Reviewerly/exHarmony huggingface dataset:
A Research-Oriented Mathematical Model for Cloud Simulations

Cloud computing draws immense attraction of the researchers worldwide. It has now become a base paradigm of almost every growing organization. To carry out fruitful research, rigorous experimentation is always required. Commercially, large-scale cloud data centers consist of tens of thousands of physical servers to fulfil the users’ requests across the globe. Therefore, it is not feasible for every researcher to conduct the research experiments and test their proposed algorithms/methods/frameworks over the real cloud environment. Alternately, simulation experiments can be performed. To perform the simulation experiments, standard and research-oriented mathematical modelling of the cloud architecture is required which can be easily implemented using any of the programming languages. To this requirement, this article discusses such a mathematical model of cloud computing architecture consisting of different cloud entities. The discussed model can be easily implemented using any programming language or simulator to perform the research experiments.
Deep Learning-Based Modulation Classification of Communication Signals

The 5G paves the way for deploying several devices like sensors, the Internet of things (IoT), and machine-to-machine communication. This deployment is gaining momentum very rapidly compared to previous technologies due to the availability of a compact and improved system, due to which now several systems/devices are increasingly interconnected day by day. Therefore, the availability of precise radio frequency environment information is ever more challenging in such scenarios. To perceive such scenarios, we proposed the classification of modulation types of the synthetic signal using a deep neural network. A set of eleven different modulation-type classifications are selected for analysis of the raw data. Moreover, to further increase the overall accuracy of the classification, the datasets are optimized using preprocessing techniques.
Malice-Aware Transaction Forwarding in Payment Channel Networks

Scalability has long been a major challenge of cryptocurrency systems, which is mainly caused by the delay in reaching consensus when processing transactions on-chain. As an effective mitigation approach, the payment channel networks (PCN) enable private channels among blockchain nodes to process transactions off-chain, relieving long-time waiting for the online transaction confirmation. The state-of-the-art studies of PCN focus on improving the efficiency and availability via optimizing routing, scheduling, and initial deposits, as well as preventing the system from security and privacy attacks. However, the behavioral decision dynamics of blockchain nodes under potential malicious attacks is largely neglected. To fill this gap, we employ game theory to study the characteristics of channel interactions from both the micro and macro perspectives under the situation of channel depletion attacks. Our study is progressive, as we conduct the game-theoretic analysis of node behavioral characteristics from individuals to the whole population of PCN. Our analysis is complementary, since we utilize not only the classic game theory with the complete rationality assumption, but also the evolutionary game theory considering the limited rationality of players to portray the evolution of the PCN. The results of numerous simulation experiments verify the effectiveness of our analysis.
Securing the Integrity of PDF Files using RSA Digital Signature and SHA-3 Hash Function

Signatures are used on documents as written proof that the document was verified by the person indicated. Signature also indicated that the document originated from the signer if the document is transferred to another party. A document maybe in physical print form but may also be a digital print. A digital print requires additional security since a digital document may easily be altered by anyone although the said document is signed using a photographed or scanned signature. One of the means of security is by using the RSA Digital Signature method which is a combination of the RSA algorithm with Digital Signature. RSA algorithm is one of the public key cryptography algorithms, while Digital Signature is a security scheme which may guarantee the authenticity, non-repudiation, and integrity of a file by means of a hash function. This research implemented a web-based combination of RSA Digital Signature with SHA-3 hash function to secure the integrity of PDF files using PHP programming language. The result is a web-based system which could guarantee the authenticity, non repudiation and integrity of PDF files. Testing were carried out on six different sizes of PDF files ranging from 6 KB, up to 23285 KB on three different web browsers: Google Chrome, Microsoft Edge, and Mozilla Firefox. Average processing times of signing and verifying on each browsers were 1.3309 seconds, 1.2565 seconds, and 1.2667 seconds.
An experience in the use of SCRUM and KANBAN for project development in a Waterfall environment

SIDIA R&D Institute is responsible for developing, validating, and localizing the Android Operating System (Android OS) used in smart devices, such as smartphones and tablets, sold in Latin American countries. Traditionally, SIDIA’s Model department is responsible for such development, and it is comprised of smaller teams working on individual Android components. For example, the Application team works on the Android’s System Apps layer, such as Photo Gallery and Phone Dialer. Like all Model’s teams, the Application employs the Waterfall methodology, a straightforward and widespread software development framework. However, the Application team can receive requests to work on projects unrelated to its primary activities. In these cases, the Waterfall methodology is unsuitable because it follows a linear pipeline [1], making it challenging to work on multiple and simultaneous projects. Hence, the present paper aims to report the experience of the Application team in transitioning from a pure Waterfall environment to a mix of Waterfall and Agile methodologies. Specifically, the team applied the SCRUM and KANBAN methodologies in four independent projects. The challenges caused by this culture change and the COVID-19 pandemic, which forced our workforce to work from home, are reported here. Furthermore, the team used Retrospective and Post-Mortem meetings to gather feedback from members about their experience with these new methodologies.
Discrete Fourier Transform with Neural Networks

The discrete Fourier transform is an important computational tool to retrieve the frequency distribution of a sampled signal. Recent years have also witnessed considerable research activity in neural networks as a mean to solve various signal processing problems. Despite this, it has been an open issue whether a neural network can be trained to return the discrete Fourier transform of its inputs. This paper presents a training methodology for neural networks with non-linear activation functions to replicate and approximate the discrete Fourier transform.
Journalists’ moves in political press conferences and their implications for accountability

Abstract Political press conferences are important spaces for public accountability because they give journalists the opportunity to scrutinize politicians’ decisions. However, the structure of press conferences poses specific constraints to journalists because their role is limited to ask questions. This situation is not problematic if their goal is to ask informative or critical questions, but it becomes problematic if journalists want to advance standpoints, arguments, or criticisms. In the latter case, journalists have to perform their argumentative moves through façade questions in order to comply with the protocol of press conferences. For this reason, it is not easy to distinguish the argumentative function of journalists’ questions, and consequently, their value for accountability. This paper draws on the pragma-dialectical theory of argumentation to give an argumentative account of political press conferences. Furthermore, the implications of journalists’ questions for accountability purposes are discussed.
Semantic Segmentation of Outcrop Images using Deep Learning Networks Toward Realization of Carbon Capture and Storage

This study was conducted to classify outcrop images using semantic segmentation methods based on deep learning algorithms. Carbon capture and storage (CCS) is an epoch-making approach to reduce greenhouse gases in the atmosphere. This study specifically examines outcrops because geological layer measurements can lead to production of a highly accurate geological model for feasible CCS inspections. Using a digital monocular RGB camera, we obtained 13 outcrop images annotated with four classes along with strata. Subsequently, we compared segmentation accuracies with changing input image sizes of three types and semantic segmentation methods of four backbones: SegNet, U-Net, ResNet-18, and Xception-65. The ResNet-18 and Xception-65 backbones were implemented using DeepLabv3+. Experimentally obtained results demonstrated that data expansion with random sampling improved the accuracy. Regarding evaluation metrics, global accuracy and local accuracy are higher than the mean intersection over union (mIoU) for our outcrop image dataset with unequal numbers of pixels in the respective classes. These experimentally obtained results revealed that resizing for input images is unnecessary for our method.
Cold-start Recommendation Method Based on Homomorphic Encryption

Nowadays, recommendation systems are widely used, and it is a very difficult issue to solve the cold-start problem. Recent studies shows that the introduction of social network relationships can alleviate the cold-start problem of the recommendation system. However, some of these algorithms have higher requirements for implementation scenarios, and some cannot guarantee user information security. In our study, we propose a matrix factorization recommendation system method based on homomorphic encryption and social network. The method introduces the sum of preference information of cold-start user neighbors as prior knowledge into the recommendation system to solved the problem of insufficient information for cold start users. In addition, the method uses Pallier homomorphic encryption algorithm to ensure the security of user information and improve computational efficiency. Experiments on three real-world data sets shows that the method has produced a significant improvement in the prediction effect of cold-start users.
The Use of Augmented Reality in Collaboration Within the Construction Industry

Abstract Site visitation is a crucial aspect of civil engineers’ job scope as it provides them with first-hand construction data. However, due to the recent COVID-19 pandemic, construction site visitations are restricted, making the tasks of a civil engineer impossible to achieve. The main objective of this research is to find out and suggest a remedy to the problems commonly faced by civil engineers through the help of Augmented Reality (AR). Hence, the study’s objective is to analyse AR’s functionalities and suggest its best application to communicate spacious awareness via visual feedback for civil engineers. For maximum usability, an integrated software suite is proposed that provides access to AR worksites, measured data, and allows peer communication. This addressed the whole spectrum of problems faced by civil engineers with the switch to remote working. However, with the COVID-19 pandemic, the limitation of this project is that testing could not be executed to determine the efficacy of the said solution. Furthermore, the proposed solution has limitations, such as requiring pre-constructed infrastructure in applicable construction sites. Therefore, regular quality assurance (QA) and quality control (QC) are required to ensure proper functionality. In the future, AR LiDAR sensors could be a possibility as it provides increased accuracy of AR measurements which is critical in engineering.
Investigating the Generalization of Image Classifiers with Augmented Test Sets

Adding prior knowledge about the task or domain being learned can greatly facilitate learning. Two of the most common examples of injecting prior knowledge into Deep Learning systems are architecture design and data augmentation. For example, the convolutional architecture biases the model to learn local features. This emphasis on local features has been a useful prior for image processing. Data augmentation is full of examples that utilize prior knowledge. For example, cropping an image and preserving the original label gives the inductive bias of local feature importance. In this study, we aim to see how the priors in architecture interplay with augmentations. We begin by showing the overall benefit of training with data augmentation, improving the Vision Transformer’s test accuracy from 74.4% to 84.3% and improving the ResNet’s test accuracy from 79.3% to 86.7%. We focus on the distinction between global and local priors such as the difference between the convolution and attention layers and cropping versus noise addition augmentations. These tests are not yet able to find complementing flaws in architectures and augmentations. We find that neither the ResNet or the Vision Transformer is robust to distribution shifts controlled with data augmentation. The performance of both models degrades heavily even with moderate augmentation strengths. Although remedied by explicitly training with the augmentation used to construct the test set, we still see a notable decrease in performance. This study illustrates the utility of generalization testing with data augmentation and the challenge of measuring the impact of global and local priors in architecture.
Toward a novel engine for compiler optimization space exploration of big data workloads

Abstract Recently, big data specific technologies have been emerging, including domain‐specific languages , software frameworks , databases , third‐party libraries , and so forth. These techniques are successful in concealing the low‐level details by producing high‐level code, which is passed through the conventional compilation cycle for generating hardware operable code. Several optimization opportunities exist in the compiler which can assist in meeting the processing deadlines of big data workloads, through optimized machine code. However, the existing iterative compilation techniques are not enough for the exploration of big data applications. In this regard, a novel engine has been presented for exploiting the compiler optimization space of big data workloads. The engine is comprised of training and testing phases. During the training stage, the big data application is optimized with Mitigates the Compiler Phase‐ordering (MiCOMP) and genetic algorithm (GA) optimization sequences, which are executed with train datasets. In the testing stage, the test datasets are executed only for the best 300 optimization sequences discovered at the training stage. The proposed engine has been tested with graph mining , machine learning , and text search categories of big data applications using a wide range of real‐world and synthetic datasets. Overall, the engine is , , and faster than Iterative Optimization for the Data Center (IODC), MiCOMP, and GA respectively in exploiting the compiler search space for big data workloads. Further, the integration of best‐10 and best‐3 techniques with the engine brings a speedup of and . The compiler level exploitation of general‐purpose machines incurs no extra overhead, no heavy computing, and no personnel cost. Also, the overall performance of big data specialized software solutions can be enhanced by compiling their high‐level code with suitable compiler optimizations.
Less entanglement exhibiting more nonlocality with noisy measurements

Abstract The Clauser–Horne–Shimony–Holt (CHSH) inequality test is widely used as a mean of invalidating the local deterministic theories. Most attempts to experimentally test nonlocality have presumed unphysical idealizations that do not hold in real experiments, namely, noiseless measurements. We demonstrate an experimental violation of the CHSH inequality that is free of idealization and rules out local models with high confidence. We show that the CHSH inequality can always be violated for any nonzero noise parameter of the measurement. Intriguingly, less entanglement exhibits more nonlocality in the CHSH test with noisy measurements. Furthermore, we theoretically propose and experimentally demonstrate how the CHSH test with noisy measurements can be used to detect weak entanglement on two-qubit states. Our results offer a deeper insight into the relation between entanglement and nonlocality.
An Online Clustering Approach for Evolving Data-Stream Based on Data Point Density

Density-based clustering has been widely studied by many researchers due to its popularity in generating arbitrary shaped clusters in data-stream. However, the majority of them are offline, sliding windowed or online-offline hybridization and cannot be applied to data-stream applications. This paper presents a fully online density-based clustering approach namely OCED (Online Clustering for Evolving Data-stream). The algorithm summarizes the data points in a sphere-shaped data structure called a micro-cluster. The initial radius of micro-cluster is learned and updated towards its optimal value based on the average data point density. A cluster graph is generated in an online manner using the micro-clusters to provide the arbitrary shaped cluster. The micro-clusters, which are irrelevant to recent data-stream content, are identified and removed to handle the evolving property. To the best of our knowledge, this is the first clustering approach that learns the radius instead of predefining. The approach is evaluated in a wide range of experiments. OCED proves its superiority over aligned approaches in terms of computational speed, cluster quality, and memory efficiency.
An Analysis of the Use of Feed-Forward Sub-Modules for Transformer-Based Image Captioning Tasks

In the quest to make deep learning systems more capable, a number of more complex, more computationally expensive and memory intensive algorithms have been proposed. This switchover glosses over the capabilities of many of the simpler systems or modules within them to adequately address current and future problems. This has led to some of the deep learning research being inaccessible to researchers who don’t possess top-of-the-line hardware. The use of simple feed forward networks has not been explicitly explored in the current transformer-based vision-language field. In this paper, we use a series of feed-forward layers to encode image features, and caption embeddings, alleviating some of the effects of the computational complexities that accompany the use of the self-attention mechanism and limit its application in long sequence task scenarios. We demonstrate that a decoder does not require masking for conditional short sequence generation where the task is not only dependent on the previously generated sequence, but another input such as image features. We perform an empirical and qualitative analysis of the use of linear transforms in place of self-attention layers in vision-language models, and obtain competitive results on the MSCOCO dataset. Our best feed-forward model obtains average scores of over 90% of the current state-of-the-art pre-trained Oscar model in the conventional image captioning metrics. We also demonstrate that the proposed models take less time training and use less memory at larger batch sizes and longer sequence lengths.
A Comparative Study of Credentialed Vulnerability Scanning and Non-credentialed Vulnerability Scanning

Vulnerability scanning is a common technique to detect security problems in computer systems. The scanning methods are divided into credentialed vulnerability scanning and non-credentialed vulnerability scanning. However, the relation and the difference between the results of two scanning types remain unclear in practice. In this paper, we use Nessus to scan 67 common hosts using the two methods, and perform a comparative analysis of the scan results. Our study shows that the results of the credentialed scans cover the results of the non-credentialed scans in most cases. Also, the difference between the scan results vary accordingly to the hosts’ security level, and the variation presents linearity in general.
Facial Image Recognition in Distributed Machine Learning Using Rich Clients

Most of current research into edge computing involves using devices only for sensing and communicating data. In contrast, by offloading machine learning on the device that constitutes the source of the data, one can construct a system with better privacy protection and reduced communication cost. We therefore consider an edge computing model in which rich clients also participate in machine learning. Experiments conducted using facial images confirmed the benefits of the proposed model in terms of privacy and communication costs.
‘I must first apologise’. Advance-fee scam letters as manifestos

Advance-fee scam letters are email messages designed to engage the recipient in a fraudulent business partnership involving an initial investment on his/her part for the sake of making a large profit. Scam letters have sparked the interest of both ‘scam-busters’ and artists who use them as raw material in various artforms – music, films and installations. As they pass from one type of discourse and genre to another, and leave the Internet to become art, advance-fee scam letters cease to be just spam and become manifestary discourse. Neither loud and shouting, nor explicit and programmatic, they engage the reader/spectator in dialogue. In Joana Hadjithomas and Khalil Joreige’s video installation, ‘A Letter can always Reach its Destination’ (2012) they become a collective summons, via an embodied appeal to the spectator, engaging the spectator’s emotions and encouraging her to think critically and ethically about people’s destinies in developing countries. By tracing the history of both the manifesto and the advance-fee scam letter, this article shows their point of convergence. By this, it responds to Martin Puchner’s quest for a new type of hybrid manifesto and appeases Julian Rosefeldt’s doubts about the power of art to reach multiple audiences.
Simulation of dual-function speckle interferometry for the measurement of out-of-plane displacement and slope

This paper proposes a dual-function speckle interferometry system based on the temporal phase-shifting technique for both the digital speckle pattern interferometry (DSPI) and digital shearography (DS) measurement. The principle of the dual-function system has been described comprehensively based on the scalar diffraction theory. The simulation results show that the dual-function system which is based on the object beam multiplexing can simultaneously measure the out-of-plane displacement and slope under the single loading. The sensitivity of the out-of-plane displacement solved by the DSPI and DS method is discussed. Due to the error of the integration, the accuracy of the out-of-plane displacement calculation in DSPI is better than in DS. Finally, the dual-function speckle interferometry system has been proven effective in the edge detection of internal defects.