Reviewerly/exHarmony huggingface dataset:
Pattern-Driven Security, Privacy, Dependability and Interoperability Management of IoT Environments

Achieving Security, Privacy, Dependability and Interoperability (SPDI) is of paramount importance for the ubiquitous deployment and impact maximization of Internet of Things (IoT) applications. Nevertheless, said requirements are not only difficult to achieve at system initialization, but also hard to prove and maintain at run-time. This paper highlights an approach to tackling the above challenges, through the definition of pattern language and a framework that can guarantee SPDI in IoT orchestrations. By integrating pattern reasoning engines at the various layers of the IoT infrastructure, and a machine-processable representation of said pattern through Drools rules, the proposed framework can provide ways to fulfill SPDI requirements at design time, and also provide the means to guarantee those SPDI properties and manage the orchestrations accordingly. Moreover, an application example of the framework is presented in an Industrial IoT monitoring environment.
Predicting Gastrointestinal Bleeding Events from Multimodal In-Hospital Electronic Health Records Using Deep Fusion Networks

Applying machine learning (ML) methods on electronic health records (EHRs) that accurately predict the occurrence of a variety of diseases or complications related to medications can contribute to improve healthcare quality. EHRs by nature contain multiple modalities of clinical data from heterogeneous sources that require proper fusion strategy. The deep neural network (DNN) approach, which possesses the ability to learn classification and feature representation, is well-suited to be employed in this context. In this study, we collect a large in-hospital EHR database to develop analytics in predicting 1-year gastrointestinal (GI) bleeding hospitalizations for patients taking anticoagulants or antiplatelet drugs. A total of 815,499 records (16,757 unique patients) are used in this study with three different available EHR modalities (disease diagnoses, medications usage, and laboratory testing measurements). We compare the performances of 4 deep multimodal fusion models and other ML approaches. NNs result in higher prediction performances compare to random forest (RF), gradient boosting decision tree (GBDT), and logistic regression (LR) approaches. We further demonstrate that deep multimodal NNs with early fusion can obtain the best GI bleeding predictive power (area under the receiver operator curve [AUROC] 0.876), which is significantly better than the HAS-BLED score (AUROC 0.668).
Pairwise Semantic Segmentation via Conjugate Fully Convolutional Network

Semantic segmentation has been popularly addressed using fully convolutional networks (FCNs) with impressive results if the training set is diverse and large enough. However, FCNs often fail to achieve satisfactory results due to a limited number of manually labelled samples in medical imaging. In this paper, we propose a conjugate fully convolutional network (CFCN) to address this challenging problem. CFCN is a novel framework where pairwise samples are input and synergistically segmented in the network for capturing a rich context representation. To avoid overfitting introduced by appearance and shape changes in a small number of training samples, a fusion module is designed to provide proxy supervision for the network training process. Quantitative evaluation shows that the proposed method has a significant performance improvement on pathological liver segmentation.
Adaptive learning feature pyramid for object detection

Inconsistent detection performance for objects of different scales lies in many state‐of‐the‐art object detection models. The feature pyramid network (FPN) alleviates this problem by fusing multi‐scale feature maps through a top‐down path. However, the features fusion strategy used in FPN lacks learning ability, which may result in suboptimal performance of the model. In this study, the authors propose a cross‐scale feature fusion network (CSFF) to fuse the low‐level location feature maps with the high‐level semantic feature maps. The CSFF first embeds a dilated convolution and deconvolution layer into the top‐down path of the FPN to enhance the learning ability of feature fusion. After that, an attention module is applied to suppress distraction and interference in the feature map. Each component of the CSFF is highly decoupled and can easily cooperate with a base network in an end‐to‐end training manner. In this study, they combine the CSFF with faster region with convolutional neural network and conduct a series of experiments on the PASCAL VOC 2007 and 2012 object detection datasets. Without any bells and whistles, the CSFF achieves a considerable detection improvement over the baseline network.
A Novel Framework for Change Requirement Management (CRM) In Agile Software Development (ASD)

Requirements changes play a huge role in Software Development Life Cycle (SDLC). Therefore, the change requirement management (CRM) is always considered an important activity in SDLC. Changing one requirement have a major impact on other requirements especially when teams are working from geographically different locations then it's become difficult to manage, categorize, organize and track requirement changes therefore CRM is considered as difficult task in SDLC especially in agile methodology. This paper review different research papers and identify the major problems associated with each existing framework such as time complexity, quick cost estimation, verification of implemented and unimplemented requirements and customer notification. On this basis of identified problems this paper proposed a framework that provides a way how to categorize requirements, track implemented/unimplemented requirements, Save/Update requirements in repository and provide notification to stakeholder about current status of requirement. Proposed framework facilitates organizations to manage changing requirements in agile software development (ASD) and reduces overall complexity and traceability issues of RCM.
UC-GAN for MR to CT Image Synthesis

Accurate MR-to-CT synthesis plays an important role in MRI-only radiotherapy treatment planning. In medical image synthesis, the cycle-generative adversarial network (CycleGAN) is becoming an influential method, however, its image quality of synthesis is not optimal yet. In this study, we proposed a new learning method named U-Net-CycleGAN (UC-GAN) to generate synthetic CT (sCT) image for MRI-only radiation treatment planning, which integrated an improved U-Net concept into the original CycleGAN framework. After experimental comparison, The MAE value and PSNR of our UC-GAN model are 76.7 ± 4.5 and 46.1 ± 1.5, respectively, which are statistics significantly better than the 94.0 ± 4.3 (MAE) and 45.1 ± 1.5 (PSNR) of the original CycleGAN model. The results of our quantitative evaluation show that the UC-GAN model can synthesize a CT image closer to the reference real CT image with better performance.
Ensemble Machine Learning Forecaster for Day Ahead PV System Generation

In this paper, the application of machine learning methods to predict the day ahead photovoltaic power generation in hourly intervals from the previous days, without using any exogenous data, have been studied. In order to select the relevant features, a random forest feature selection is used. This paper proposes a forecasting approach based on ensembles of artificial neural networks and support vector regression. The focus of this paper is on a single installed photovoltaic system, and in order to evaluate the performance of the proposed approaches, the measured data related to the photovoltaic installation on the roof of EnergyVille-1 is used. The results show that proposed approach can improve the accuracy of forecasting.
Ontology-Powered Hybrid Extensional-Intensional Learning

Deep learning has made headlines in the past few years due to successes in tasks, such as self-driving vehicles and board games, which were previously thought difficult or impossible. The successes have generated much interest in artificial intelligence among researchers and members of the public. However, deep learning algorithms generally require very large labelled data sets to work well and large labelled data sets are not always readily available. In addition, most machine learning techniques, including deep learning, often perform well statistically but can fail miserably when, for example, data are deliberately perturbed in an adversarial attack. Another criticism of deep learning techniques is a relative lack of explainability. This paper proposes the use of intentional learning to simultaneously address these issues. Preliminary evaluation on the MNIST data set has shown promising results. Specifically, by combing extensional and intensional learning, it is possible to achieve similar accuracy result as extensional learning only using only one-sixth of the original training data set.
Automatic method change suggestion to complement multi-entity edits

When maintaining software, developers sometimes change multiple program entities (i.e., classes, methods, and fields) to fulfill one maintenance task. We call such complex changes multi-entity edits. Consistently and completely applying multi-entity edits can be challenging, because (1) the changes scatter in different entities and (2) the incorrectly edited code may not trigger any compilation or runtime error. This paper introduces CMSuggester, an approach to suggest complementary changes for multi-entity edits. Given a multi-entity edit that (i) adds a new field or method and (ii) modifies one or more methods to access the field or invoke the method, CMSuggester suggests other methods to co-change for the new field access or method invocation. The design of CMSuggester is motivated by our preliminary study, which reveals that co-changed methods usually access existing fields or invoke existing methods in common. Our evaluation shows that based on common field accesses, CMSuggester recommended method changes in 463 of 685 tasks with 70% suggestion accuracy; based on common method invocations, CMSuggester handled 557 of 692 tasks with 70% accuracy. Compared with prior work ROSE, TARMAQ, and Transitive Association Rules (TAR), CMSuggester recommended more method changes with higher accuracy. Our research can help developers correctly apply multi-entity edits.
Automatic Privacy Detection in Scanned Document Images Based on Deep Neural Networks

The authors consider the problem of automatic detection of private scanned documents based on text recognition with deep neural networks. The paper suggests implementing a two-phase approach with the first stage which includes efficient EAST text detection and recognition using Tesseract OCR Engine. Secondly, the authors classify the privacy of a scanned document by deep neural networks applied to the extracted text. After that, a special dataset is gathered in order to train these networks. The experiments show that using OCR Engine for both text detection and segmentation ends up with relatively poor identification of private documents when compared to preliminary text detection with EAST method. Moreover, conventional keyword spotting using the list of sensitive words is less accurate when compared to neural network-based methods. Finally, it was demonstrated that the classification of a bag of most frequent words outperforms traditional text classification techniques with LSTM and convolutional networks.
The complexity of exact learning of acyclic conditional preference networks from swap examples

Learning of user preferences, as represented by, for example, Conditional Preference Networks (CP-nets), has become a core issue in AI research. Recent studies investigate learning of CP-nets from randomly chosen examples or from membership and equivalence queries. To assess the optimality of learning algorithms as well as to better understand the combinatorial structure of classes of CP-nets, it is helpful to calculate certain learning-theoretic information complexity parameters. This article focuses on the frequently studied case of exact learning from so-called swap examples, which express preferences among objects that differ in only one attribute. It presents bounds on or exact values of some well-studied information complexity parameters, namely the VC dimension, the teaching dimension, and the recursive teaching dimension, for classes of acyclic CP-nets. We further provide algorithms that exactly learn tree-structured and general acyclic CP-nets from membership queries. Using our results on complexity parameters, we prove that our algorithms, as well as another query learning algorithm for acyclic CP-nets presented in the literature, are near-optimal.
Medical Image Retrieval by Region Based Shape Feature For CT Images

Content Based Image Retrieval (CBIR) is the huge field for image retrieval from the wide databases. It is one of the popular techniques from computer vision domain. CBIR consist of feature extraction followed by similarity comparison. Shape feature extraction can be made on two descriptors one is region based and other is contour based. This paper implements shape feature extraction with region based descriptors. Zernike moments and Hu's seven moments have been used as a feature extraction techniques and Support Vector Machine (SVM) is used as a classifier. Different distance metrics are then used for similarity comparison with these feature extraction methods for efficient results. For performance evaluation distance metrics used are Euclidean, Chebyshev, Cityblock, Canberra, Standardized Euclidean (Seuclidean). Medical database with 6 classes consist of 100 images each namely head, hip, shoulder, pelvis, knee, ankle is used. After obtaining all the experimental results in terms of precision and recall, a comparative study is made for selected database.
AlleAlle: bounded relational model finding with unbounded data

Relational model finding is a successful technique which has been used in a wide range of problems during the last decade. This success is partly due to the fact that many problems contain relational structures which can be explored using relational model finders. Although these model finders allow for the exploration of such structures they often struggle with incorporating the non-relational elements.
DynTypo: Example-Based Dynamic Text Effects Transfer

In this paper, we present a novel approach for dynamic text effects transfer by using example-based texture synthesis. In contrast to previous works that require an input video of the target to provide motion guidance, we aim to animate a still image of the target text by transferring the desired dynamic effects from an observed exemplar. Due to the simplicity of target guidance and complexity of realistic effects, it is prone to producing temporal artifacts such as flickers and pulsations. To address the problem, our core idea is to find a common Nearest-neighbor Field (NNF) that would optimize the textural coherence across all keyframes simultaneously. With the static NNF for video sequences, we implicitly transfer motion properties from source to target. We also introduce a guided NNF search by employing the distance-based weight map and Simulated Annealing (SA) for deep direction-guided propagation to allow intense dynamic effects to be completely transferred with no semantic guidance provided. Experimental results demonstrate the effectiveness and superiority of our method in dynamic text effects transfer through extensive comparisons with state-of-the-art algorithms. We also show the potentiality of our method via multiple experiments for various application domains.
Eye-Tracking Based Visualizations and Metrics Analysis for Individual Eye Movement Patterns

Uniqueness in the analysis pattern of objects by individual humans has a profound impact on the study of their visual learning and behavior. Eye movement patterns have been effectively emerging as a biometric based key for security systems, product recognition patterns, user identifications, as well as medical research purposes. The modern eye tracking systems are non-invasive and financially affordable. Therefore, in this paper, we proposed eye-tracking based visualizations and metrics analysis for individual eye movement patterns collected during any kinds of activities depending on the scope of the our experimental paradigms. Individuals can be aware of their own performances during certain task and improve upon their weak areas. The objective of the paper is to utilize the important visual metrics obtained from fixation, saccades and face recognition and use them to analyze for individual categorization. The obtained results shown that the specific features and patterns can be extracted the viewing aspect of individual subjects using naive Bayes classifier. We were successfully able to predict the individual eye movements with an accuracy of 90.22%.
On the foundations of cryptography

research-article Share on On the foundations of cryptography Author: Oded Goldreich View Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 411–496https://doi.org/10.1145/3335741.3335759Published:04 October 2019Publication History 4citation182DownloadsMetricsTotal Citations4Total Downloads182Last 12 Months4Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Binary tomography on the isometric tessellation involving pixel shape orientation

In this study, a tomography reconstruction problem of binary images is considered on the isometric grid. On this grid, the triangle pixels have two types of orientations, accordingly, the authors call them delta or nabla shape pixels. The proposed reconstruction method uses data of projections of three natural directions. They are the lane directions of the triangular tessellation (these directions are somewhat analogous to row/column directions on the rectangular grids). The projection ray, penetrating through a grid lane, now not passing through the middle of pixels (i.e. through the middle line of triangle shape pixels), as usually taken, but little bit shifted from the middle parallel to the lane. This method provides the exact information about the number of nabla and delta shape triangle pixels in each lane of the image. This additional information is included in the reconstruction process to improve the quality of reconstruction. They formulate the suggested model into an energy-minimisation problem and apply a gradient-based approach for its minimisation. They show and analyse various experimental results on test images. The presented approach shows both better quality reconstructions and shorter running time than the earlier approaches.
MILP-Aided Related-Tweak/Key Impossible Differential Attack and its Applications to QARMA, Joltik-BC

In this paper, we study the relation of related-tweak/key impossible differentials with singlekey ones.Following a heuristic strategy, we can derive longer related-tweak/key impossible differentials from single-key ones.We implement this strategy with the MILP technique and apply it to search relatedtweak/key impossible differentials of two tweakable block ciphers: QARMA-64 and Joltik-BC-128.For QARMA-64, we find several 7-round related-tweak impossible differential distinguishers and use them to mount a 10-round key recovery attack including the outer whitening key; for Joltik-BC-128, we find two 6-round related-tweakey impossible differential distinguishers and use them attack 9-round and 10-round Joltik-BC-128 respectively.
Security of Streaming Media Communications with Logistic Map and Self-Adaptive Detection-Based Steganography

Voice over IP (VoIP) is finding its way into several applications, but its security concerns still remain. This paper shows how a new self-adaptive steganographic method can ensure the security of covert VoIP communications over the Internet. In this study an Active Voice Period Detection algorithm is devised for PCM codec to detect whether a VoIP packet carries active or inactive voice data, and the data embedding location in a VoIP stream is chosen randomly according to random sequences generated from a logistic chaotic map. The initial parameters of the chaotic map and the selection of where to embed the message are negotiated between the communicating parties. Steganography experiments on active and inactive voice periods were carried out using a VoIP communications system. Performance evaluation and security analysis indicates that the proposed VoIP steganographic scheme can withstand statistical detection, and achieve secure real-time covert communications with high speech quality and negligible signal distortion.
TangiTime

The environments constructed from contemporary technology (interactive, tangible and ubiquitous) are still challenging the mainstream HCI methods for designing interactive systems, demanding new ways of considering the interaction design and its evaluation. In this work, we investigate the concepts of enactive system and (socio)enactive experience by proposing the design of a learning environment. We designed and constructed TangiTime, an educational exhibit to explore the concept of 'deep time' in a (socio)enactive experience of interaction. We discuss the design process, the artifacts implementation, and results of an exploratory study to observe and understand how (socio)enactive aspects of experience potentially emerge into the educational exhibit.
XNORCONV: CNNs accelerator implemented on FPGA using a hybrid CNNs structure and an inter‐layer pipeline method

Nowadays, convolutional neural networks (CNNs) have become a research hotspot because of their high performance in computer vision and pattern recognition. However, as the high energy consumption of traditional graphic processing units-based CNNs, it is difficult to deploy them into portable devices. To deal with this problem, a hybrid CNN structure (XNORCONV) was proposed and implemented on field-programmable gate array (FPGA) in this study. Two improvements have been applied in XNORCONV. Firstly, the multiplications in the convolutional layer (CONV) were replaced by XNOR operations to save the multiplier and reduce computational complexity. Secondly, an inter-layer pipeline was designed to further accelerate the calculation. XNORCONV was implemented on Xilinx Zynq-7000 xc7z020clg400-1 under the clock frequency of 150 MHz and tested with MNIST dataset. The results of the experiment show that XNORCONV can classify each picture from MNIST in , and achieve 98.4% recognition accuracy. Compared with traditional Lenet-5 on different platforms, XNORCONV reduced multiplication by 85.6% with only 0.4% accuracy loss.