Reviewerly/exHarmony huggingface dataset:
Fuzzy-based Human Factor Centered Cybersecurity Risk Assessment

With the rise of IT systems, cyber-attacks are becoming not only more intense and complex, but also more sophisticated. The source of threats can be multiple, including hardware, software, physical and human factors. Yet, it can be stated that the weakest link is the human element. For this reason the aim of this study to develop a risk assessment model which is able to predict the risk level taking into account the characteristics of the company. In the proposed hierarchical model, fuzzy-based subsystems are used to handle the inherent uncertainties and subjectivity in the data and in the evaluation process.
Development of an Underwater Simulator using Unity3D and Robot Operating System

We developed an Open-Source Underwater Simulator that uses Unity3D for the simulating Remotely Operated Vehicle (ROV) operations and Robot Operating System (ROS) as the middleware to control the simulated vehicle. This simulator provides a virtual environment in which a vehicle can be simulated with a multi-beam SONAR and interact with features such as hilly terrain, lake, and pipelines. The vehicle is fitted with sensors and cameras to continuously monitor its state. The end-user is presented with an interactive user interface, which allows them to control various elements of the simulation. The application connects with ROS over a WebSocket to receive commands and send back parameters, video feed, and SONAR data, allowing the user to map the vehicle’s environment in 3 dimensions using a mapping library. The simulator is tested with an adaptive lawn-mower type planner to display its full capabilities.
Generating Diverse Back-Translations via Constraint Random Decoding

Back-translation has been proven to be an effective data augmentation method that translates target monolingual data into source-side to improve the performance of Neural Machine Translation (NMT), especially in low-resource scenarios. Previous researches show that diversity of the synthetic source sentences is essential for back-translation. However, the frequently used random methods such as sampling or noised beam search, although can output diverse back-translations, often generate noisy synthetic sentences. To alleviate this problem, we propose a simple but effective constraint random decoding method for back-translation. The proposed method is based on an automatic post-editing (APE) data augment framework, which incorporates fluency boost learning. Moreover, to increase the diversity of synthetic data and ensure quality, we proposed to use an evolution decoding algorithm. Compared with the original back-translation, our method can generate more diverse while less noisy synthetic sentences. The experimental results show that the proposed method can get 0.6 BLEU improvements on the WMT18 EN-DE news dataset and more than 0.4 BLEU improvements on the EN-ZH dataset which is in the medical field, respectively.
Optimal dynamic balancing of a hybrid serial-parallel robotic manipulator through bio-inspired computing

One of the most challenging robotic manipulator designs is finding an appropriate balance between the shaking force and shaking moment because this reduces vibrations. Several approaches have been introduced in the last decades; nevertheless, some assumptions must be established to make such a balance. In this paper, a dynamic balancing approach is proposed. The main novelty is the no dependence on specific trajectories to be executed by the manipulator, which allows finding a design with a similar tradeoff in the balancing under robot configuration changes. Also, the proposal incorporates mass distribution and link shape in a single design procedure. The proposal is stated as a constrained nonlinear optimization problem and applied to a hybrid serial-parallel robotic manipulator. The use of different bio-inspired algorithms and one gradient one in the solution of the balancing problem reveals that differential evolution finds the most suitable design. Besides, comparative simulation results of the obtained design with other design approaches show that the obtained design presents the most suitable tradeoff between the shaking force and the shaking moment when the manipulator executes tasks with different operating velocities.
Examining the impact of adaptive convolution on natural language understanding

Convolutional neural networks (CNNs) have shown promising results on many natural language understanding (NLU) tasks owing to their ability to capture informative features in local patches. However, they extract informative patterns in a static manner; they use the same set of filters regardless of different inputs. In this article, we propose an adaptive convolution to provide greater flexibility to traditional CNNs. Unlike the traditional convolution, the adaptive convolution utilizes adaptively generated convolution filters which are conditioned on inputs. We achieve this by attaching filter-generating networks, which are carefully designed to generate input-specific filters, to each convolution block in existing CNNs. We show the efficacy of our approach for existing CNNs through extensive performance evaluations. Our results indicate that adaptive convolutions improve all the baselines, without any exception, by as much as 2.6 percentage points (%p) on sentiment analysis, 1.6 %p on text classification, and 3.6 %p on textual entailment.
Single-exposure modulation-based structured illumination microscopy using spatial area phase-shift

In structured illumination microscopy (SIM) for three-dimensional (3D) measurement, the vertical scanning position with the maximum modulation is implemented to reshape the surface topography. Generally, to obtain the modulation distribution for each scanning position, the modulation decoding algorithms mainly include temporal phase-shifting (TP) method and frequency analysis technique. In TP method, at least three images with a fixed phase-shift are required, which results in low efficiency and complex phase-shifting mechanism. Frequency analysis technique can realize modulation distribution from single image, but the high frequency information of the sample will be inevitably lost. In this paper, we propose a spatial area phase-shifting reconstruction method which requires only one exposure to decode the modulation distribution. In this technique, n pixels from one period of illuminated pattern are used to replace the corresponding n images in the temporal phase shift. Through combining the phase information of each pixel with the least square algorithm, the modulation distribution is achieved. The proposed method can achieve an accurate 3D reconstruction. Also, the high frequency information of the sample can be effectively retained compared with frequency analysis method. The simulations and experiments are conducted to verify the feasibility of this method, demonstrating the potential to be applied in high precision, complex surface and real-time measurement.
Spam Email Detection Using Machine Learning and Neural Networks

Spam emails are junk emails which are unrequested deceptive emails sent or forwarded to any person or a company which may contain malware and has access to confidential information of any individual. A lot of research work has been done in this area of spam detection which is limited to some specific domains. Machine learning is generally used to classify whether an email is valid (ham) or unwanted (spam). Two feature sets are introduced namely stopwords and word count to determine an email is spam or ham on the basis of textual information and fields of an email file. The entire process involves the comparison of two different feature sets on Multinomial Naïve Bayes, Logistic Regression, Linear Support Vector Machine, and Artificial Neural Network Algorithms to determine a more reliable method for spam detection. For this purpose, we use benchmark datasets as well as real time evaluation to experimentally evaluate the proposed work. Detection of a spam email on basis of content, malware, and sender’s information can reduce the threat to user’s confidential information to a great extent.
A new image encryption algorithm using a bit shifted logistic map

This paper proposes a new Image Encryption Algorithm (IEA) using a new bit shifted logistic map. The Bit Shifted Logistic Map (BSLM) is used for the generation of pseudo-random numbers for the further encryption/decryption process. The BSLM is verified using a bifurcation diagrams and Lyapunov exponents. The IEA is ensured to the achieve two basic cryptographic properties - confusion and diffusion. The IEA using the BSLM is further verified using the common security analysis methods like visual analysis, histogram plots, and randomness studies. Results have been compared with those reported in previously published papers. The overall simplicity of the methodology enables it to be easily implemented in existing software and hardware infrastructure.
Image Compression using Huffman Coding Scheme with Partial/Piecewise Color Selection

Compression is the specialty of presenting the data in a conservative structure as opposed to its unique or uncompressed structure. Moreover, utilizing information compression, the size of a specific document can be decreased. This is extremely helpful when preparing, putting away, or moving a gigantic document, which needs huge resources. The speed of transmission relies on the number of pieces sent, the time needed for the encoder to create the coded message, and the time needed for the decoder to recoup the primary ensemble. In an information storage application, the level of compression is the essential concern. Compression can be named either lossless or lossy. Lossless compression methods remake the primary data from the compacted record with no loss of information. In this manner, the data does not alter during the decompression and compression measures. These sorts of compression calculations are called reversible compressions since the primary message is recreated by the decompression cycle. This paper analyzes the exhibition of the Huffman Encoding Algorithm, Lempel Zev Welch Algorithm, Arithmetic Encoding Algorithm, Adaptive Huffman Encoding Algorithm, Shannon Fano Algorithm, and Run Length Encoding Algorithm. Specifically, the efficiency of these calculations in compacting text information is compressed and assessed.
Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images

Self-supervised method has proven to be a suitable approach for despeckling on synthetic aperture radar (SAR) images. However, most self-supervised despeckling methods are trained by noisy-noisy image pairs, which are constructed by using natural images with simulated speckle noise, time-series real-world SAR images or generative adversarial network, limiting the practicability of these methods in real-world SAR images. Therefore, in this paper, a novel self-supervised despeckling algorithm with an enhanced U-Net is proposed for real-world SAR images. Firstly, unlike previous self-supervised despeckling works, the noisy-noisy image pairs are generated from real-word SAR images through a novel generation training pairs module, which makes it possible to train deep convolutional neural networks using real-world SAR images. Secondly, an enhanced U-Net is designed to improve the feature extraction and fusion capabilities of the network. Thirdly, a self-supervised training loss function with a regularization loss is proposed to address the difference of target pixel values between neighbors on the original SAR images. Finally, visual and quantitative experiments on simulated and real-world SAR images show that the proposed algorithm notably removes speckle noise with better preserving features, which exceed several state-of-the-art despeckling methods.
Adapting Deep Neural Networks for Pedestrian-Detection to Low-Light Conditions without Re-training

Pedestrian detection is an integral component in many automated surveillance applications. Several state-of-the-art systems exist for pedestrian detection, however most of them are ineffective in low-light conditions. Systems specifically designed for low-light conditions require special equipment, such as depth sensing cameras. However, a lack of large publicly available depth datasets, prevents their use in training deep learning models.In this paper we propose a pre-processing pipeline, which enables any existing normal-light pedestrian detection system to operate in low-light conditions. It is based on a signal-processing and traditional computer-vision techniques, such as the use of signal strength of a depth sensing camera (amplitude images) and robust principal component analysis (RPCA). The information in an amplitude image is less noisy, and is of lower dimension than depth data, marking it computationally inexpensive to process. RPCA processes these amplitude images to generate foreground masks, which represent potential regions of interest. These masks can then be used to rectify the RGB images to increase the contrast between the foreground and background, even in low-light conditions. We show that these rectified RGB images can be used by normal-light deep learning models for pedestrian-detection, without any additional training.To test this hypothesis, we use the ’Oyla Low-Light Pedestrian Benchmark’ (OLPB) dataset. Our results using two state-of-the art deep learning models (CrowdDet and CenterNet) show: a) The deep models perform poorly as pedestrian detectors in low-light conditions; b) Equipping the deep-networks with our pre-processing pipeline significantly improves the average precision for pedestrian-detection of the models without any re-training. Taken together, the results suggest that our approach could act as a useful pre-processor for deep learning models that aren’t specially designed for pedestrian-detection in lowlight conditions.
Towards Integrating Blockchains with Microservice Architecture Using Model-Driven Engineering

Abstract Blockchain presents a feasible method to persist immutable information in a distributed ledger to improve the level of authentication and trust. Moreover, smart contracts enable the automated execution of any contract concluded between participants of the Blockchain network. On the other hand, Microservice Architecture (MSA) is a novel approach towards service-based scalable applications. In our paper, we present an approach based on Model-Driven Engineering (MDE) that aims to facilitate the integration process of Blockchains into MSA-based applications in order to benefit from the advantages attributed to Blockchains.
The Use of Sub-forums in Software Product Forums

Software product forums is a platform filled with user feedback that utilises the sub-forum feature to categorise user discussion into themes. These sub-forums are very similar to classification labels that have been used to automatically classify user feedback on other platforms such as Troubleshooting and Feature Request. It would be very beneficial to the CrowdRE community if these sub-forum categories can be utilised in a research setting as it would reduce the effort required to label content for classification manually. However, no research has been done on the accuracy of these sub-forum categorisations in software product forums. In this exploratory study, we examined the accuracy of user categorised posts in two software product forums and discovered that users incorrectly categorise more than 20% of the posts during submission. Our discovery suggests that at the current stage, sub-forum categories should not be trusted as a label to classify feedback automatically.
Badaslr: Exceptional cases of ASLR aiding exploitation

Address Space Layout Randomization (ASLR) is de-facto standard exploit mitigation in our daily life software. The simplest idea of unpredictably randomizing memory layout significantly raises the bar for memory exploitation due to the additionally required attack primitives such as information leakage. Ironically, although exceptional, there are rare edge cases where ASLR becomes handy for memory exploitation. In this paper, we dig into such theoretical set of cases and name it as BadASLR. Based on our study, we introduce four categories of BadASLR: (i) aiding free chunk reclamation in heap spraying attack, (ii) aiding stack pivoting in frame-pointer null poisoning attack, (iii) reviving the exploitability of invalid pointer referencing bug, and (iv) introducing wild-card ROP gadgets in x86/x64 position independent code environment. To evaluate if BadASLR can be an actual plausible scenario, we look into real-world bug bounty cases, CTF/wargame challenges. Surprisingly, we found multiple vulnerabilities in commercial software where ASLR becomes handy for attacker. With BadASLR cases, we succeeded in exploiting peculiar vulnerabilities, and received total 10,000 USD as bug bounty reward including one CVE assignment.
An Effective Text Steganographic Scheme Based on Multilingual Approach for Secure Data Communication

Nowadays, as communication through different internet mediums is increasing rapidly, information security is a huge concern among people of all backgrounds. Text steganography is the method of ensuring data security by concealing any secret message using cover media in such a way that no naked eyes can identify the modification of cover texts. This paper proposes a Bit-Level Embedding Technique, a variant of text steganography, to hide secret messages in the cover medium by manipulating the white spaces available in a text medium and utilizing various Latin alphabets while assuring its confidentiality not attracting suspicion from its appearance. Finally, several experiments and comparisons have been conducted to demonstrate the superiority of the proposed scheme in terms of embedding capacity, invisibility, robustness, and security that make this a standard text steganographic method. The authors propose a data hiding technique where 5-bit can be embedded per embeddable location, which is very rare in text steganography, and the utilization of multilingual forms of English alphabets gives this technique a superior performance.
A reversible data hiding method for image protection

Encryption is the way toward encoding given information that can't be perceived by an unapproved individual. Securing a message or a picture which is partaken in a social stage be the difficult undertaking these days. This paper proposes an account procedure to steganography by means of reversible room before encryption surface mix. Reversible room before encryption surface mix measure explore supplementary unobtrusive side picture that arranges different facet picture with a similar and relative appearance with optional dimensions. This paper networks the Reversible room before encryption surface association cycle with steganography for covering secrete data. Rather than using contemporary cover photo for covering information our figure seal the source surface picture and introduces coded message via pattern of Reversible room before encryption surface association. This licenses us to isolate the secret messages and source surface from a stego designed surface. This philosophy proffers main three focal points. Most importantly, the positioning provide data embedding furthest extended that is contrast to dimensions of surface of the stego picture. Second, a steganalytic compute not apparently to conquer steganography technique. Third, changeable capacity gained from our arrangement gives value, which grants recovery surface of the source. Exploratory results are guaranteed to provide various consignment of introducing curtailment, produce ostensibly viable surface pictures, and also recuperate fount surface.
Identification of Multiple Copy-move Attacks in Digital Images using FFT and CNN

Copy-move forgery is an image manipulation technique wherein significant elements are added or removed from the image to spread misinformation. These forged images have flooded the internet owing to easily accessible image editing software. Copy-move attacks distort edges around the manipulated elements and thus can be detected by analyzing these edges. Traditional detection methods utilize algorithms like Cosine Transform, Scale-invariant Feature Transform, and Convolution Neural Networks (CNN), which have varying levels of accuracy and fail when there are multiple copy-move attacks in the image. Since edge detection and analysis are critical steps, performing Preliminary Edge Detection on the images proves effective. This study proposes Fast Fourier Transform (FFT) to perform preliminary edge detection on the images used to train the CNN model. The proposed algorithm that combines FFT and CNNs achieves enhanced testing accuracy of 95.91%, low validation loss of 0.252, and performs exceptionally well with multiple copy-move attacks.
An evolutionary ensemble analogy‐based software effort estimation

Abstract Analogy‐based estimation (ABE) is the most commonly applied method for estimating using software. Although there are several analogy‐based estimation techniques, there is no consistent conclusion of which technique is the best in all circumstances. Therefore, this article presents an evolutionary ensemble ABE (EEABE) for software cost estimation. Ensemble effort estimation (EEE) models forecast software development endeavors through using multiple estimation methods. In this article, EEABE combines GA as an evolutionary algorithm with six ABE models. The proposed method has been evaluated on four well‐known datasets comprising Maxwell, Albrecht, Kemerer, and Desharnais, by the k‐fold cross‐validation technique and based on the performance criteria of MRE, MMRE, MDMRE, BMMRE, and PRED(0.25). The simulation results demonstrate that the use of EEABE increases the accuracy of estimation and reduces the cost. Besides, EEABE is a flexible and adaptable model with any type of dataset and software development project, and it has been optimized with the possibility of using various ABE techniques.
Comparison of Hurst exponent estimation methods

Through recent years many researchers have developed methods to estimate the self-similarity and long memory parameter that is best known as the Hurst parameter. In this paper, we set a comparison between nine different methods. Most of them use the deviations slope to find an estimate for the Hurst parameter like Rescaled range (R/S), Aggregate Variance (AV), and Absolute moments (AM), and some depend on filtration technique like Discrete Variations (DV), Variance versus level using wavelets (VVL) and Second-order discrete derivative using wavelets (SODDW) were the comparison set by a simulation study to find the most efficient method through MASE. The results of simulation experiments were shown that the performance of the methods is relatively close, except for the SODDW method was the most efficient in MASE.&#x0D; Paper type Categorize your paper under one of these classifications: General review
Fast convergence for federated learning in OFDMA systems

Device selection is a central issue for federated learning (FL) in a network with limited bandwidth resources, e.g., OFDMA. More participating devices in learning lead to faster convergence but will incur longer communication delays since less bandwidth will be allocated to each device. To balance between device participation and communication delay, Shi et al. [1] propose a joint bandwidth allocation and scheduling problem to achieve fast convergence. But their solution does not guarantee optimality and the computational complexity of the problem remains open. To address this issue, we propose a new algorithm that optimally solves this problem in polynomial-time by casting it into a joint cardinality-constrained device selection and bandwidth allocation problem. Experimental results on non-IID data sets show that our algorithm will attain faster convergence and higher accuracy than existing methods.