Reviewerly/exHarmony huggingface dataset:
web-rMKL: a web server for dimensionality reduction and sample clustering of multi-view data based on unsupervised multiple kernel learning

More and more affordable high-throughput techniques for measuring molecular features of biomedical samples have led to a huge increase in availability and size of different types of multi-omic datasets, containing, for example, genetic or histone modification data. Due to the multi-view characteristic of the data, established approaches for exploratory analysis are not directly applicable. Here we present web-rMKL, a web server that provides an integrative dimensionality reduction with subsequent clustering of samples based on data from multiple inputs. The underlying machine learning method rMKL-LPP performed best for clinical enrichment in a recent benchmark of state-of-the-art multi-view clustering algorithms. The method was introduced for a multi-omic cancer subtype discovery setting, however, it is not limited to this application scenario as exemplified by a presented use case for stem cell differentiation. web-rMKL offers an intuitive interface for uploading data and setting the parameters. rMKL-LPP runs on the back end and the user may receive notifications once the results are available. We also introduce a preprocessing tool for generating kernel matrices from tables containing numerical feature values. This program can be used to generate admissible input if no precomputed kernel matrices are available. The web server is freely available at web-rMKL.org.
Revisiting the ARM Debug Facility for OS Kernel Security

Hardware debugging facilities, such as watchpoints, have been used for software development and analysis. In this paper, we expanded the use of watchpoints as a hardware security primitive for enhancing the runtime security of mobile devices. By analyzing the watchpoints in detail, we derived useful watchpoint properties that can be exploited to build security applications. Based on our analysis, we designed example applications for hardening the OS kernel by exploiting watchpoints. The proposed applications were implemented on a Juno development board with 64-bit ARM architecture (ARMv8). Hardening the kernel by fully enabling the proposed schemes was found to impose reasonable overhead, i.e., 3% with SPEC CPU2006.
A State Encoding Methodology for Side-Channel Security vs. Power Trade-off Exploration

Power side-channel attacks have been shown to be effective against recovering protected information from integrated circuits. Existing defense methods are expensive in area, power or both. Small-scale ICs used in embedded systems and IoT devices are expected to be safe and secure, and yet cannot afford the area and power overheads of the sophisticated defense methods. This paper presents a design methodology for finite state controllers (FSMs) to defend against power analysis attacks while ensuring low power overhead. Further, a desired level of security can be achieved while minimizing power consumption. We formulate a set of constraints on state encoding based on security and power metrics. We express these constraints as a Boolean satisfiability (SAT) problem and use a SAT solver to generate constraint satisfying encodings. Using over 100 FSMs from BenGen and MCNC benchmark suites, experimental results show an average power reduction of up to 40% with respect to secure-only FSMs and 4-20% reduction with respect to minimal encoding strategy. Trade-off between security and power is demonstrated as mutual information between power side-channel and Hamming attacks models varies between 0 and 2.
Approximating large scale arbitrary unitaries with integrated multimode interferometers

Unitary operations using linear optics have many applications within the quantum and neuromorphic space. In silicon photonics, using networks of simple beam splitters and phase shifters have proven sufficient to realize large-scale arbitrary unitaries. While this technique has shown success with high fidelity, the grid physically scales with an upper bound of O(n<sup>2</sup>). Consequently, we propose to considerably reduce the footprint by using multimode interference (MMI) devices. In this paper, we investigate the active control of these MMIs and their suitability for approximating traditionally used unitary circuits.
Cybersecurity Certification: Certified Information Systems Security Professional (CISSP)

There is a large and fast growing demand for cybersecurity professionals who are well prepared and qualified to perform the challenging work of defending the cyber space. This paper explores and discusses the significant value and benchmark role of the Certified Information Systems Security Professional (CISSP) certification in the competency development for cybersecurity workforce by analyzing the CISSP certification requirements and objectives and mapping them to the US cybersecurity industry model of competencies and the US national cybersecurity workforce framework (NCWF). This paper also discusses the value and implications of the CISSP certification on cybersecurity education and training curriculum.
Evolutionary Perspective of Structural Clones in Software

Cloning in software represents similar program structures having its own benefits and drawbacks. Proper clone analysis is required to exploit the benefits of clones. A study of software clone evolution serves the purpose of understanding the maintenance implications of clones, which leads to their appropriate management. Structural clones (recurring patterns of simple clones) represent design level similarities in software. Evolutionary characteristics of clones can assess the relevance of those clones for software developers and maintainers. Although the evolution of simple clones has been thoroughly studied by researchers, the evolution of structural clones is still to be explored. In this paper, we study the evolution of structural clones by performing a longitudinal study on multiple versions of five Java systems. To perform a systematic study of the structural clone evolution, we define structural clones and their evolution patterns in a formal notation. Our results show that structural clones are more likely to change inconsistently, however, less frequently than simple clones, whereas the lifetime of the structural clones is similar to that of the simple clones. Evolutionary characteristics of structural clones suggest that they require more attention in their management. Analysis of structural clone evolution reveals similar reasons for changes, and similar trends in evolution patterns, for all subject systems. These trends reveal evolutionary characteristics of structural clones that can help in devising appropriate strategies for managing them, hence devising better clone management systems.
Development of an Innovative Mobile Phone-Based Newborn Care Training Application

Mobile infrastructure in low - and middle-income countries (LMIC) has shown immense potential to reach the unreachable. Healthcare providers (HCP) are one such group who are at the frontline of the fight against infant mortality in LMICs. Mortality among newborn infants (birth to 28 days) now accounts for around 45% of all under 5-years child mortality. Birth asphyxia is one of the three leading causes of newborn death; neonatal resuscitation training, among health care providers, reduces mortality from birth asphyxia. We have developed a mobile phone-based training app, called mobile Helping Babies Survive (mHBS), to support the training of health care providers on neonatal resuscitation. mHBS is integrated with the District Health Information System (DHIS2) platform, which is used in over 60 countries around the world. The mHBS/DHIS2 training app is a part of an application suite which includes another DHIS2-linked data collection app, mHBS tracker. The mHBS training application has the potential to scale-up integration with other neonatal training apps. Ultimately, the mHBS training suite will provide new insights into healthcare worker education along with the necessary tools for effective care of newborn babies.
SALSA

Trolls designed to create discord, chaos, and misinformation have been on the rise lately because of the heightened anonymity provided by the internet. Current methods of troll detection focus on identifying whether a user is a troll are based on various features outside a single instance of a post. We explore the possibility of using sentiment, aggression, lexical, and syntactic textual features to determine whether a tweet is meant to troll or not. From experiments and analysis, it was shown that these textual features are indeed sufficient for a classifier to perform well in detecting whether or not a tweet was meant to troll. Furthermore, we also show how these features are relevant for troll detection and how certain ways to combine these features can improve performance in terms of accuracy, precision, recall and F1 score.
Genetic programming theory and practice: a fifteen-year trajectory

The GPTP workshop series, which began in 2003, has served over the years as a focal meeting for genetic programming (GP) researchers. As such, we think it provides an excellent source for studying the development of GP over the past fifteen years. We thus present herein a trajectory of the thematic developments in the field of GP.
On the learning machine with quaternionic domain neural network and its high-dimensional applications

There are various high-dimensional engineering and scientific applications in communication, control, robotics, computer vision, biometrics, etc.; where researchers are facing predicament to fabricate an intelligent and robust neural system which can process higher dimensional information efficient ly. In various literatures, the conventional neural networks based only on real valued, are tried to solve the problem associated with high-dimensional parameters, but these neural network structures possess high complexity and are very time consuming and weak to noise. These networks are also not able to learn magnitude and phase values simultaneously in space. The quaternion is the number, which possesses the magnitude in all four directions and phase information is embedded within it. This paper presents a learning machine with a quaternionic domain neural network that can finely process magnitude and phase information of high dimension data without any hassle. The learning and generalization capability of the proposed learning machine is performed through chaotic time series predictions (Lorenz system and Chua's circuit), 3D linear transformations, and 3D face recognition as benchmark problems, which demonstrate the significance of the work.
Research on Domain Ontology Automation Construction Based on Chinese Texts

The main construction method of the current ontology is to rely on ontology experts for manual construction. Because manual construction requires a lot of manual participation, manual construction has great limitations. Text data as one of the main forms of data source, how to construct domain ontology automatically from texts and how to provide semantic retrieval support to text quickly by ontology is the hotspot of ontology research at present. Aiming at the above problems, an automatic construction method of domain ontology based on knowledge graph and association rule mining is presented, and it can extract the concepts, hierarchies and non-hierarchies of domain ontology from text, and finally form ontology by Jena. It also provides semantic retrieval of text by associating text and concepts in the process of ontology construction. Finally, the effect of automatic ontology construction is verified by the effect of text retrieval.
Modification of Gradient Vector Flow Using Directional Contrast for Salient Object Detection

Scene analysis is a relevant research field for its several applications in the area of computer vision. This paper attempts to analyze scene information present in the image by augmenting salient object information with background information. The salient object is initially identified using a method called as Minimum Directional Contrast (MDC). The underlying assumption behind using this method for defining salient objects is that salient pixels have higher minimum directional contrast than nonsalient pixels. Finding MDC provides us with a raw salient metric. The gradient vector flow (GVF) model of image segmentation inculcates the raw saliency information. The gradient of MDC is calculated and added to the data term of the energy functional of GVF so that the contour formation utilizes not only edge formation but also saliency information. The result obtained gives us not only the salient object but also added background information. Three public datasets have been used to evaluate the results obtained. The comparative study of the proposed method for salient object detection with other state-of-the-art methods available in the literature is presented in terms of precision, recall, and F1-Score.
A Comparative Study and State-of-the-art Evaluation for Pedestrian Detection

Pedestrian detection has many applications in computer vision including robotics, scene understanding, person reidentification and video-surveillance system. In fact, the process of person detection aims to detect and localize each person in the images, represented via bounding boxes. Recent deep learning pedestrian detectors, which are hybrid methods that combines traditional hand-crafted features and deep convolutional features such as Fast/Faster Region based-CNN (R-CNN), have shown excellent performance for general object detection. In this context, we propose in this paper an overview of the state-of-the-art performance of current deep learning pedestrian detectors and a comparison of these detectors is provided. Evaluation criteria, popular datasets used for evaluation and a quantitative results are also described and discussed in this work.
Preserving privacy of drone videos using proxy re-encryption technique

Unmanned Aerial Vehicles (UAVs) also known as drones are being used in many applications where they can record or stream video. One of such applications is the Intelligent Transportation Systems (ITS) where drones may need to record videos and send a control center to be shared by various clients such as law enforcement or emergency personnel. In such cases, the recording might include faces of civilians or other sensitive information that might pose privacy concerns. While the video can be encrypted and stored in the cloud that way, it can still be accessed once the keys are exposed to third parties. To prevent such cases, in this paper, we propose a proxy re-encryption technique that will provide a key to third parties that can be used only once to access the videos. The key management is handled by a trusted control center. The implementation results indicate that there is almost no additional overhead with the approach while it can still preserve the privacy.
Supervised Lexicon Extraction for Emotion Classification

Emotion Classification (EC) aims at assigning an emotion label to a textual document with two inputs – a set of emotion labels (e.g. anger, joy, sadness) and a document collection. The best performing approaches for EC are dictionary-based and suffer from two main limitations: (i) the out-of-vocabulary (OOV) keywords problem and (ii) they cannot be used across heterogeneous domains. In this work, we propose a way to overcome these limitations with a supervised approach based on TF-IDF indexing and Multinomial Linear Regression with Elastic-Net regularization to extract an emotion lexicon and classify short documents from diversified domains. We compare the proposed approach to state-of-the-art methods for document representation and classification by running an extensive experimental study on two shared and heterogeneous data sets.
Enhancing Security for JPEG Image Against Mosaic Attack Using Inter-Block Shuffle Encryption

Inter-block shuffle has been proved to be able to enhance the security for JPEG image encryption.However, it does not change the numbers of non-zero coefficients in 8 * 8 blocks.This leads to information leakage.In this paper, the defects of inter-block shuffle are analyzed.An attack scheme is developed based on a combined application of chosen-plain-text attack and non-zero quantized coefficients counting attack.It is verified that the main content of the original image could be reconstructed without the knowledge of encryption algorithm details and keys.The attack results are visualized to be mosaic appearance images.An improvement of the attack is also provided, which is called a fast mosaic attack.In the improved attack, the chosen images are designed to have distinguished numbers of non-zero quantized DCT coefficients in different blocks.The designed images remarkably accelerate the attack processing.The fast mosaic attack generates the same results as the original scheme with the much smaller time cost.
Steganography in stylized images

We propose a steganographic scheme for stylized images. Given a natural image, an image style transfer algorithm is executed twice to produce two similar stylized images with different parameters. One of them is used for embedding and another one is employed as a reference. For the cover image, embedding costs are assigned to measure the detection risk of the modifications. Each embedding cost is then asymmetrically adjusted into two polarity costs to further measure the detection risk of different modification polarities with the guidance of the reference image. After embedding with syndrome-trellis coding, the difference of the stegoimage and the reference image is minimized, which results in a high undetectability against steganalysis. Experimental results also prove that the proposed scheme performs much better than the existing steganographic methods when examined by modern steganalytic tools.
Real-Time Tracking-by-Detection in Broadcast Sports Videos

This paper presents a novel algorithm for real-time tracking-by-detection of players in sports videos. The solution consists of a Convolutional Neural Network with optimized architecture and a new type of Inter Frame Connection logic for data association and tracklet handling. The proposed data association connects region proposals in the current frame with detected objects in the previous frames. The association is established before thresholding the region proposals and used to stabilize the detection performance. The proposed solution demonstrates superior performance over the existing pre-trained detection models and tracking concepts.
A variational image segmentation method exploring both intensity means and texture patterns

Existing image segmentation methods usually consider only intensity or texture features, and thus cannot work well on images with both varying intensity and salient texture patterns. This paper aims to solve this problem by exploring both intensity and texture features for better image segmentation. To this end, we propose a novel variational image segmentation model, which uses constant vectors to represent intensity means in different image subregions and structured dictionaries to encode local texture patterns in images. We develop an effective algorithm to implement the proposed variational model by seeking for intensity means and a specific dictionary for the input image and meanwhile computing the representation coefficients of image patches with respect to the learned dictionary. We derive closed-form solutions for all these components, which result in the high efficiency of our proposed method. Extensive evaluation experiments with comparison to existing methods have been done on both synthetic and real-world images. The results demonstrate the superiority of our proposed method in effectively segmenting images with both unsmooth intensity variations and salient texture patterns.
Usage of Hough Transform for Expiry Date Extraction via Optical Character Recognition

In this article we evaluate the impact of using two image pre-processing approaches with the objective of aiding an Optical Character Recognition (OCR) software in correctly retrieving an expiry date from an image of a product containing it. In particular, we analyze the impact of finding the rotation angle of an image using the Hough transform and the impact of image binarization using adaptive Gaussian threshold. We attempt to further increase OCR accuracy through a sliding window approach. Our results show that applying the Hough transform noticeably improves OCR performance with minimal impact on the execution time.
A Pattern-Oriented Design Framework for Self-Adaptive Software Systems

Multiple interacting MAPE-K loops, structured according to specific interaction patterns, have been introduced to design the adaptation logic in case of decentralized self-adaptive software systems. Designing such complex systems requires the availability of tools where MAPE patterns can be easily instantiated to provide fast architectural solutions, and the encoding towards specific domains is facilitated by automatic mapping of such pattern instantiations in domain-specific languages; validation and verification must be also supported to assure correct development of reliable systems. In this paper, we present a pattern-oriented framework, based on the MSL (MAPE Specification Language) modeling language, for the design of self-adaptive systems. The framework supports: (i) explicit modeling of the adaptation logic in terms of patterns of interactive MAPE-K loops; (ii) ability to tailor MSL models for a specific application domain and synthesize from them other modeling artifacts/code according to a target implementation context and scope (e.g., OpenHAB); (iii) ability to perform validation and verification of MSL models by means of the ASMETA formal framework.
Collaborative Filtering Using Fuzzy Clustering for Categorical Multivariate Data Based on <i>q</i>-Divergence

In this study, a collaborative filtering method that uses fuzzy clustering and is based on q -divergence is proposed for categorical multivariate data. The results of experiments conducted on an artificial dataset indicate that the proposed method is more effective than the conventional one if the number of clusters and the initial setting are adequately set. Furthermore, the results of the experiments conducted on three real datasets indicate that the proposed method outperforms the conventional method in terms of recommendation accuracy as well.
After Brazil’s General Data Protection Law: Authorization in Decentralized Web Applications

Decentralized web applications do not offer fine-grained access controls to users’ data, which potentially creates openings for data breaches. For software companies that need to comply with Brazil’s General Data Protection Law (LGPD), data breaches not only might harm application users but also could expose the companies to hefty fines. In this context, engineering fine-grained authorization controls (that comply with the LGPD) to decentralized web application requires creating audit trails, possibly in the source code. Although the literature offers some solutions, they are scattered. We present Esfinge Guardian, an authorization framework that completely separates authorization from other concerns, which increases compliance with the LGPD. We conclude the work with a brief discussion.