Reviewerly/exHarmony huggingface dataset:
Implementation of Traditional Transposition Cipher with Salting Principle

Transposition Cipher was the most often used conventional cipher in the early days because it was resistant to frequency analysis. The purpose of this study is to provide another way of encrypting information through transposition and substitution cipher with salting – which is a common method to hashing. The aim behind this cipher is to keep all the original letters while mixing them and keeping the total number of characters intact. Through series of simulations, the new algorithm is proven to eliminate the repetitive appearance of letters as compared to the existing ones. In addition, this strategy will be more secure when paired with another algorithm to make a hybrid technique.
Qude: Exploring Tactile Code in Long-Distance Relationships

Long-distance relationships (LDRs) have long been of interest in HCI research, and several strategies and designs for mediating intimacy have been proposed. This work-in-progress aims to explore and identify existing practices in experiencing intimacy in technology-mediated LDRs, to design artefacts that could mediate some of the related struggles, and to explore the prototypes with users. Based on the insights from seven in-depth semi-structured interviews and a creative task, we developed the concept of Qude, which entails two prototypes: a vibro-tactile wearable that enables a person to tap and send a rhythm to their partner; and a supporting app, through which users can assign meanings to the rhythms, thus creating an exclusive, tactile code. The paper concludes by reflecting on preliminary findings from four user explorations and the contextual analysis. We provide considerations for further iterations of Qude, as well as a discussion of possibilities for future designs.
Classical Adversarial Attack on mm-Wave FMCW Radar

Deep Learning (DL) based classification of conventional objects using mm-Wave Frequency Modulated Continuous Wave (FMCW) radars is useful for multiple real-world automotive applications. These applications require highly precise and accurate embedded setups to tackle undesirable mishaps. In general adversarial attacks can account for significant degradations in the performance of DL models. Provided a specific hardware setup, the system should be equipped with robust classification algorithms to counter adversarial attacks. The goal of this paper is to present an experimental study regarding the effects of a classical adversarial attack on some of the state-of-the-art DL algorithms. The first phase is to acquire experimental data and construct Range-Angle profile datasets using mm-Wave FMCW radars in real-world situations with and without objects, including a human and car on 5, 10, and 20 meters distances. Afterward, we have developed four DL models, including a self-designed RadarNet using a Convolutional Neural Network (CNN) and transfer learning-based ResNet34, InceptionV3, and GoogleNet2. The models yield an average accuracy of 94&#x0025; using confusion matrix as a performance parameter. We further applied the Fast Gradient Sign Method (FGSM) adversarial attack on all four models and present a comparative study of its effects on classification accuracy. The results demonstrate that the average accuracy for DL-models degrades to 18.87&#x0025;, 17.53&#x0025;, and 16.08&#x0025; for epsilon values of 0.001, 0.005, and 0.009, respectively. Having a significant degradation in accuracy highlights that adversarial retraining is essential to counter the effects of FGSM adversarial attacks.
State of the Art Recommendation Approaches

Recommender systems have been used successfully in order to deal with information overload problems in a wide variety of domains ranging from e-commerce, e-tourism, to e-learning. They typically predict the ratings of unseen items by a user and recommend the top N items based on user's profile. Moreover, the profile can be enriched further by using additional information such as contextual data, domain knowledge, and tagging information among others for improving the quality of recommendations. Traditional approaches have not been effective in exploiting these additional data sources. Hence, new techniques need to be developed for extracting and integrating them into the recommendation process. In this article, the authors present a survey on state of the art recommendation approaches their algorithms, issues and also provides further research directions for developing smart and intelligent recommender systems.
Optimal Selection of Parallel Atrous Convolutions for MobileNet V3

An eye disease screening system is an important tool for health practitioners in performing mass screening tests at a low cost. Thus, the system is usually built for a mobile platform where the form factor is small and easily dispatched to rural areas. The system is also expected to perform automated decision-making with the help of the state-of-the-art intelligent artificial intelligence system. Hence, MobileNet V3 is an optimized convolutional neural network, which has been designed specifically for mobile applications. It consists of a stack of expansion modules that have been embedded with squeeze and excitation units. However, the network does not have dedicated multi-scale feature extraction functions to cater to objects of interest of various sizes. Therefore, a set of parallel atrous convolution with multiple dilation rates has been integrated into the original network to further improve classification accuracy. The results show that a set of atrous convolution with a maximum dilation rate of 4 produces the best accuracy of 0.719.
Association Metrics Between Two Continuous Variables for Software Project Data

The correlation coefficient is commonly used in analyses of software project data sets for the purpose of quantifying the relationship between two variables. However, while there are various types of relationships between two variables, the correlation coefficient cannot distinguish between these types. This study proposes new metrics between two continuous variables that have the potential to characterize the relationship types.
Philosophical Algebra

The observations of Micrographia aimed to provide some “meanest foundations” to eventually raise “superstructures” both in the study of nature and the reform of science. Like the rest of the book, the preface was intended by Hooke as a part of a more detailed methodological programme that was the subject of some Cutler lectures read at Gresham College in the second half of the 1660s. In 1664, Hooke only hinted at “another discourse” on the “manner of compiling a natural and artificial history” by means of “philosophical tables” that should be the “most useful for the raising of axioms and theories.” But in the early Cutlerian lectures read after the publication of Micrographia, Hooke focused only on the first part of his methodological programme, namely a new philosophical history that would furnish the mind “with fit material to work on.” He did not deal with the “rules and methods of proceeding or operating with this so collected and qualify’d supellex.” This part of the new method was described in Micrographia as a “new kind of algebra, or analitik art.” “By this, as by that art of algebra in geometry,” Hooke claimed, “’twill be very easy to proceed in any natural inquiry, regularly and certainly.” For this reason, it “might not improperly be call’d a philosophical algebra.” In the lectures read to the Gresham College and the Royal Society in the second half of the seventeenth century, Hooke did not go beyond the analogy between his “philosophical algebra” and the symbolic algebra of Viète and Descartes. In some late lectures read in the 1690s, he still referred to it as a project to complete. Over more than thirty years, Hooke never fixed his views on the analytic component of his methodological programme. In the works and papers following Micrographia, there are contrasting references to it.
Hybrid Model Using K-Means Clustering for Volumetric Quantification of Lung Tumor: A Case Study

Early detection of cancerous lung tumors has a greater influence in increasing the life expectancy and even in the prognostic aspects. The challenges associated with the diagnosis of the tumor is all the more complicated when the tumor is attached to blood vessels or lung wall. Identification of the size of the tumor also has its role in staging and prognosis of lung cancer. Medical image processing is certainly a crucial approach in improving the accuracy of the diagnosis. Considering the importance of aiming at accurate image analysis and immense complex data volume in medical image analysis, the benefits of the cooperation of different complementary approaches are appreciable. In this paper a hybrid model of segmentation using K-means clustering with morphological operation is proposed. A comparison based on the sequence of the process implemented is presented which extends its scope in volumetric quantification of lung tumors.
Novelty Prediction in Broadband Line Multi-variate Time Series Using a Deep Long Short-Term Memory Network

To ensure reliable broadband performance, particularly amongst customers working from home and reliant on home broadband performance, novelty prediction is an important part of the internet service providers (ISPs). It is ideal to identify the onset of poor broadband performance in real time before the customer experience, so that an ISP can take appropriate action. This paper proposes a Broadband (BB) line novelty prediction system. A deep bidirectional long short-term memory (BiLSTM) recurrent neural network is used for sequence-to-label classification using a telecoms dataset. This BiLSTM network, being able to understand historical information, is well-suited for sequence-to-label classification and the prediction of novelties before novelties occur. Time series observation points are retrieved from control hubs and faulty hubs over periods of five days, and a new feature is created to represent data incompleteness. The pre-processing phase produces descriptive features for each component to summarize the data characteristics. Both sequence-to-label decoding and our proposed sequence-to-label-rate decoding methods are then employed to make novelty predictions. A real telecoms dataset relating to customers is used to evaluate the performance of the system developed. The results are compared with traditional machine learning algorithms and show that the proposed system can predict novelties proactively, and with improved accuracy.
The Kinetic Universe of <i>Philippe Leroux's</i> De la texture<i> (2007): Drum Rudiments, Waveform Profiles, and Process Polyphony</i>

This vignette makes a number of analytical observations about Philippe Leroux's octet De la texture (2007), with a particular focus on the composer's transformation of gestural materials drawn from drum rudiments and waveform shapes into formal processes unfolding at various levels throughout the piece. It demonstrates how Leroux deploys his primal building blocks, whether composed of rudiments, waveform profiles, or non-pitched materials, within processes of kinetic ebb and flow that include alterations in dynamics, register, and tempo, as well as changes in pitch and/or rhythm.
Cyber Attacks, Contributing Factors, and Tackling Strategies

This article describes how as access to the Internet has increased, cybersecurity has become important, with businesses and the government spending much time and resources to combat cyber attacks. The purpose of this article was to review the existing literature related to cybersecurity. Specifically, the review synthesizes the empirical research in (1) various types of cyber attacks, (2) contributing factors related to cybersecurity behavior, and (3) strategi to improve cybersecurity behavior. The most developed line of research in this area has been focusing on the strategies to improve cybersecurity behavior, showing a questionable trend of quickly creating solutions before fully conceptualizing the problem.
On Game-theoretic Computation Power Diversification in the Bitcoin Mining Network

In the Bitcoin mining network, miners contribute computation power to solve crypto-puzzles in exchange for financial rewards. Due to the randomness and the competitiveness of mining, individual miners tend to join mining pools for low risks and steady incomes. Usually, a pool is managed by its central operator, who charges fees for providing risk-sharing services. This paper presents a hierarchical distributed computation paradigm where miners can distribute their power among multiple pools. By adding virtual pools, we separate miners’ dual roles of being the operator as well as being the member when solo mining. We formulate a multi-leader multi-follower Stackelberg game to study the joint utility maximization of pool operators and miners, thereby addressing a computation power allocation problem. We investigate two practical pool operation modes, a uniform-share-difficulty mode and a nonuniform-share-difficulty mode. We derive analytical results for the Stackelberg equilibrium of the game under both modes, based on which optimal strategies are designed for all operators and miners. Numerical evaluations are presented to verify the proposed model.
Lightweight Offline Authentication Scheme for Secure Remote Working Environment

During pandemic situations, remote work security and reliability become more important for sustainability. The main problem in remote working is providing a reliable internet connection between remote working employees and remote systems. Lack of internet connection issues cause remote working problems, and some companies allow employees to work on untrusted personal computers, which causes untrusted and unmanageable IT systems. According to these problems, this study proposed a lightweight, offline authentication scheme for the secure remote work environment. The proposed model is designed for LPS (Lightweight Portable Security) devices, and these LPS devices provide portable USB bootable images with a secure environment for this connection and trust problems. LPS devices are single USB dongles to make users more dependent on these LPS devices. This design provides mobile application-dependent authentication to provide strong user authentication with 2FA (2 Factor Authentication), which is something you have (mobile phone and USB dongle) and something you know (mobile PIN). Also, the proposed model provides geofencing between USB dongles and Mobile phones to offer robust security.
Determining Of Semantically Close Texts Of Stock Market News Using Natural Language Processing

The paper considers the problem of finding seman-tically close texts in the domain of short stock market news. Two approaches to perform semantic search are proposed: with and without context awareness. The contextualized approach uses whole sentences in feature space mapping based on universal sentence encoder. The methodology of cluster analysis with determining optimal number of clusters is presented as well as construction of the classifier. The semantic search is illustrated with two experiments: for the complete dataset and within specific cluster. Another approach deals with isolated words of the sentence ignoring context and computes the vector representation of the whole sentence using classes of its words. The semantic search reduces to finding nearest neighbors in feature space using Euclidean distance as a measure of similarity. The experiments are performed with search keys of different length. The exper-iments illustrate that results obtained with contextualized and context-less approaches are of different thematic coverage.
Implementation of K-Medoids Clustering Algorithm for Grouping Palm Oil Exports by Destination Country

Palm oil is one of the plantation products that becomes the export of Indonesia's leading commodity. The volume of palm oil exports tends to increase every year. This is influenced by the large demand for palm oil in palm oil importing countries in the world. The high demand for palm oil is an opportunity that must be developed so that Indonesia can compete with its competitors in the current pandemic. Indonesia as the world's largest palm oil producer must be able to group the countries that are priority as the largest importers of palm oil. For this reason, it is necessary to group the countries that are a priority as the largest importer of palm oil. The purpose of this study is to group palm oil exports based on the destination country using the K-Medoids Clustering algorithm. Based on the results of tests that have been conducted in this study using the K-Medoids Clustering algorithm, Cluster 1 is a category of countries importing low palm oil or Low, namely 7 (Netherlands, USA, Spain, Egypt, Bangladesh, Italy, Singapore) of 10 categories of countries tested, then cluster 2 is a category of countries importing medium palm oil or Medium which is 1 (Pakistan) of 10 categories of countries tested, and lastly cluster 3 is a category of high palm oil importing countries or High which is 2 (India and China) from 10 categories of countries tested.
Imagery, Privacy and Ethics: An Overview of Partially Occluded Facial Biometric Analysis in the Era of Face Masks

The COVID-19 pandemic has drastically changed human lifestyles, with implications on many aspects of human life. With the proliferation of masks to combat the spread of the virus, many computer vision workflows have been inadvertently affected to varying degrees. Consequently, many research articles have been dedicated to evaluating the impact to existing facial imagery recognition problems. Several works have attempted to either extend existing facial models or develop new techniques specific to masked faces. Many new benchmark tasks have also been introduced in this subdomain. However, a detailed review of such advancements is not available for perusal in this critical area for COVID-safe protocol development. In this work, we address this issue as the first review of masked facial recognition tasks and techniques robust to masked facial images. Our motivation is to provide a central reference for automated public health and COVID-safe identification protocols while also exploring the ethical aspects of further development of such techniques.
New Quantum Binary Image Detector (NQBID)

Binary image simply called Black and White image is one of the powerful image processing techniques helps in different applications such as computer vision, object detection, real time applications, and etc&#x20DB; Most of the Binary image detection techniques depends on certain threshold of a grey scale image but due to nanotech-nology and quantum computing discovers and searches a new method must be found that compatible with these modern in-ventions. This paper proposes new binary image detection technique (NQBID) using quantum computing principles and Bloch&#x0027;s sphere format that applies to image with different color space systems directly without need any additional in-terface or conversion to grey image. At the end of this paper a comparison produces between different results of the bi-nary image detected from different color image spaces using the newly proposes technique.
Fourier-Bessel series neural networks for classification

An innovative neural network for classification where hidden neurons are built from double Fourier-Bessel series (FBSNN) is presented. Topology of neural network includes 2 inputs, single output and arbitrary number of hidden neurons. This FBSNN was tested for linearly separable and non-separable, 2- and 3-classes classification problems in machine learning techniques and applications. For comparison, the results obtained from FBSNN were also analyzed by the famous backward propagation neural networks (BP ANN) and radial basis function neural networks (RBFNN), simultaneously. The performance in training process and the decision boundaries of all testing cases are also compared and examined. We found that FBSNN can provides correct classification using small number of hidden neurons and offers very rapid convergence in training process as compared with BPANN and RBFNN.
A Decentralized Machine Learning Scheme with Input Perturbation-Based Differential Privacy

With the increase in Internet of Things (IoT) devices, machine learning and big data analysis have been rapidly developing. The current big data analysis based on deep learning assumes that the raw data owned by countless users is aggregated into the cloud. However, the number of applications requiring data containing personal information, such as healthcare, increases every year. There are many concerns about operating a system that assumes data aggregation to the cloud due to risks such as data leakage.
Inter-Organizational Data Sharing: What Issues Should Be Considered?

Currently, data sharing activities between agencies have been done in many ways. This provides benefits but behind it there are also threats. Therefore, we explore what needs to be considered in sharing current data based on previously published research results. This research was conducted with a systematic literature review approach to 34 articles entered in the Scopus electronic database in 2017–2021. From the results of this study, we then propose six domains that need to be considered when sharing data, namely Security; access control; data privacy; confidentiality, integrity, and availability of data; compliance and trust; also data audit capabilities.
Photo Steel Stamp Removal Algorithm Based on Image Filtering

In modern society, it is necessary to use the photo with steel stamp on the ID for matching to search and match specific pedestrians. The steel stamp on the ID photo will obscure most of the portrait information, so it is essential to eliminate the stamp to get a clearer portrait. In this paper, an integrated filtering algorithm is proposed to eliminate the photo stencil area by marking the location of the stencil on the photo, followed by combining three different types of filtering algorithms. Three filtering algorithms can be chosen for the calculation of the steel stamp points as well as the points in the steel stamp field. The first filtering algorithm is a method which improves the principle of mean filtering, and it replaces the original value by calculating the mean of the steel mark point and its field range. The second filtering algorithm is an improvement on the principle of median filtering by calculating the median value of the stencil as well as its domain range, and then replacing the median value with the value of the stencil pixel point as well as the domain range. The third filtering algorithm is based on the principle of Gaussian filtering. The weight values of the steel mark and its field range are calculated by Gaussian template, so as to replace the value of the steel mark pixel point and its neighborhoods. By combining the Mean, Median and Gaussian filters into one new filter, the integrated filtering algorithm does not require the Creation of a mask. Meanwhile, it has been verified effective in eliminating photo stencil areas than the single filter and other types of traditional elimination algorithms available in the image processing.