Reviewerly/exHarmony huggingface dataset:
Stereo Images Encryption by OSA &amp; RSA Algorithms

Abstract The proposed new partial encryption schemes use a secure encryption algorithm to encrypt only part of the compressed data. After application of image compression algorithm the partial encryption will applied. For two pairs of different gray scale images with the size (256 ´ 256) pixels, only 0.0244%-25% of the original data is encrypted. As a result, we see a significant reduction of time in the stage of encryption and decryption.In the compression step, the Orthogonal Search Algorithm (OSA) for motion estimation (the different between stereo images) is used. The resulting disparity vector and the remaining image were compressed by Discrete Cosine Transform (DCT), Quantization and arithmetic encoding. The image compressed was encrypted by RSA algorithm. The decoded images then compared with the original images.Good results showed in the experimental results of Peak Signal-to-Noise Ratio (PSNR), Compression Ratio (CR) and processing time. The proposed schemes of partial encryption are fast, secure and not be reducing in compression performance of the selected compression methods.
Detection of Occluded Road Signs on Autonomous Driving Vehicles

Autonomous driving vehicle relies heavily on its perception system to sense surrounding environments and make driving decisions. One important task on autonomous driving vehicles is to correctly recognize different traffic signs. However, the traffic signs in the wild can be in various conditions, e.g., occluded, deteriorated, or vandalized, and not all of them are recognizable. In this work, we propose a novel system that leverages the perception system on autonomous vehicle to identify occluded road signs in real time. Based on transfer learning, we propose the occluded sign classification network (OSCN) that is able to achieve a precision of 96.34% on a real-world dataset.
Improving the Accuracy of Energy Predictive Models for Multicore CPUs Using Additivity of Performance Monitoring Counters

Energy predictive modelling using performance monitoring counters (PMCs) has emerged as the leading mainstream approach for modelling the energy consumption of an application. Modern computing platforms such as multicore CPUs provide a large set of PMCs. The programmers, however, can obtain only a small number of PMCs (typically 3–4) during an application run due to the limited number of hardware registers dedicated to storing them. Therefore, selection of a reliable subset of PMCs as predictor variables is crucial to the prediction accuracy of online energy models. State-of-the-art methods for selecting the PMCs are largely based on their correlation with energy consumption. Recently, Additivity is introduced as a property of PMCs that appears to have significant impact on the accuracy of energy predictive models. It is based on an experimental observation that energy consumption of serial execution of two applications is equal to the sum of the energy consumption of those applications when they are run separately. In this work, we demonstrate how the accuracy of energy predictive models based on three popular techniques (Linear regression, Random forests, and Neural networks) can be improved by selecting PMCs based on a property of additivity.
Segmentation of gliomas in magnetic resonance images using recurrent neural networks

In our work we focus on automatic segmentation of high-grade gliomas (HGG) from magnetic resonance images (MRI). The results of segmentation have great impact on treatment of patients and consequently on the length of their life. In this paper a new approach of automatic glioma segmentation based on recurrent neural units is proposed. We use the Long Short-Term Memory units (LSTMs) which are able to extract latent features of brain structure by global contextual information. Unlike convolutional neural networks, where global context is gained by combination of local features, LSTMs have the potential to capture the global context at once. We use a region-based classification using the 3D Hilbert space-filling curve. To evaluate this method, the HGG data from the International Multimodal Brain Tumor Segmentation (BraTS-17) Challenge 2017 are being used. Our method achieved a dice score 0.62, 0.77, 0.64, on validation dataset of BraTS-17, for enhancing tumor, whole tumor and tumor core, respectively.
Adopting Immersive Technologies for Design Practice: The Internal and External Barriers

Abstract In the first quarter of the new millennium, the immersive technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) are only a few steps away from becoming the mainstream tools within the design industry. This study investigated the internal and external barriers of technology adoption within design-oriented businesses. A mixed method was used to collect and analyze the data from the employees of a large design firm. This research confirmed that external barriers such as funding, technical support, training, and business strategy that exist at the organizational level are interrelated with the internal barriers such as designers’ and managers’ perception and attitude toward the new technologies. The managerial applications were discussed later and the directions for future research were provided.
A Framework for Universally Composable Oblivious Transfer from One-Round Key-Exchange

Oblivious transfer is one of the main pillars of modern cryptography and plays a major role as a building block for other more complex cryptographic primitives. In this work, we present an efficient and versatile framework for oblivious transfer (OT) using one-round key-exchange (ORKE), a special class of key exchange (KE) where only one message is sent from each party to the other. Our contributions can be summarized as follows: Examples are presented using the classical Diffie-Hellman KE, RLWE-based KE and Supersingular Isogeny Diffie-Hellman KE.
Tracking Assisted Faster Video Object Detection

Recent approaches have achieved great success on still image object detection. Despite the high accuracy, directly applying image object detectors for video object detection is rather slow. Inspired from the fact that object tracking is much more efficient than object detection, we propose to combine object detection and tracking for fast video object detection. Computational expensive detection network is applied on sparsely arranged key frames, while proposals of non-key frames are obtained through tracking and regression of previous frame's proposals. Assisted with an adaptive key-frame arrangement module, our method can adaptively decide whether to track or to detect based on tracking quality. Extensive experiments show that the proposed method can significantly boost detection speed with a rather small drop in detection accuracy.
Uncertainty‐aware forecast interval for hourly PV power output

A forecast interval is effective for handling the forecast uncertainty in solar photovoltaic systems. In estimating the forecast interval, most available approaches apply an identical policy to all the point forecasts. This results in an inefficient interval (e.g. an unnecessarily wide interval for an accurate forecast). They also adopt a complex model and even require modification of the available deterministic forecasting model, which may adversely affect their application. To overcome these limitations, the authors introduce a forecast uncertainty-aware forecast interval. They calculate a forecast accuracy-related uncertainty metric from an ensemble method based on the dropout technique. The dropout technique is widely used in deep learning models. This implies that the proposed approach can be applied to available deep learning forecasting models without modifying them. Using the uncertainty metric and relevant data of previous forecast results, they estimate the uncertainty-aware forecast interval. Through experiments using real-world data, they first demonstrate the close relation of their uncertainty metric to the forecasting accuracy. Then, they demonstrate that the uncertainty-aware forecast interval reduces the mean interval length by up to 25.7% and decreases the prediction interval coverage probability by 4.07%, compared to available approaches. This illustrates that their approach results in an effective interval.
Domain-Specific Languages for the Design, Deployment and Manipulation of Heterogeneous Databases

The need for levels of availability and scalability beyond those supported by relational databases has led to the emergence of a new generation of purpose-specific databases grouped under the term NoSQL. In general, NoSQL databases are designed with horizontal scalability as a primary concern and deliver increased availability and fault tolerance at a cost of temporary inconsistency and reduced durability of data. To balance the requirements for data consistency and availability, organisations increasingly migrate towards hybrid data persistence architectures comprising both relational and NoSQL databases. The consensus is that this trend will only become stronger in the future; critical data will continue to be stored in ACID (largely relational) databases while non-critical data will be progressively migrated to high-availability NoSQL databases. Designing and deploying a hybrid data persistence architecture that involves a combination of relational and NoSQL databases is a complex, technically challenging and error-prone task. In this paper we outline a model-based methodology developed in the context of the EC-funded H2020 TYPHON project for designing, developing, querying and evolving such scalable architectures for persistence, analytics and monitoring of large volumes of hybrid (relational, graph-based, document-based, natural language, etc.) data, in a systematic and disciplined manner.
A Survey of Technology Used to Conduct Virtual Research Consultations in Academic Libraries

AbstractResearchers surveyed academic librarians to determine current software and practices used in synchronous virtual research consultations. The survey findings identified the top software products currently used by respondents for virtual research consultations. Through open-ended comments, it became clear that libraries are not tied to one particular product, but are remaining flexible by offering multiple platforms for virtual research consultations.Keywords: Academic librariesresearch consultationsdistance learningvirtual referencesoftwaresurvey
L1-norm-based kernel entropy components

Kernel entropy component analysis (KECA) is a recently proposed dimensionality reduction approach, which has showed superiority in many pattern analysis algorithms previously based on principal component analysis (PCA). The optimized KECA (OKECA) is a state-of-the-art extension of KECA and can return projections retaining more expressive power than KECA. However, OKECA is not robust to outliers and has high computational complexities attributed to its inherent properties of L2-norm. To tackle these two problems, we propose a new variant of KECA, namely L1-norm-based KECA (L1-KECA) for data transformation and feature extraction. L1-KECA attempts to find a new kernel decomposition matrix such that the extracted features store the maximum information potential, which is measured by L1-norm. Accordingly, we present a greedy iterative algorithm which has much faster convergence than OKECA's. Additionally, L1-KECA retains OKECA's capability to obtain accurate density estimation with very few features (just one or two). Moreover, a new semi-supervised L1-KECA classifier is developed and employed into the data classification. Extensive experiments on different real-world datasets validate that our model is superior to most existing KECA-based and PCA-based approaches. Code has been also made publicly available.
Metaphors for Software Visualization Systems Based on Virtual Reality

The paper discusses research and development in the field of software visualization based on virtual reality environments. Spatial metaphors play an important role in such systems. A brief overview of the projects of software visualization systems based on vir- tual reality is provided. Among the systems developed over the past decades, one can find systems both for program visualization and for visual programming. Descriptions of prototypes of software visualization systems, software objects visualization and supercomputer performance data visualization, realized by the authors of the paper, are presented. These prototypes, designed for virtual reality environments, were developed with the use of several versions of a cosmic space metaphor and an extended city metaphor. The paper also discusses psychological aspects of the human factor in developing software visualization systems with the use of virtual reality.
Finding theme communities from database networks

Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities. Here, a theme community is a cohesive subgraph such that a common pattern is frequent in all transaction databases associated with the vertices in the subgraph. Finding all theme communities from a database network enjoys many novel applications. However, it is challenging since even counting the number of all theme communities in a database network is #P-hard. Inspired by the observation that a theme community shrinks when the length of the pattern increases, we investigate several properties of theme communities and develop TCFI, a scalable algorithm that uses these properties to effectively prune the patterns that cannot form any theme community. We also design TC-Tree, a scalable algorithm that decomposes and indexes theme communities efficiently. Retrieving a ranked list of theme communities from a TC-Tree of hundreds of millions of theme communities takes less than 1 second. Extensive experiments and a case study demonstrate the effectiveness and scalability of TCFI and TC-Tree in discovering and querying meaningful theme communities from large database networks.
Definitions and Attributes of Smart Home Appliances

Abstract The status of Smart Home Appliances (SHAs) in the smart home industry has been raised as major components of a smart home. To design a good SHA, diverse stakeholders in the smart home ecosystem need to have a common understanding of smart homes and SHAs. However, the studies on what they are, especially those reflecting practitioners’ point of view, have been scarce. In this research, we conducted systematic literature reviews and practitioner interviews using the concept mapping method to identify the attributes of SHAs depending both on academia and industry. In addition, we suggest a framework of the smart home ecosystem which describes how those attributes are revealed in the relationships among SHAs, users, and external stakeholders. Since all of the interviewees are Korean SHA development professionals, the findings from this research could have been limited to the current status of the Korean SHA industry. Nonetheless, we expect that this research would contribute to building a consensus on what smart homes and SHAs are and provide the designers and developers of SHAs with systematic perspectives on smart homes.
Adversarial Nuclei Segmentation on H&amp;E Stained Histopathology Images

Computer aided methods in pathology are advancing rapidly. Problems like segmentation, classification and detection of pathology images are solved with machine learning and image processing techniques. State-of-the-art methods in nuclei segmentation problem include supervised deep learning techniques. However, labeling process of pathology images is an expensive and time consuming process. In this work, nuclei segmentation problem is formulated as image-to-image translation problem and using Cycle-Consistent Generative Adversarial Networks, an unsupervised segmentation scheme is proposed for hematoxylin & eosin stained histopathology data.
Robustness-Driven Hybrid Descriptor for Noise-Deterrent Texture Classification

A robustness-driven hybrid descriptor (RDHD) for noise-deterrent texture classification is presented in this paper. This paper offers the ability to categorize a variety of textures under challenging image acquisition conditions. An image is initially resolved into its low-frequency components by applying wavelet decomposition. The resulting low-frequency components are further processed for feature extraction using completed joint-scale local binary patterns (CJLBP). Moreover, a second feature set is obtained by computing the low order derivatives of the original sample. The evaluated feature sets are integrated to get a final feature vector representation. The texture-discriminating performance of the hybrid descriptor is analyzed using renowned datasets: Outex original, Outex extended, and KTH-TIPS. The experimental results demonstrate a stable and robust performance of the descriptor under a variety of noisy conditions. An accuracy of 95.86%, 32.52%, and 88.74% at noise variance of 0.025 is achieved for the given datasets, respectively. A comparison between performance parameters of the proposed paper with its parent descriptors and recently published paper is also presented.
A Comparative Study of Various CAPTCHA Methods for Securing Web Pages

CAPTCHA is broadly used for to make system secure. Its aim is to avoid robotic form submission or prove customer individuality. A pattern matching algorithm is useful to user for imagery to see gesture and find similar matches. This technique is very hard to gain unauthorized access of data since crafty bit is to recognize gestures in the images. This technique is not feasible present day. This technique performs actions on the behavior of user gestures which make it distinctive and secure. A Human Computer Interaction between the computer and individual easily understand person language and raise the users are responsive interface. The gesture is the non-verbal type of message communication which enables the Human Computer Interaction interface. The gesture is much more skilled to reduce the utilization of most conspicuous an electronic device which the additional strategy are to deal with computers. Scientists have just considered that Turing examination with the cartoon CAPTCHA is fun and amusing method. In this paper, authors have discussed various different methods of CAPTCHA to secure web page and user's individuality.
The Science of Rule-based Classifiers

The exponential rise in the amount of data has fueled various facets in data science and data engineering. Classification is a supervised machine learning task that underlies several diagnosis systems and rule-based decision support systems. The efficiency of a rule-based classifier depends on factors such as quality of the rules in the ruleset, rule ordering, and cardinality of the ruleset. This paper experimentally analyzes the effect of case satisfaction mechanisms and rule selection approaches in the design of rule-based classifiers with a focus on clinical datasets. Moreover, a hybrid rule combination approach called rule pool is proposed. Rule pool combines the best rules from a decision tree classifier and associative classifiers. Results of the experiments with 5 medical datasets obtained from the University of California Irvine repository show that the proposed hybrid approach has its advantages in comparison with previous methods in terms of classification accuracy. Rules from Decision trees on an average yielded an accuracy of 79.92%; Associative Classifiers yielded an accuracy of 74.79% and the hybrid rule pool based approach yielded an accuracy of 80.95%. Moreover, a difference in classification efficiency based on the Best-first and Best-fit case satisfaction methods was inferred; the former yielded an average accuracy of 76.89% and the latter yielded 79.87%. From the analysis of the experimental results, it was inferred that the rule pool includes informative rules from decision trees and also the top-ranked rules of the associative classifiers. This causes the efficiency of the rule pool approach to be relatively higher than the traditional rule-based classifiers and associative classifiers. Moreover, on combining the rules from different algorithms, when the one-item rules were replaced by longer non-redundant strong rules, an improvement in accuracy was observed. The findings of this work may be considered for designing efficient rule-based classification approaches for clinical knowledge-mining framework.
Rank-Select Indices Without Tears

A rank-select index for a sequence $$B=(b_1,\ldots ,b_n)$$ of n bits, where $$n\in \mathbb {N}=\{1,2,\ldots \}$$ , is a data structure that, if provided with a constant-time operation to access (the integer whose binary representation is) the subsequence of B in $$\varTheta (\log n)$$ specified consecutive positions (thus B is stored outside of the data structure), can compute $$\textit{rank}_B(j)=\sum _{i=1}^j b_i$$ for given $$j\in \{0,\ldots ,n\}$$ and $$\textit{select}_B(k)=\min \{j\in \mathbb {N}\mid \textit{rank}_B(j)\ge k\}$$ for given $$k\in \{1,\ldots ,\sum _{i=1}^n b_i\}$$ . We describe a new rank-select index that, like previous rank-select indices, occupies $$O({{n\log \log n}/{\log n}})$$ bits and executes $$\textit{rank}$$ and $$\textit{select}$$ queries in constant time. Its derivation is intended to be largely free of tedious low-level detail, its operations are given by straight-line code, and it can be constructed in $$O({n/{\log n}})$$ time if B can be accessed as above.
DISCO: Depth Inference from Stereo using Context

Recent deep learning based approaches have outperformed classical stereo matching methods. However, current deep learning based end-to-end stereo matching methods adopt a generic encoder-decoder style network with skip connections. To limit computational requirement, many networks perform excessive down sampling, which results in significant loss of useful low-level information. Additionally, many network designs do not exploit the rich multi-scale contextual information. In this work, we address these aforementioned problems by carefully designing the network architecture to preserve required spatial information throughout the network, while at the same time achieve large effective receptive field to extract multiscale contextual information. For the first time, we create a synthetic disparity dataset reflecting real life images captured using a smartphone; this enables us to obtain state-of-the-art results on common real life images. The proposed model DISCO is pre-trained on the synthetic Scene Flow dataset and evaluated on popular benchmarks and our in-house dataset of challenging real life images. The proposed model outperforms existing state-of-the-art methods in terms of quality as well as quantitative metrics.