Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Federated Learning

Conventional machine learning (ML) needs centralized training data to be present on a given machine or datacenter. The healthcare, finance, and other institutions where data sharing is prohibited require an approach for training ML models in secured architecture. Recently, techniques such as federated learning (FL), MIT Media Lab's Split Neural networks, blockchain, aim to address privacy and regulation of data. However, there are difference between the design principles of FL and the requirements of Institutions like healthcare, finance, etc., which needs blockchain-orchestrated FL having the following features: clients with their local data can define access policies to their data and define how updated weights are to be encrypted between the workers and the aggregator using blockchain technology and also prepares audit trail logs undertaken within network and it keeps actual list of participants hidden. This is expected to remove barriers in a range of sectors including healthcare, finance, security, logistics, governance, operations, and manufacturing.
Grid-Based Approach to Determining Parameters of the DBSCAN Algorithm

Clustering is a very important technique used in many fields in order to deal with large datasets. In clustering algorithms, one of the most popular approaches is based on an analysis of clusters density. Density-based algorithms include different methods but the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the most cited in the scientific literature. This algorithm can identify clusters of arbitrary shapes and sizes that occur in a dataset. Thus, the DBSCAN is very widely applied in various applications and has many modifications. However, there is a key issue of the right choice of its two input parameters, i.e the neighborhood radius (eps) and the MinPts. In this paper, a new method for determining the neighborhood radius (eps) and the MinPts is proposed. This method is based on finding a proper grid of cells for a dataset. Next, the grid is used to calculate the right values of these two parameters. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.
Securely Outsource Modular Exponentiations With Single Untrusted Cloud Server

Modular exponentiation is one of the most fundamental operations in lots of encryption and signature systems.Due to the heavy computation cost of modular exponentiation, many schemes have been put forward to securely outsource modular exponentiation to cloud.However, most of the existing approaches need two non-colluded cloud servers to securely complete the modular exponentiation, which will result in private data leakage upon the cloud servers collude.Besides, most existing schemes assume both base and exponent in modular exponentiation are private, which does not conform to many real-world applications.For example, in public key encryption system that uses modular exponentiation, one of base and exponent is the public key, and the other is a message.Usually, only the message should be privately protected.In this paper, we propose two secure outsourcing schemes for fixed-base (public base and private exponent) and fixed-exponent (private base and public exponent), respectively.In the proposed schemes, we employ only one cloud server and can thus avoid collusion attack.Further, we achieve an efficient and secure Paillier encryption outsourcing scheme based on our secure modular exponentiation outsourcing methods.Additionally, we theoretically analyze our overheads and leverage simulation experiments to evaluate our proposed solutions, which show our schemes can achieve high efficiency.
Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems

Abstract The rapid development of e-commerce has led to a swiftly increasing number of competing providers in electronic markets, which maintain their own, individual data describing the offered items. Recommender systems are popular and powerful tools relying on this data to guide users to their individually best item choice. Literature suggests that data quality of item content data has substantial influence on recommendation quality. Thereby, the dimension completeness is expected to be particularly important. Herein resides a considerable chance to improve recommendation quality by increasing completeness via extending an item content data set with an additional data set of the same domain. This paper therefore proposes a procedure for such a systematic data extension and analyzes effects of the procedure regarding items, content and users based on real-world data sets from four leading web portals. The evaluation results suggest that the proposed procedure is indeed effective in enabling improved recommendation quality.
DP-ANN: A new Differential Private Artificial Neural Network with Application on Health data (Workshop Paper)

Privacy of the individual data, especially in the Health data, is very sensitive and important. Privacy preserving Machine learning is emerging as one of the solutions for the security of data with the utility to create knowledge. In this paper, we have proposed a differential private artificial neural network (DP-ANN) and shows its application to predict the spread and the peak number of COVID-19 cases. We proposed a differential private artificial neural network (DP-ANN) in which Laplacian noise has been introduced at activation function level and it has been compared with existing privacy ideas at error function and weights level of ANN. Results show that DP-ANN model with the private activation function produces the result similar to the base ANN model.
A dictionary-based method for detecting machine-generated domains

Internet robots, also known as bots, have transformed the businesses and society with convenience. However, the dynamics of these interactions could be under adversarial circumstances with detrimental effects on network security. Bots that use domain-generation algorithms (DGAs) can generate many random domains dynamically so that static domain blacklists become ineffective in preventing malicious attacks by botnets. Various families of recent botnets have used DGA to establish communication with the bots. Researchers have introduced various detection methods with moderate success. Methods proposed so far either detect only DGAs that use non-variations forms or focus on the classification accuracy instead of time complexity, which would be critical in real-world production. The goal of this article is to explore how machine learning can help in detecting machine-generated domain names. To that end, we propose a dictionary-based n-gram method that can detect 39 DGA variations. We compared our method with existing research and found that our method can improve the performance of the existing classification algorithms. At last, our method can achieve competitive results as the LSTM model while requiring less time and complexity. Our research helps real-time production for DGA detection and provides insight in protecting DNS server and information security.
Making as imaginative crossroads: Ghanaian makers and the geopolitics of technological progress

Abstract This paper is about the politics of technological progress as it is being played out among a loose network of Ghanaian makers. It unpacks how the practice of ‘making’ unfolds as a site for positioning the self and the nation within a global imaginary of techno futures. The paper argues, first, that ‘making’ in Ghana is emblematic of a crossroads of imaginative possibilities for technological design and production, and second, that this marks a distinct turn in the politics of technological progress, particularly when situated against ongoing econo‐political negotiations between the Global south, the West, and China.
FilterJoint: Toward an Understanding of Whole-Body Gesture Articulation

Classification accuracy of whole-body gestures can be improved by selecting gestures that have few conflicts (i.e., confusions or misclassifications). To identify such gestures, an understanding of the nuances of how users articulate whole-body gestures can help, especially when conflicts may be due to confusion among seemingly dissimilar gestures. To the best of our knowledge, such an understanding is currently missing in the literature. As a first step to enable this understanding, we designed a method that facilitates investigation of variations in how users move their body parts as they perform a motion. This method, which we call filterJoint, selects the key body parts that are actively moving during the performance of a motion. The paths along which these body parts move in space over time can then be analyzed to make inferences about how users articulate whole-body gestures. We present two case studies to show how the filterJoint method enables a deeper understanding of whole-body gesture articulation, and we highlight implications for the selection of whole-body gesture sets as a result of these insights.
Computing newforms using supersingular isogeny graphs

We describe an algorithm that we used to compute the q-expansions of all weight 2 cusp forms of prime level at most $$2,\!000,\!000$$ and dimension at most 6. We also present an algorithm that we used to verify that there was only one cusp form of dimension 7 or more per Atkin-Lehner eigenspace for prime levels between $$10,\!000$$ and $$1,\!000,\!000$$ . Our algorithm is based on Mestre’s Méthode des Graphes, and involves supersingular isogeny graphs and Wiedemann’s algorithm for finding the minimal polynomial of sparse matrices over finite fields.
Forward analysis for WSTS, part I: completions

Abstract We define representations for downward-closed subsets of a rich family of well-quasi-orders, and more generally for closed subsets of an even richer family of Noetherian topological spaces. This includes the cases of finite words, of multisets, of finite trees, notably. Those representations are given as finite unions of ideals, or more generally of irreducible closed subsets. All the representations we explore are computable, in the sense that we exhibit algorithms that decide inclusion, and compute finite unions and finite intersections. The origin of this work lies in the need for computing finite representations of sets of successors of the downward closure of one state, or more generally of a downward-closed set of states, in a well-structured transition system, and this is where we start: we define adequate notions of completions of well-quasi-orders, and more generally, of Noetherian spaces. For verification purposes, we argue that the required completions must be ideal completions, or more generally sobrifications, that is, spaces of irreducible closed subsets.
Unconstrained Face Verification Based on Monogenic Binary Pattern and Convolutional Neural Network

Unconstrained Face Verification is still an important problem worth researching. The major challenges such as illumination, pose, occlusion and expression can produce more complex variations in both shape and texture of the face. In this paper, we propose a method based on Monogenic Binary Pattern and Convolutional Neural Network (MBP-CNN) to improve the performance of face recognition system. For each facial image, the proposed method firstly extracts local features using Monogenic Binary Pattern (MBP) which is an excellent and powerful local descriptor compared to the well-recognized Gabor filtering-based LBP models. Then, we use Convolutional Neural Networks which is one of the best representative network architectures of deep learning in the literature, in order to extract more deep features. Thus, the developed MBP-CNN has robustness to variations of illumination, occlusion, pose, expression, texture and shape by combining Monogenic Binary Pattern and convolutional neural network. Moreover, MBP-CNN was more accurately represented by combining global and local information of facial images. Experiments demonstrate that our method provided competitive performance on the LFW database, compared to the others described in the state-of-the-art.
Process of information engagement: Integrating information behavior and user engagement

Abstract User engagement is considered critical to technology interactions. The user engagement scale (UES) is a measurement tool for engagement based on self‐reported measures. However, UES does not emphasize interactions with information and focus solely on perception. This visual presentation describes a novel conceptual and measurement framework to systematically describe and evaluate information engagement (IE). Following a comprehensive literature review, we conceptualize The process of information engagement (PIE) which includes three dimensions: (a) Participation ‐ users' investment in the information interaction, including information retrieval and behavioral responses ‐ measured through information exchange (b) Perception ‐ users' relation to the information, the level of emotional involvement, interest and intent in the information (c) Perseverance – users' retention of the information, the quality of the integration and influence of the information. The findings provide foundation for future work to conceptualize and operationalize IE measurement across a range of situations.
IMA-NET: Innovative e-Management Platform for Academic Network

In academic networks are priority in higher education international cooperation. Successful management of teachers' and students' mobilities is one of the main tasks in academic networks. Based on the positive experience with the coordination of CEEPUS Network - CIII-BG-1103-05-2021 Modelling, Simulation and Computer-aided Design in Engineering and Management, a platform for Innovative e-Management of Academic Network IMA-NET is developed. The paper describes in details the software realization of IMA-NET platform, its modules and its impact on network management.
Can blockchain be a strategic resource for ODL?: a study

Purpose The purpose of this paper is to examine the hitherto unexplored strategic potentialities of blockchain as an intangible resource for open and distance learning (ODL) to attain and sustain competitive advantage by creating a more learner centric learning ecosystem. Design/methodology/approach Research design included review of the literature, desk research and case analyses to conduct this study so that the hitherto unexplored strategic potentialities of blockchain, a technological innovation, for ODL could be explored adequately. Findings The findings of the paper reveal that ODL must not miss out on the opportunity of harnessing the hitherto unexplored strategic potentialities of blockchain in its quest of attaining and sustain competitiveness vis-a-vis conventional system of learning. Results of this study sufficiently hint at the possible viability of considering blockchain as a strategic resource for ODL. Practical implications Results of this study sufficiently indicate the possible viability of considering blockchain as a strategic resource for ODL which in turn make them significant for decision-makers and policymakers engaged in developing ODL. Findings hint that ODL needs to make efforts to harness the strategic potentialities of blockchain for attaining and sustaining competitiveness which would also be helpful for ODL to contribute towards achievement of sustainable development goals. This paper attempts to provide a strategic viewpoint to the use of this technological innovation as a significant intangible resource discussing primarily the strategic perspectives of blockchain helpful in the further development of ODL. Originality/value Although uses of blockchain-enabled applications in ODL are relatively new, the growth is very fast. The present paper offers originality and value by filling the gap created by the inadequacies of studies focusing on the idiosyncratic characteristics including but not limited to decentralization and security of blockchain. Findings suggest that blockchain offers huge potentialities in terms of benefits for entire ODL ecosystem comprising different stakeholders such as institutions, learners, educators, etc.
Lightweight cryptography for Internet of Things using FPGA-based Design with Partial Reconfiguration

In the daily context of Internet of Things, this paper implements lightweight cryptographic algorithms to secure designs based on the Field Programmable Gate Array (FPGA) technology with the advantage of partial reconfiguration. Also, this paper presents a FPGA market study and the benefits of using this technology in IoT. The proposed solution contains Espresso and Grain 128 lightweight algorithms to ensure data confidentiality in IoT and CRC32 to check data integrity. The application is designed and implemented using Basys 3 Artix-7 FPGA Board from Digilent and Vivado Design Suite software. The novelty of this paper relies on the benefits of using partial reconfiguration by switching between the two algorithms depending on IoT traffic and security conditions.
Security across abstraction layers: old and new examples

A common technique for building ICT systems is to build them as successive layers of bstraction: for instance, the Instruction Set Architecture (ISA) is an abstraction of the hardware, and compilers or interpreters build higher level abstractions on top of the ISA.The functionality of an ICT application can often be understood by considering only a single level of abstraction. For instance the source code of the application defines the functionality using the level of abstraction of the source programming language. Functionality can be well understood by just studying this source code.Many important security issues in ICT system however are cross-layer issues: they can not be understood by considering the system at a single level of abstraction, but they require understanding how multiple levels of abstraction are implemented. Attacks may rely on, or exploit, implementation details of one or more layers below the source code level of abstraction.The purpose of this paper is to illustrate this cross-layer nature of security by discussing old and new examples of cross-layer security issues, and by providing a classification of these issues.
Real Time Multiple Object Detection on Low-Constrained Devices using Lightweight Deep Neural Network

Over the past few decades, deep neural networks have gained attention due to their widespread use in filtering, combinatorial advancement, style transfer, pattern recognition, computer, and some different territories. Object detection is one of the demanding tasks in the area of computer vision. There are numerous well-known object recognition approaches that focus on deep neural networks. The popular ones are region-based convolution neural networks (RCNN), single-shot multi-object locator (SSD), and you only look once (YOLO), centered in Area. Each of these models requires huge processing and power consumption, which is a major challenge for implementations of multiple object detection in real-time on resource devices, such as mobile phones or embedded gadgets. The collaborative integration of SSDlite and mobile networks provides better results for the identification and tracking of artifacts on resource-constrained networks. In this paper, the faster_rcnn_inception_v2,ssd_mobilenet_v1_fpn and SSDlite-mobilenetV2 coco models are trained on GPU GTX 1660 GeForce GTX 1660 Ti using MS-COCO dataset and checked on using RabberiPi 3 B + . The parameters are accuracy and frame per second (FPS) are used to compare performance of the models on GPU as well as CPU was tested.
Modelling large scale camera networks for identification and tracking: an abstract framework

In this study, the authors discuss a novel approach for multi‐camera‐based unobtrusive identification and tracking of occupants in wide‐area, multi‐building scenarios. Considering the scalability issues in adopting a centralised approach to monitor wide‐area scenarios, they proposed a distributed approach to occupant identification and tracking. The key technical idea underlying their approach is to abstract a wide‐area indoor surveillance environment using a distributed state transition system (DSTS) model, which in turn is composed of independent building‐specific state transition systems, coordinating and collaborating with each other. This study presents the details of their DSTS model and examines the temporal ordering of recognition events within the DSTS for ensuring accurate state information and responses to spatio–temporal queries. They also provide an experimental evaluation of the performance of their model using precision‐recall metrics. Their conclusion is that the DSTS model serves as an efficient mechanism for tracking occupants in wide‐area, multi‐building scenarios monitored by camera networks.
Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis

While facial biometrics has been widely used for identification purpose, it has recently been researched as medical biometrics for a range of diseases. In this chapter, we investigate the facial landmark detection for atypical 3D facial modelling in facial palsy cases, while potentially such modelling can assist the medical diagnosis using atypical facial features. In our work, a study of landmarks localisation methods such as stacked hourglass networks is conducted and evaluated to ascertain their accuracy when presented with unseen atypical faces. The evaluation highlights that the state-of-the-art stacked hourglass architecture outperforms other traditional methods.
Anomaly detection of aircraft lead‐acid battery

Abstract The lead‐acid battery has been widely used in various fields. In civil aviation aircraft, it plays a vital role in the power system to maintain normal operation during the flight mission. Thus, an effective abnormal detection system for monitoring and diagnosing the status of aircraft lead‐acid battery is essential to ensure its safety and reliability. This paper aims to effectively identify aircraft battery faulty using unsupervised anomaly detection techniques. It introduces state‐of‐the‐art anomaly detection algorithms and evaluates their performance on a large real civil aviation battery data. The experimental results show that the latest isolation‐based anomaly detectors, iForest and iNNE, have outstanding performance on this task and have promising applicability as efficient methods for guaranteeing the lead‐acid battery quality and reliability in civil aviation aircraft.
Raphael's Platonic Vision

Abstract The four frescoes by Raphael in the Stanza della Segnatura in the Vatican Museum visually embody close approximations of several numerical ratios that are of deep significance in the material grounding of musical harmonies in the physics of natural harmonics. Of special significance is the Pythagorean musical frequency ratio of 9:8, the (discordant) whole tone interval, which in Plato's Timaeus is called the epogdoôn (‘and an eighth’).