Reviewerly/exHarmony huggingface dataset:
Trusted relational databases with blockchain: design and optimization

With the emergence of large scale data collection from Internet of Things and mobile devices, the notion of trust is now an increasingly important aspect of the next generation of data processing systems. We propose a blockchain enabled relational storage system that supports immutable transactions and temporal snapshots. By embedding blockchains in relational tables, the database stores trust related information in a tamper proof fashion, making the data provenance provably verifiable. To support large query workloads, we further propose an optimization algorithm that determines the best temporal snapshots to materialize in order to minimize the total time cost of answering a given query workload. Experimental evaluation shows that our materialized snapshot approach improves the performance of large analytical query workloads by as much as 50 times.
A question answering system in hadith using linguistic knowledge

Question answering system aims at retrieving precise information from a large collection of documents. This work presents a question answering method to apply on Hadith in order to provide an informative answer corresponding to the user's query. Hadith englobes stories and qualification of the prophet Muhammad (PBSL). It also includes the sayings of his companions and their disciples. The problem with current methods is that they fail to capture the meaning when comparing a sentence and a user's query; hence there is often a conflict between the extracted sentences and user's requirements. However, our proposed method has successfully tackled this problem through: (1) avoiding extract a passage whose similarity with the query is high but whose meaning is different. (2) Computing the semantic and syntactic similarity of the sentence-to-sentence and sentence-to-query. (3) Expanding the words in both the query and sentences to tackle the fundamental problem of term mismatch between sentences and the user's query. Furthermore, in order to reduce redundant Hadith texts, the proposed method uses the greedy algorithm to impose diversity penalty on the sentences. The experimental results display that the proposed method is able to improve performance compared with the existing methods on Hadith datasets.
Effective rating prediction based on selective contextual information

Many researchers have realized the importance of contextual information and focused on designing systems that predict user's contextual preferences. In this respect, several researches have been devoted to Context-Aware Recommender Systems (CARS). One of the remaining issues in these systems especially the collaborative filtering based ones, is determining which contextual information can be adopted to make effective rating prediction. In fact, many contextual dimensions (e.g., location, time, mood etc.) may affect the user's preferences, but not all of these dimensions are equally important for the rating prediction effectiveness. Many existing CARS approaches cannot fully capture the influence of relevant contextual dimensions and their interaction on the rating, and furthermore cannot obtain a better recommendation performance. To address these issues, we highlight contextual dimensions weighting, study the correlation between them to elicit the most useful ones, and propose two improved rating prediction methods based on collaborative filtering techniques, involving relevant and dependent contextual dimensions. Experimental results, with respect to rating prediction quality and recommendation performance on both public available and large created contextual datasets, show that our proposal outperforms the existing recommender systems especially on the created datasets.
Deep Neural Networks for fatty liver ultrasound images classification

Depth learning has been applied extensively in various fields of computer vision in recent year. Although a CNN-based network structure can obtain the ideal results in many image recognition, it is rarely used to classify the ultrasonic images of the fatty liver. This is principally because the fatty liver ultrasonic image has no obvious texture features and the low resolution. In this paper, we design the network structure for the characteristics of B-mode ultrasonic images, and utilize the CNN-based model to classify fatty liver ultrasound images. The experimental results show that we achieve a satisfactory classification effect through applying the proposed CNN network and this method is better than the traditional method for classifying fatty liver ultrasonic images.
Multi-Frame Pyramid Refinement Network for Video Frame Interpolation

Video frame interpolation aims at synthesizing new video frames in-between existing frames to generate higher frame rate video. Current methods usually use two adjacent frames to generate intermediate frames, but sometimes fail to handle challenges like large motion, occlusion, and motion blur. This paper proposes a multi-frame pyramid refinement network to effectively use spatio-temporal information contained in multiple frames (more than two). There are three technical contributions in the proposed network. First, a special coarse-to-fine framework is proposed to refine optical flows in-between multiple frames with residual flows at each pyramid level. Therefore, large motion and occlusion can be effectively estimated. Second, a 3D U-net feature extractor is used to excavate spatio-temporal context and restore texture, which tend to disappear at course pyramid levels. Third, a multi-step perceptual loss is adopted to preserve more details in intermediate frame. It is worth mentioning that our approach can be easily extended to multi-frame interpolation. Our network is trained end-to-end using more than 80K collected frame groups (25 frames per group). Experimental results on several independent datasets show that our approach can effectively handle challenging cases, and perform consistently better than other state-of-the-art methods.
ABFL: An autoencoder based practical approach for software fault localization

Fault localization is essential to software debugging. Despite existing techniques, such as mutation analysis, development history and bug reports, have made great contributions to fault localization, the challenge of infeasibility still exits in practice due to expense of mutation analysis, lacking of development history and bug reports. To improve accuracy and feasibility in fault code locating, in this paper, we propose ABFL, an Autoencoder Based practical approach for Fault Localization. ABFL first introduces an autoencoder to extract 32 features from software static source code. Then it employs Spectrum Based Fault Localization (SBFL) techniques to calculate 14 types of scores, which are taken as another group of features in software running time. Finally, relying on the constructed ranking model, ABFL integrates two groups of features together and precisely locates faulty statements in code. The executed extensive experiments on the Defects4J repository show that our approach is superior to the state-of-the-art SBFL techniques, ranking the faulty statement at the 1st, 3rd, and 5th positions with 49, 94, and 123 faults, respectively.
Performance Sensing Data Prediction for an Aircraft Auxiliary Power Unit Using the Optimized Extreme Learning Machine

The aircraft auxiliary power unit (APU) is responsible for environmental control in the cabin and the main engines starting the aircraft. The prediction of its performance sensing data is significant for condition-based maintenance. As a complex system, its performance sensing data have a typically nonlinear feature. In order to monitor this process, a model with strong nonlinear fitting ability needs to be formulated. A neural network has advantages of solving a nonlinear problem. Compared with the traditional back propagation neural network algorithm, an extreme learning machine (ELM) has features of a faster learning speed and better generalization performance. To enhance the training of the neural network with a back propagation algorithm, an ELM is employed to predict the performance sensing data of the APU in this study. However, the randomly generated weights and thresholds of the ELM often may result in unstable prediction results. To address this problem, a restricted Boltzmann machine (RBM) is utilized to optimize the ELM. In this way, a stable performance parameter prediction model of the APU can be obtained and better performance parameter prediction results can be achieved. The proposed method is evaluated by the real APU sensing data of China Southern Airlines Company Limited Shenyang Maintenance Base. Experimental results show that the optimized ELM with an RBM is more stable and can obtain more accurate prediction results.
RISKEE: A Risk-Tree Based Method for Assessing Risk in Cyber Security

In this paper, the RISKEE method for evaluating risk in cyber security is described. RISKEE is based on attack graphs and the Diamond model combined with the FAIR method for assessing and calculating risk. It can be used to determine the risks of cyber-security attacks as a basis for decision-making. It works by forwarding estimations of attack frequencies and probabilities over an attack graph, calculating the risk at impact nodes with Monte-Carlo simulation, and propagating the resulting risk backward again. The method can be applied throughout all development phases and even be refined at runtime of a system. It involves system analysts, cyber security experts as well as domain experts for judgement of the attack frequencies, system vulnerabilities, and loss magnitudes.
An Investigation of Surrogate Models for Efficient Performance-Based Decoding of 3D Point Clouds

Abstract This work investigates surrogate modeling techniques for learning to approximate a computationally expensive function evaluation of 3D models. While in the past, 3D point clouds have been a data format that is too high dimensional for surrogate modeling, by leveraging advances in 3D object autoencoding neural networks, these point clouds can be mapped to a one-dimensional latent space. This leads to the fundamental research question: what surrogate modeling technique is most suitable for learning relationships between the 3D geometric features of the objects captured in the encoded latent vector and the physical phenomena captured in the evaluation software? Radial basis functions (RBFs), Kriging, and shallow 1D analogs of popular deep 2D image classification neural networks are investigated in this work. We find the nonintuitive result that departing from neural networks to decode latent representations of 3D objects into performance predictions is far more efficient than using a neural network decoder. In test cases using datasets of aircraft and watercraft 3D models, the non-neural network surrogate models achieve comparable accuracy to the neural network models. We find that an RBF surrogate model is able to approximate the lift and drag coefficients of 234 aircraft models with a mean absolute error of 1.97 × 10−3 and trains in only 3 seconds. Furthermore, the RBF surrogate model is able to rank a set of designs with an average percentile error of less than 8%. In comparison, a 1D ResNet achieves an average absolute error of 1.35 × 103 in 38 min for the same test case. We validate the comparable accuracy of the four techniques through a test case involving 214 3D watercraft models, but we also find that the distribution of the performance values of the data, in particular the presence of many outliers, has a significant negative impact on accuracy. These results contradict a common perception of neural networks as an efficient “one-size-fits-all” solution for learning black-box functions and suggests that even within systems that utilize multiple neural networks, potentially more efficient alternatives should be considered for each network in the system. Depending on the required accuracy of the application, this surrogate modeling approach could be used to approximate an expensive simulation software, or if the tolerance for error is low, it serves as a first pass which can narrow down the number of candidate designs to be analyzed more thoroughly.
Structured light imaging for breast-conserving surgery, part II: texture analysis and classification

Subdiffuse spatial frequency domain imaging (sd-SFDI) data of 42 freshly excised, bread-loafed tumor resections from breast-conserving surgery (BCS) were evaluated using texture analysis and a machine learning framework for tissue classification. Resections contained 56 regions of interest (RoIs) determined by expert histopathological analysis. RoIs were coregistered with sd-SFDI data and sampled into ∼4 × 4 mm2 subimage samples of confirmed and homogeneous histological categories. Sd-SFDI reflectance textures were analyzed using gray-level co-occurrence matrix pixel statistics, image primitives, and power spectral density curve parameters. Texture metrics exhibited statistical significance (p-value < 0.05) between three benign and three malignant tissue subtypes. Pairs of benign and malignant subtypes underwent texture-based, binary classification with correlation-based feature selection. Classification performance was evaluated using fivefold cross-validation and feature grid searching. Classification using subdiffuse, monochromatic reflectance (illumination spatial frequency of fx = 1.37 mm − 1, optical wavelength of λ = 490 nm) achieved accuracies ranging from 0.55 (95% CI: 0.41 to 0.69) to 0.95 (95% CI: 0.90 to 1.00) depending on the benign–malignant diagnosis pair. Texture analysis of sd-SFDI data maintains the spatial context within images, is free of light transport model assumptions, and may provide an alternative, computationally efficient approach for wide field-of-view (cm2) BCS tumor margin assessment relative to pixel-based optical scatter or color properties alone.
A Novel Combination of Distributed Ledger Technologies on Internet of Things: Use Case on Precision Agriculture

Internet-of-Things (IoT) is an enabling technology for numerous initiatives worldwide such as manufacturing, smart cities, precision agriculture, and eHealth. The massive field data aggregation of distributed administered IoT devices allows new insights and actionable information for dynamic intelligent decision-making. In such distributed environments, data integrity, referring to reliability and consistency, is deemed insufficient and requires immediate facilitation. In this article, we introduce a distributed ledger (DLT)-based system for ensuring IoT data integrity which securely processes the aggregated field data. Its uniqueness lies in the embedded use of IOTA’s ledger, called “The Tangle”, used to transmit and store the data. Our approach shifts from a cloud-centric IoT system, where the Super nodes simply aggregate and push data to the cloud, to a node-centric system, where each Super node owns the data pushed in a distributed and decentralized database (i.e., the Tangle). The backend serves as a consumer of data and a provider of additional resources, such as administration panel, analytics, data marketplace, etc. The proposed implementation is highly modularand constitutes a significant contribution to the Open Source communities, regarding blockchain and IoT.
DLT / Blockchain in Transactive Energy Use Cases Segmentation and Standardization Framework

Transactive Energy Systems are designed by taking advantage of the advanced control and economic operational functionalities to dynamically balance the electrical demand and supply within the electrical grid using advanced information and communication technologies. Distributed Ledger Technology (DLT), in particular blockchain, is considered one of the promising emerging technologies which is likely to transform the future business and social consumer behavior in several industrial segments. In this paper we present blockchain in Energy use cases segmentation and the associated standardization framework activities which are carried out by IEEE Standards Association (SA).
Generative Adversarial Networks Capabilities for Super-Resolution Reconstruction of Weather Radar Echo Images

Improving the resolution of degraded radar echo images of weather radar systems can aid severe weather forecasting and disaster prevention. Previous approaches to this problem include classical super-resolution (SR) algorithms such as iterative back-projection (IBP) and a recent nonlocal self-similarity sparse representation (NSSR) that exploits the data redundancy of radar echo data, etc. However, since radar echoes tend to have rich edge information and contour textures, the textural detail in the reconstructed echoes of traditional approaches is typically absent. Inspired by the recent advances of faster and deeper neural networks, especially the generative adversarial networks (GAN), which are capable of pushing SR solutions to the natural image manifold, we propose using GAN to tackle the problem of weather radar echo super-resolution to achieve better reconstruction performance (measured in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)). Using authentic weather radar echo data, we present the experimental results and compare its reconstruction performance with the above-mentioned methods. The experimental results showed that the GAN-based method is capable of generating perceptually superior solutions while achieving higher PSNR/SSIM results.
Predicting Intentions from Motion: The Subject-Adversarial Adaptation Approach

Abstract This paper aims at investigating the action prediction problem from a pure kinematic perspective. Specifically, we address the problem of recognizing future actions, indeed human intentions, underlying a same initial (and apparently unrelated) motor act. This study is inspired by neuroscientific findings asserting that motor acts at the very onset are embedding information about the intention with which are performed, even when different intentions originate from a same class of movements. To demonstrate this claim in computational and empirical terms, we designed an ad hoc experiment and built a new 3D and 2D dataset where, in both training and testing, we analyze a same class of grasping movements underlying different intentions. We investigate how much the intention discriminants generalize across subjects, discovering that each subject tends to affect the prediction by his/her own bias. Inspired by the domain adaptation problem, we propose to interpret each subject as a domain, leading to a novel subject adversarial paradigm. The proposed approach favorably copes with our new problem, boosting the considered baseline features encoding 2D and 3D information and which do not exploit the subject information.
Constrained deep networks: Lagrangian optimization via Log-barrier extensions

This study investigates imposing hard inequality constraints on the outputs of convolutional neural networks (CNN) during training. Several recent works showed that the theoretical and practical advantages of Lagrangian optimization over simple penalties do not materialize in practice when dealing with modern CNNs involving millions of parameters. Therefore, constrained CNNs are typically handled with penalties. We propose log-barrier extensions, which approximate Lagrangian optimization of constrained-CNN problems with a sequence of unconstrained losses. Unlike standard interior-point and log-barrier methods, our formulation does not need an initial feasible solution. The proposed extension yields an upper bound on the duality gap-generalizing the result of standard log-barriers-and yielding sub-optimality certificates for feasible solutions. While sub-optimality is not guaranteed for non-convex problems, this result shows that log-barrier extensions are a principled way to approximate Lagrangian optimization for constrained CNNs via implicit dual variables. We report weakly supervised image segmentation experiments, with various constraints, showing that our formulation outperforms substantially the existing constrained-CNN methods, in terms of accuracy, constraint satisfaction and training stability, more so when dealing with a large number of constraints.
Security modelling and assessment of modern networks using time independent Graphical Security Models

Graphical Security Models (GSMs), such as an Attack Graph, are used to assess the security of networks, but they are often limited to assess the security of the given network state (i.e., a snapshot at the current time). To address this issue, we develop a GSM named Time-independent Hierarchical Attack Representation Model (TI-HARM), which analyses the security of multiple network states combined taking into account the time duration of each network state and the visibility of the network components (e.g., hosts and edges). Also, we develop a new security rating system for dynamic networks to evaluate the changing security posture. Lastly, we present an approach that utilises the functionalities of the TI-HARM to compute global optimal defence solutions for dynamic networks. Our experimental results show that the TI-HARM can model and analyse the security of multiple states of dynamic networks, which the existing GSMs mostly assumed that it is static. Also, we found that the TI-HARM can be used to effectively compute the global optimal security solutions compared to existing models that only focus on local optimal solutions. Therefore, our proposed approach could be used to aid security administrators to understand the security posture of dynamic networks better and be able to enhance the security taking into account multiple changes in dynamic networks.
SoftMod

We present SoftMod, a novel modular electronics kit consisting of soft and flexible modules that snap together. Unlike existing modular kits, SoftMod tracks the topology of interconnected modules and supports basic plug-and-play behavior as well as advanced user-specified behavior. As such, the shape of a SoftMod assembly does not depend on the desired behavior and various 2D and 3D electronic systems can be realized. While the plug-and-play nature of our modules stimulates play, the advanced features for specifying behavior and for making a variety of soft and flexible shapes, offer a high-ceiling when experimenting with novel types of interfaces, such as wearables, and interactive skin and textiles.
Inflation and financial sector development in Sub-Saharan African countries

Purpose The purpose of this paper is to examine the link between inflation and the financial sector performance in Sub-Saharan African (SSA) countries. Design/methodology/approach The study analyzes the relationship between inflation and the financial sector performance for selected 22 Sub-Saharan countries from 1980 to 2013. The study used panel data and the dynamic panel generalized method of moments econometric method. The study concentrates on the link between inflation and the development of the banking sector. Findings The findings suggest that inflation does not promote financial sector development in SSA region while trade openness has a positive impact on the selected financial development indicators. Other variables that enhance financial development in SSA include government expenditure and good governance. Practical implications The main policy implication of the study is that in order for SSA countries to benefit from a deeper and more active financial sectors, the rates of inflation must be maintained low and be consistently under control. Also, for SSA region financial sectors to become deeper and more active it is crucial to develop stronger economic institutions including independent central banks and sound fiscal authorities. Originality/value The study differs from previous studies as it includes more (22) countries from SSA region while previous studies were either regional or country specific. The study also incorporates trade openness and the role of institutional quality in enhancing financial development. This differentiates the study from previous studies on the subject from the region.