signals and novel data representations suitable for the task and context, the developed methods can generate plausible candidate gaze targets in real time. The methods are based on Feedforward Neural Networks and Long Short-Term Memory Networks. The proposed methods are developed using several hours of unrestricted interaction data and their performance is compared with a heuristic baseline method. The study offers an extensive evaluation of the proposed methods that investigates the contribution of different predictors to the accurate generation of candidate gaze targets. The results show that the methods can accurately generate candidate gaze targets when the person being modeled is in a listening state. However, when the person being modeled is in a speaking state, the proposed methods yield significantly lower performance.
Preliminary design, modeling and control of a fully actuated quadrotor UAV

In this paper a preliminary study on a new concept of fully actuated Unmanned Aerial Vehicle (UAV), named ODQuad (OmniDirectional Quadrotor), is presented. By exploiting two additional actuators, the designed UAV can simultaneously modify the tilting angle of all the propellers, in such a way to decouple position and attitude motions. This solution, differently from other fully actuated UAVs with tilted propellers, avoids internal forces and energy dissipation, due to non-parallel propellers' axes. A preliminary mechanical design and the kinematic and dynamic models are developed. Moreover, a motion control scheme, based on a hierarchical two loop, has been designed. Simulations are provided in order to show the feasibility of the concept and the effectiveness of the control scheme.