Reviewerly/exHarmony huggingface dataset:
Towards Automated Logging for Forensic-Ready Software Systems

Security incidents can arise from the misuse of existing software systems. Thus, appropriate logging mechanisms should be implemented at the software level to support the detection and investigation of security incidents. However, due to insufficient logging, security incidents often go undetected for long periods. Moreover, even after a security incident is detected, there is not enough information to fully reconstruct how an incident occurred. Insufficient logging may be due to the limited security expertise of software developers, who may not know what are the most critical security incidents. Also, for large software systems and a multitude of potential misuse scenarios, it is cumbersome to identify when and what logging instructions should be implemented. In this paper, we propose a preliminary idea to automate the development of "forensic-ready" software systems. These systems can log a minimum amount of relevant data that can be used to detect and investigate potential security incidents. Our approach allows a security engineer to elicit a set of potential software misuse scenarios, expressed as annotated sequence diagrams. These diagrams are then used-together with a control flow graph of the software system- to identify the exact location where logging instructions should be placed and the information they should log. Finally, logging instructions can be injected into designated software system locations using Aspect-Oriented Programming. We illustrate our approach using an example of software misuse in a human resources management software system.
BFAGC: A Bias-Free Adaptive Gamma Correction Method for Image Enhancement

One of the most interesting and visually appealing areas of image processing is image enhancement. In order to enhance the images, various methods have already been introduced. But, these existing enhancement methods are hardly able to enhance all types of images. To address this issue, Adaptive Gamma Correction (AGC) method has been introduced which classifies images into several classes based on the statistical information of the image and then applies an adaptive gamma correction method. However, AGC method can be affected by outlier pixels and fails to appropriately enhance the images. To solve this problem, we propose an improved version of Adaptive Gamma Correction (AGC) namely Bias-Free Adaptive Gamma Correction (BFAGC). The proposed BFAGC is rarely affected by the outlier pixels. The extensive experimental analysis has been performed to evaluate the efficacy of the proposed BFAGC method. Both the qualitative and quantitative evaluation metrics have shown that BFAGC produces comparatively better enhanced images than AGC and other existing state-of-the-art methods.
A CNN accelerator on embedded FPGA using dynamic reconfigurable coprocessor

Convolutional neural network (CNN) has been widely deployed in deep learning networks at present. However, numerous convolution operations are computing intensive and often require powerful accelerator such as FPGA. The existed accelerators usually as IO devices communicate with CPU by data bus such as AMBA which has large latency relatively. Moreover, different network models such as AlexNet, VGG, SSD and YOLO have different operators. As a result, implementing a unified CNN accelerator on FPGA has challenges such as the requirement of reconfigurable resource on FPGA. In this paper, a new approach for CNN accelerator implementation is proposed which leverages coprocessor mechanism and partial reconfiguration to realize accelerator dynamic reconfiguration online. By this way, each network model can have a unique accelerator and the accelerator can be loaded into FPGA online when used that can reduce resource requirement. Because accelerator is a coprocessor of host CPU, the accelerator can communicate with CPU by coprocessor interface that can reduce communication latency. A prototype system that includes an ARM1136 softcore microprocessor and an AlexNet accelerator is implemented on FPGA. In this prototype system, the accelerator as a coprocessor can be loaded into FPGA online by partial reconfigure and each CNN operation such as conv2d, max pool is a coprocessor instruction. The experimental results show that the accelerator can recognize each picture from MINIST database of handwritten digits and achieve 1.85x speedup compared with AMBA bus by coprocessor interface.
AMAD

Anomaly detection is facing with emerging challenges in many important industry domains, such as cyber security and online recommendation and advertising. The recent trend in these areas calls for anomaly detection on time-evolving data with high-dimensional categorical features without labeled samples. Also, there is an increasing demand for identifying and monitoring irregular patterns at multiple resolutions. In this work, we propose a unified end-to-end approach to solve these challenges by combining the advantages of Adversarial Autoencoder and Recurrent Neural Network. The model learns data representations cross different scales with attention mechanisms, on which an enhanced two-resolution anomaly detector is developed for both instances and data blocks. Extensive experiments are performed over three types of datasets to demonstrate the efficacy of our method and its superiority over the state-of-art approaches.
A Novel Chroma Processing Scheme for Improved Color Accuracy of HDR Video Content

Our eyes do not perceive all colors the same way, being sensitive to changes in some colors more than others. In this work, by taking advantage of this characteristic of the human eye, we propose a chroma processing scheme that reduces the color errors generated by the quantization and subsampling processes in the HDR video delivery pipeline. To this end, our method uses the de-correlated color space of IC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> and re-distributes chroma code-words in favor of those colors that our eyes are most sensitive to. Performance evaluations have shown that our proposed chroma processing can improve color accuracy of the tested HDR video content by an average of 18.6% and 26.2% in terms of color accuracy compared to that of the 10-bit YC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> for the 4:4:4 and 4:2:0 formats, respectively. Additionally, subjective evaluations showed that the proposed chroma processing scheme does not negatively affect the overall compression performance while it preserves colors through the quantization and subsampling processes much more accurately than existing methods.
Applying Deep Learning for the Detection of Abnormalities in Mammograms

Medical imaging producesWessels, Steven massive amounts of data.Van der Haar, Dustin Computer aided diagnosis (CAD) systems that use traditional machine learning algorithms to derive insights from the data provided in the medical industry struggle to perform at a competent level regarding sensitivity and false positive minimization. This paper looks at some of the current methods used to improve CAD systems in the domain of forming breast cancer diagnosis with mammograms. This paper presents deep learning models that use Convolutional Neural Networks (CNN) to identify abnormalities in mammographic studies that can be used as a tool for the diagnosis of breast cancer. We run two experimental cases on two public mammogram databases, namely MIAS and the DDSM. Firstly, the abnormality severity was classified. Secondly, the combination of abnormality type and its severity were compared in multi-label classification. Two CNN architectures, namely miniature versions of VGGNet and GoogLeNet, were also compared. We were able to achieve a best AUC of 0.85 for the classification of abnormality severity on the DDSM data set and a best Hamming loss of 0.27 on the MIAS data set for the multi-label classification task.
A Bangla Spell Checking Technique to Facilitate Error Correction in Text Entry Environment

Spell checker is a common tool used in different text entry techniques for error free writing. Recent advances in spell checking techniques in different languages significantly decreases word and sentence level typos from writing text. But developing an optimized spell checker in Bangla language is still a great research challenge due to the complex rules of Bangla spelling. To overcome the challenge we propose a new technique of spell checking considering both word and sentence level errors to facilitate the text correction techniques of Bangla language. We use a hybrid approach by combining edit distance algorithm and probability based N-gram language model for the error detection and correction tasks. A large corpus is built containing almost 0.5 million words and 50,000 n-gram sentences to detect and correct the errors. We evaluate the performance of our proposed method with different test data collected from online sources. Again, the efficiency of proposed technique is estimated by conducting a pilot study involving users of different ages. The accuracy rate of our proposed spell checker is approximately 97%. The validity of our claims have been proved through comparison with other Bangla spell checkers.
Multi-resolution wavelet analysis for noise reduction in impulse puncture voltage measurements

This work describes an approach for digital processing of voltage impulse signals in puncture tests of insulators on high voltage laboratories, using wavelet transform based multi-resolution analysis. Quantifying the amplitude of such impulses is not a trivial task because they have typical magnitudes of hundreds of kilovolts and rise times of 100 ns to 200 ns. The processing is mainly necessary due to high frequency electromagnetic interferences caused by the impulse generation system, which may strongly affect the measurement accuracy and, consequently, the test result. This manuscript presents qualitative and quantitative results for different types of wavelets studied, as well as the best performance obtained by the filter. Software for signal processing and analysis was also developed for this application. Results are also presented using the wavelet filter for a pioneering inter-laboratory comparison between high voltage measuring systems used in impulse puncture tests of insulators.
Belief Expansion in Subset Models

Subset models provide a new semantics for justifcation logic. The main idea of subset models is that evidence terms are interpreted as sets of possible worlds. A term then justifies a formula if that formula is true in each world of the interpretation of the term. In this paper, we introduce a belief expansion operator for subset models. We study the main properties of the resulting logic as well as the differences to a previous (symbolic) approach to belief expansion in justification logic.
The Effect of Eye Movements in Response to Different Types of Scenes Using a Graph-Based Visual Saliency Algorithm

Saliency is the quality of an object that makes it stands out from neighbouring items and grabs viewer attention. Regarding image processing, it refers to the pixel or group of pixels that stand out in an image or a video clip and capture the attention of the viewer. Our eye movements are usually guided by saliency while inspecting a scene. Rapid detection of emotive stimuli an ability possessed by humans. Visual objects in a scene are also emotionally salient. As different images and clips can elicit different emotional responses in a viewer such as happiness or sadness, there is a need to measure these emotions along with visual saliency. This study was conducted to determine whether the existing available visual saliency models can also measure emotional saliency. A classical Graph-Based Visual Saliency (GBVS) model is used in the study. Results show that there is low saliency or salient features in sad movies with at least a significant difference of 0.05 between happy and sad videos as well as a large mean difference of 76.57 and 57.0, hence making these videos less emotionally salient. However, overall visual content does not capture emotional salience. The applied Graph-Based Visual Saliency model notably identified happy emotions but could not analyze sad emotions.
Mineralogy as a proxy to characterise geochemical dispersion processes: A study from the Eromanga Basin over the Prominent Hill IOCG deposit, South Australia

The Early Cretaceous interbedded sandstone, siltstone and mudstone of the Cadna-owie Formation and the reduced mudstone of the Bulldog Shale overlying the Prominent Hill IOCG deposit in South Australia (total tonnage: 140 Mt. @ 1.2% Cu and 0.5 g/t Au; OZ Minerals Limited 2017) are shown to preserve Cu concentrations up to 5043 ppm Cu. These cover sequence materials are approximately 100 m thick over the basement rocks that host the deposit. This study investigates the source of elevated Cu in the cover material by assessing the Cu deportment in these formations and focuses on understanding element migration processes resulting in development of elevated Cu concentrations. Petrographic and QEMSCAN® analysis show that Cu resides within sulphides including high-temperature Cu-sulphides (e.g. bornite) in the Cadna-owie Formation. The Cu-sulphides are associated with Fe-oxides (magnetite) and minerals including pyroxene, amphibole, garnet, chlorite and plagioclase that are interpreted to be detrital and are likely to be of metamorphic origin. Based on these observations, lateral physical processes driven by sedimentary dynamics are interpreted to be the main processes responsible for elevated Cu concentrations in the Cadna-owie Formation in the Prominent Hill area. Chemical migration processes likely had only localised influence and resulted in minor Cu redistribution. QEMSCAN® data collected on samples from the weathering profile of the Bulldog Shale shows that weathering processes (e.g. pyrite oxidation, serpentinisation and feldspar hydrolysis) mostly affect the upper Bulldog Shale and that Eh-pH conditions gradually change with depth from the oxidised and acidic upper Bulldog Shale to the reduced and slightly acidic lower Bulldog Shale. Copper speciation and Eh-pH conditions are used to interpret downward chemical migration of Cu due to weathering as the main process for development of elevated Cu concentrations within the weathered Bulldog Shale in the Prominent Hill area. Upward chemical and lateral physical migration processes may have influenced Cu concentrations within the unweathered Bulldog Shale, however the degree of influence is yet to be determined. Overall this study shows that elevated Cu concentrations in the Early Cretaceous cover sequences overlying the Prominent Hill deposit can be linked back to mineralisation itself and that processes resulting in elevated Cu concentrations include chemical and physical migration.
Use of Virtual Reality in Participatory Design

Virtual Reality technology was used as a visualization tool in a participatory design process of an urban design project in Santiago, Chile.Community members with little or no familiarity with digital technologies visualized the project using traditional means of architectural representation and immersive virtual reality, and then, answered standardized questionnaires on spatial comprehension.Participants who visualized the project using virtual reality showed a higher level of spatial comprehension and a more precise understanding of the formal and spatial qualities of the project than users who used traditional means.
Efficient and Privacy-Preserving Edit Distance Query Over Encrypted Genomic Data

Genomic data have seen tremendous growth recently due to the advancement in DNA sequencing technologies and are being generated at an ever-higher velocity. The similarity between two genomic data records can be measured by the edit distance, which has significant applications in disease diagnosis and treatment. At the same time, due to the limited storage and computational resources at the local data owner, a typical solution for the edit distance computation is to outsource the genomic data to a powerful cloud. However, as the genomic data are sensitive and the cloud server is not fully trusted, there are privacy considerations during the edit distance computation. Apart from data privacy, efficiency also needs to be taken into consideration. In order to deal with the privacy and efficiency issues, in this paper, we propose an efficient and privacy-preserving edit distance computation scheme for a single data owner and single cloud server scenario. In specific, we first design an index technique to reduce the computational cost and communication overhead of the genomic data outsourcing, and introduce a fast edit distance computation technique to speed up the edit distance query. Then, we propose an efficient and privacy-preserving edit distance computation scheme by deploying the homomorphic encryption technique, which can well preserve the private information including genomic data records and edit distances. Besides, security analysis shows that the proposed scheme is privacy-preserving and performance evaluation validates the efficiency of the proposed scheme.
Haptic Learning Towards Neural-Network-based adaptive Cobot Path-Planning for unstructured spaces

Collaborative Robots, or Cobots, bring new possibilities for human-machine interaction within the fabrication process, allowing each actor to contribute with their specific capabilities.However creative interaction brings unexpected changes, obstacles, complexities and non-linearities which are encountered in real time and cannot be predicted in advance.This paper presents an experimental methodology for robotic path planning using Machine Learning.The focus of this methodology is obstacle avoidance.A neural network is deployed, providing a relationship between the robot's pose and its surroundings, thus allowing for motion planning and obstacle avoidance, directly integrated within the design environment.The method is demonstrated through a series of case-studies.The method combines haptic teaching with machine learning to create a task specific dataset, giving the robot the ability to adapt to obstacles without being explicitly programmed at every instruction.This opens the door to shifting to robotic applications for construction in unstructured environments, where adapting to the singularities of the workspace, its occupants and activities presents an important computational hurdle today.
Multichannel Crossed Convolutional Neural Network for Combined Estimation of Cortical Thickness and Bulk Velocities Using Ultrasonic Guided Waves: A Simulation Study

Ultrasonic guided waves (UGW) propagated in the long cortical bone can be measured via axial transmission method. However, ultrasonic identification of long cortical bone is a multi-parameter inverse problem and the optimal solution of the inverse problem often involves complex solving process. Deep neural network is essentially a multi-parameter powerful predictor based on universal approximation theorem. In the study, we investigate the feasibility of applying the multichannel crossed convolutional neural network (MCC-CNN) for simultaneous estimation of cortical thickness and bulk velocities. Finite-difference time-domain method (FDTD) is used to obtain the simulated UGW signals. The results illustrated that the proposed method confirms the feasibility and accuracy, which could be helpful to the UGW based evaluation of long cortical bone.
Trends in Agile: Business Agility

The agile conference (http://www.agilealliance.org/agile2019/) is the largest global symposium on agile software development, catering particularly to practitioners. Agile 2019, held 5-9 August in Washington, D.C., drew more than 2,400 participants.
Platypus: Offchain Protocol Without Synchrony

Offchain protocols aim at bypassing the scalability and privacy limitations of classic blockchains by allowing a subset of participants to execute multiple transactions outside the blockchain. While existing solutions like payment networks and factories depend on a complex routing protocol, other solutions simply require participants to build a childchain, a secondary blockchain where their transactions are privately executed. Unfortunately, all childchain solutions assume either synchrony or a trusted execution environment. In this paper we present Platypus, an offchain protocol that requires neither synchony nor a trusted execution environment. Relieving the need for a trusted execution environment allows Platypus to ensure privacy without trusting a central authority, like Intel, that manufactures dedicated hardware chipset, like SGX. Relieving the need for synchrony means that no attacker can steal coins by leveraging clock drifts or message delays to lure timelocks. In order to prove our algorithm correct, we formalize the chilchain problem as a Byzantine variant of the classic Atomic Commit problem, where closing an offchain protocol is equivalent to committing the whole set of payments previously recorded on the childchain “atomically” on the main chain. Platypus is resilience optimal and we explain how to generalize it to crosschain payments.
Attaining classical capacity per unit cost of noisy bosonic Gaussian channels

I show that classical capacity per unit cost of noisy bosonic Gaussian channels can be attained by employing generalized on-off keying modulation format and a projective measurement of individual output states. This means that neither complicated collective measurements nor phase-sensitive detection is required to communicate over optical channels at the ultimate limit imposed by laws of quantum mechanics in the limit of low average cost.
Mathematical Methods Applied for Authorship Attribution on the Phonological Level

The proposed combination of statistical methods has proved efficient for authorship attribution. The complex analysis method based on the proposed combination of statistical methods has made it possible to minimize the number of phoneme groups by which the authorial differentiation of texts has been done.
A Novel Multi-Sensor Image Matching Algorithm Based on Adaptive Multiscale Structure Orientation

Automatic and reliable multi-sensor image matching is a very challenging task due to the significant nonlinear radiometric differences between multi-sensor images.In this paper, a novel dense descriptor based on adaptive multiscale structure orientation is proposed for capturing the geometrical structure information of an image.The dense descriptor of the proposed matching algorithm is not only illumination and contrast invariant but also robust against the image noise.Further, an improved similarity measurement is introduced for adapting the orientation reversal caused by the intensity inversion between multi-sensor images.Based on the robust dense descriptor and the improved similarity measurement, we developed a novel and practical template matching algorithm to match multi-sensor images reliably.We evaluate the proposed matching algorithm by comparing it with other state-of-the-art algorithms.The experimental results show the proposed algorithm has a significant advantage on matching accuracy.
Eigenstructure and fractionalization of the quaternion discrete Fourier transform

Quaternion signal processing has found significant application to color image processing and bivariate signal analysis. Among the studies on quaternion transforms, just a few deal with fractionalization and none does so from an eigenstructure analysis point of view. This work aims to contribute by defining a fractional quaternion discrete Fourier transform (with one or multiple parameters) out of its eigendecomposition, after proving that it shares eigenvectors with the usual unitary discrete Fourier transform. For illustrative purposes, an encryption scheme for color images with opacity layer is provided and discussed.