Reviewerly/exHarmony huggingface dataset:
360 Panorama Synthesis from a Sparse Set of Images with Unknown Field of View

360° images represent scenes captured in all possible viewing directions and enable viewers to navigate freely around the scene thereby providing an immersive experience. Conversely, conventional images represent scenes in a single viewing direction with a small or limited field of view (FOV). As a result, only certain parts of the scenes are observed, and valuable information about the surroundings is lost. In this paper, a learning-based approach that reconstructs the scene in 360° × 180°from a sparse set of conventional images (typically 4 images) is proposed. The proposed approach first estimates the FOV of input images relative to the panorama. The estimated FOV is then used as the prior for synthesizing a high-resolution 360°panoramic output. The proposed method overcomes the difficulty of learning-based approach in synthesizing high resolution images (up to 512×1024). Experimental results demonstrate that the proposed method produces 360° panorama with reasonable quality. Results also show that the proposed method outperforms the alternative method and can be generalized for non-panoramic scenes and images captured by a smartphone camera.
Power Optimisation Model for Leveraging Cloud System

Cloud computing is an emerging technology that has significantly increased over the years due to an increase in the number of internet users and its numerous benefits. It is a technology that leverages cost and considers locational challenges. The emergence of cloud computing has brought innovation and hope to many under-developed countries with less skilled workforce, limited resources and unstable electricity supply. Although this technology is transforming information technology, it still faces some significant challenges like high energy consumption rate, confusion in adoption method and security issues. The focus of this paper is on reducing the problems with ambiguity on data center structure and its power usage by simplifying how the application works and then provide a holistic approach to minimizing it power usage rate. This paper minimizes the energy utilization of the cloud data center using an intelligent method to shut down, schedule and monitors the entire cloud system. Additionally, observing the complexity of the cloud system design, a holistic approach to calculating and measuring the percise amount of power usage during each operational cycle is vital to achieving a more efficient data centre energy model. This work proposes an improved energy model that calculates, in detail, the energy usage on the system by focusing on each system component and its contribution to the high energy consumption level of the data centre. Furthermore, the proposed model is then used to develop an algorithm that minimises the power usage in the cloud environment to an optimal low level while maintaining high level of service level agreement. The result obtained from this experiment shows that the developed model removed a high level of obscurity in the issue of the cause of high energy consumption in the data center and thereby encourage cloud providers to act accordingly in reducing the effect.
A Weight Initialization Method Associated with Samples for Deep Feedforward Neural Network

Artificial neural network is an important force to promote the development of artificial intelligence, but it usually needs to be trained before use. The initial weight given randomly is the most widely used method when training neural networks. However, randomly given initial weights are independent of the samples. A weight initialization method associated with samples for deep feedforward neural network (DFFNN) is proposed. The initial weights set by this method are a combination of the original weights given randomly and the weights obtained by the first epoch training. The initial weights not only have random characteristics, but are also closely related to the samples to be trained. The proposed method is tested with the bearing data provided by the Case Western Reserve University (CWRU) Bearing Data Center. The testing results show that the proposed method can accelerate the training of DFFNN to some extent.
A Multilingual ASR of Sepedi-English Code-Switched Speech for Automatic Language Identification

This paper presents an integration of multilingual speech recognition into language identification (LID) for code-switched speech using phonotatic features as language information. A multilingual speech recognition system converts the spoken utterances into occurrences of phone sequences. The hidden Markov models (HMMs) are employed to build a multilingual acoustic models that can handle multiple languages within an utterance. We propose two phoneme clustering methods to determine the phoneme similarities among the target languages. A supervised machine learning technique is employed to learn the language transition of the phonotactic information given the phoneme sequences. The classification decision is made by support vector machines (SVM) technique which classifies language identity given the likelihood scores based on the phoneme occurrence segments. We experiments were performed using a mixed language speech corpus for Sepedi and English. We evaluate the ASR-LID system measuring the performance of the phone error rate (PER) and the LID classification accuracy portions separately. We obtained a lower PER on a system that employed data-driven phoneme clustering method which was modelled with 32-Gaussian mixtures per state. The proposed multilingual ASR-LID framework has achieved an acceptable recognition and classification accuracy on code-switched and monolingual speech respectively.
Algorithm and the Program of Construction of the Fuzzy Logical Model

The paper considers the algorithm and the program for constructing a fuzzy logic model, since solving the problem of intellectual analysis is characterized by a lack of essential information and data in a linguistic form. In such cases it is advisable to apply theories of fuzzy sets and Z-numbers. On the basis of the constructed algorithm and the developed software package, a number of test model problems of estimation, prediction and classification.
Comparative Study of Chaos-Based Robust Digital Image Watermarking Techniques

This paper contrasts the implementation of several chaos-based digital image watermarking techniques in terms of imperceptibility, robustness, security and complexity. Most spatial and transform domains methods have been used namely Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Redundant Discrete Wavelet Transform (RDWT) with Singular Value Decomposition (SVD) mathematical tool which has enhanced embedding capability. Both continuous and discrete chaotic maps namely Arnold's Chaotic Map (ACM), Logistic Chaotic map (LCM) and Lorenz Chaotic Map (LoCM) are included to result in secure schemes. Exhaustive series of tests have been performed along with a range of evaluation metrics for imperceptibility evaluation. In addition, Robustness of all the techniques has also been confirmed by applying a variety of attacks on the resulting images. This study will help to adopt the most suitable methods for a given application as sufficient number of test images have been utilized.
An Approach Towards Encrypting Paired Digits Using Dynamic Programming and Diffie-Hellman Key Exchange

Cryptography has gone through tremendous evolution in recent years. But the concept of enciphering text was started in 1900 BC. There have been many modifications to older cryptography as well as profound discoveries have been made in this field. Likewise, this paper proposes a cryptographic technique that uses the idea of the Diffie-Hellman key Exchange to share a common key. This key-value behaves as a seed value in the formation of key sequences. A methodology has been proposed to encrypt paired digits using dynamic programming. Based on the shared common key, a table is generated on both sides of the sender and receiver to have a common data set. This data set helps in encryption and decryption both. The dynamic table uses the Fibonacci series and recursive functions to fill the table. Decryption needs searching operation in the receiver’s table which would search for (x, y) pair. This (x, y) pair is concatenated to produce plain text.
Overlap Sampler for Region-Based Object Detection

The top accuracy of object detection to date is led by region-based approaches, where the per-region stage is responsible for recognizing proposals generated by the region proposal network. In that stage, sampling heuristics (e.g., OHEM, IoU-balanced sampling) is always applied to select a part of examples during training. But nowadays, existing samplers ignore the overlaps among examples, which may result in some low-quality predictions preserved. To mitigate the issue, we propose Overlap Sampler that selects examples according to the overlaps among examples, which enables the training to focus on the important examples. Benefitted from it, the Faster R-CNN could obtain impressively 1.5 points higher Average Precision (AP) on the challenging COCO benchmark, a state-of-the-art result among existing samplers for region-based detectors. Moreover, the proposed sampler also yields considerable improvements for the instance segmentation task. Our code is released at https://github.com/ChenJoya/overlap-sampler.
Assessment of Heavy Metal Contamination in the Soil of Al-Muthanna Storage Site using Geospatial techniques

During the early 90s, military operations and United Nations Special Commission "UNSCOM" teams have destroyed the Iraqi past chemical program. Both operations led to a large number of scattered remnants of contaminated areas. The quantities of hazardous materials, incompletely destructed materials and toxic chemicals were sealed in two bunkers. Lack of appropriate destruction technology led to the spread of contamination around the storage area. This paper aims to introduce the environmental assessment of the hazard contamination in the storage site area using Geomatics analysis technique. The environmental assessment for contamination level of eight main chemical compounds (Chromium Cr, Phosphate PO4, Nitrate NO3, Cadmium Cd, Copper Cu, Nickel Ni, Zinc Zn and Lead Pb) have been assessed and analyzed. Soil samples taken from the grid on the site and its surrounding areas have been investigated and analyzed compared to the reference points. The storage area grid was divided into 30 major sectors. 10 samples from each sector were acquired and evaluated. The assessment results indicated that Cr level exceeded the permitted level by (180) times, PO4 level exceeded the permitted level by (35) times, Cu by (1050) times over the permitted level and Ni level exceeds the permitted level over by (50) times. Activity level of Zn in the soil samples of the study area also exhibits large variability with (16000) times over the permitted level on the surrounding isolation protection on the north-west Wall. Very high Contamination spot activity of Pb was found in Destruction zone, about (1350) times over the background level. There is no activity detected for the Cadmium and Nitrate in the storage area site.
A hybird self-learning method based on particle swarm optimization and salp swarm algorithm

This paper presents a novel self-learning hybrid optimization algorithm based on the particle swarm optimization (PSO) algorithm and the salp swarm algorithm (SSA) algorithm, namely HSL-PSO-SSA, for solving the function optimization problems. In HSL-PSO-SSA, three search strategies based on the ideas of PSO and SSA are adopted and a probability model is designed to determine the probability of a search strategy being used to update an individual in the search population. The performance of the HSL-PSO-SSA is investigated on solving the unimodal and multimodal benchmark functions. From the experimental results, it is observed that the proposed HSL-PSO-SSA outperforms the compared algorithms including the standard PSO and the original SSA.
Humanitarian makerspaces in crisis-affected communities

Abstract There is an increasing demand for humanitarian aid around the world. At the same time, the number of makerspaces has been growing exponentially. Recently, the humanitarian sector has become interested in how these new design spaces can help crisis-affected populations. Despite the emergence of humanitarian makerspaces, there is little research to date that documents their outcomes and impacts. A multi-case study approach is taken to analyze three makerspaces that support migrants, refugees, and asylum seekers in Greece. A maturity grid tool is used to show that humanitarian makerspaces are driving impact in six key areas. The study underlines how these makerspaces support different design activities and have different outcomes. It also considers the challenges which are preventing humanitarian makerspaces from achieving their ultimate goals, drawing attention to the need for an enabling ecosystem in both the local and humanitarian context. This research brings clarity to the poorly understood phenomenon of humanitarian makerspaces and highlights the important role of design in humanitarian interventions. It also reveals practical insights for humanitarian organizations who are considering setting up makerspaces in crisis-affected communities.
Offline Handwritten Devanagari Character Identification

Handwritten character identification is a most torrid area of research where countless researchers have presented their work and is still an area less than research to accomplish higher identification accuracy. In earlier period acquisition, storing and exchanging information in type of handwritten script was the well-situated way and is still widespread as a convenient medium in the era of digital equipment. As advanced technology like tablet has been used and many comparable devices that allows humans to key in data in form of handwriting character. Manuscript is written by the use of paper and then converting to an image via scanner, identify handwritten characters as of the image is well-known as off-line handwritten character identification is a demanding work due to the fact that each author will have diverse style of writing and all scripts have their own character set and complexities to write.
Structural and Morphological Quantitative 3D Characterisation of Ammonium Nitrate Prills by X-Ray Computed Tomography

The mixture of ammonium nitrate (AN) prills and fuel oil (FO), usually referred to as ANFO, is extensively used in the mining industry as a bulk explosive. One of the major performance predictors of ANFO mixtures is the fuel oil retention, which is itself governed by the complex pore structure of the AN prills. In this study, we present how X-ray computed tomography (XCT), and the associated advanced data processing workflow, can be used to fully characterise the structure and morphology of AN prills. We show that structural parameters such as volume fraction of the different phases and morphological parameters such as specific surface area and shape factor can be reliably extracted from the XCT data, and that there is a good agreement with the measured oil retention values. Importantly, oil retention measurements (qualifying the efficiency of ANFO as explosives) correlate well with the specific surface area determined by XCT. XCT can therefore be employed non-destructively; it can accurately evaluate and characterise porosity in ammonium nitrate prills, and even predict their efficiency.
First-order sensitivity of the optimal value in a Markov decision model with respect to deviations in the transition probability function

Abstract Markov decision models (MDM) used in practical applications are most often less complex than the underlying ‘true’ MDM. The reduction of model complexity is performed for several reasons. However, it is obviously of interest to know what kind of model reduction is reasonable (in regard to the optimal value) and what kind is not. In this article we propose a way how to address this question. We introduce a sort of derivative of the optimal value as a function of the transition probabilities, which can be used to measure the (first-order) sensitivity of the optimal value w.r.t. changes in the transition probabilities. ‘Differentiability’ is obtained for a fairly broad class of MDMs, and the ‘derivative’ is specified explicitly. Our theoretical findings are illustrated by means of optimization problems in inventory control and mathematical finance.
Steganalysis of Very Low Embedded JPEG Image in Spatial and Transform Domain Steganographic Scheme Using SVM

Steganalysis recognizes the manifestation of a hidden message in an artefact. In this paper, the analysis is done statistically, by extracting features that shows a change during an embedding. Machine-learning approach is employed here by using a classifier to identify the stego image and cover image. SVM is used as a classifier and a comparative study is done by using steganographic schemes from spatial plus transform domain. The two steganographic schemes are LSB matching and F5 Six unlike kernel functions, four diverse samplings are used for classification. In this paper, the percentage embedding is kept as low as 10%.
Mapping Out Bitcoin’s Pseudonymous actors

The Bitcoin system is a pseudo-anonymous payment system and cryptocurrency, which can separate the virtual Bitcoin users' identities from any real-world identities. In this work, we developed a methodology on how to extract information about the real-world users behind Bitcoin transactions from the circulating data in the network. We focus on determining information about Bitcoin users ' physical location by examining users' spending habits. The extracted features are trained by a customized un-supervised clustering algorithm that combines K-Means and DBSCAN algorithms. The results show that over 70% of high valued transaction belong to gambling services, while almost 15% of them belongs to mining pools and exchanges services. Also, we notice that 7% of the discovered antonymous systems (AS) contained only one single Bitcoin node.
Quasi-Orthogonal Sparse Superposition Codes

We introduce a new family of error correction codes based on sparse superposition called Quasi- Orthogonal} Sparse Superposition Codes (QO-SSC). The core of QO-SSC is a special structure of the SSC projection matrix which is obtained by concatenation of orthogonal submatrices. Concrete examples of QO-SCC constructions based on Zadoff- Chu sequences and Kerdock bent sequences are provided. A low-complexity iterative successive interference cancellation algorithm is proposed for QO-SSC decoding. Performance evaluations show that QO-SSC is an efficient solution for short- packet transmission at low signal-to-noise ratios, as it achieves lower block error rate than conventional coded modulation schemes, e.g., QPSK- modulated polar codes. As a further advantage, QO-SSC features a low PAPR when transmitted using OFDM waveforms.
Rule Extraction From Neuro-fuzzy System for Classification Using Feature Weights

Recent trends in data mining and machine learning focus on knowledge extraction and explanation, to make crucial decisions from data, but data is virtually enormous in size and mostly associated with noise. Neuro-fuzzy systems are most suitable for representing knowledge in a data-driven environment. Many neuro-fuzzy systems were proposed for feature selection and classification; however, they focus on quantitative (accuracy) than qualitative (transparency). Such neuro-fuzzy systems for feature selection and classification include Enhance Neuro-Fuzzy (ENF) and Adaptive Dynamic Clustering Neuro-Fuzzy (ADCNF). Here a neuro-fuzzy system is proposed for feature selection and classification with improved accuracy and transparency. The novelty of the proposed system lies in determining a significant number of linguistic features for each input and in suggesting a compelling order of classification rules using the importance of input feature and the certainty of the rules. The performance of the proposed system is tested with 8 benchmark datasets. 10-fold cross-validation is used to compare the accuracy of the systems. Other performance measures such as false positive rate, precision, recall, f-measure, Matthews correlation coefficient and Nauck's index are also used for comparing the systems. It is observed from the experimental results that the proposed system is superior to the existing neuro-fuzzy systems.
EPPS: Efficient Privacy-Preserving Scheme in Distributed Deep Learning

As a promising training model with Neural Network, distributed deep learning has been widely applied in various scenarios, where clients and the cloud server work together only by sharing local gradients and global parameters. However, research has shown that the adversary can still reconstruct the users' private information even if little information is leaked. To address this problem, several approaches of privacy-preserving distributed training have been exploited with existing mature technologies, such as Differential Privacy, Secure Multi-party Computation and Homomorphic Encryption. However, state of-the-art results are still defective in security, functionality and efficiency. In this paper, we propose an Efficient Privacy Preserving Scheme (EPPS) for distributed deep learning. We claim that our solution achieves the best performance tradeoff between security, efficiency and functionality. Specifically, we adopt the threshold Paillier encryption as the underlying structure to construct our secure training model. Hence, the confidentiality of honest users' of local gradients can be guaranteed, even the cloud server colluding with multiple users. In addition, since users are often accidentally offline due to either network environment or equipment damage, our EPPS can also support users exiting at any phases of the entire work process. Further more, we conducted extensive experiments on real-world data to demonstrate the preferable performance of our proposed scheme.
A case study on modularity violations in cyber‐physical systems

Abstract In recent years, cyber‐physical systems (CPS) have been widely used in different problem domains. The hardware and software components in a CPS are deeply intertwined at various levels of abstraction under changing contexts to achieve the desired goals. One way to manage this complexity is through the use of modular architecture that enables portions of a CPS to be upgraded, replaced, or fixed in a plug‐and‐play manner. In practice, nominally modular architectures depart from this ideal. Thus, there is a need to identify, measure, and understand the causes of these departures. Techniques have been developed to accomplish this for pure software systems, but can these techniques be adapted to infer hardware‐related modularity violations in a CPS? To investigate this question, we adapted methods from pure‐software systems and analyzed two open‐source CPS: OpenWrt and MD PnP. We found that the software architecture of these systems is well modularized. However, we were able to detect and measure modularity violations associated with hardware and other domain‐related concepts. Furthermore, software components related to hardware were more likely to change frequently compared to general software components. Together, these findings suggest that it is feasible to identify a subset of hardware‐driven modularity violations using techniques adapted from pure software systems.