Reviewerly/exHarmony huggingface dataset:
The Iso-ElGamal Cryptographic Scheme

In this paper, we use Santilli’s isonumber theory of the first kind to add a new degree of freedom of unit to the ElGamal cryptographic system leading to a new encryption scheme. In this scheme, the key generation, encryption, and decryption steps are performed in an isofield with an isounit replacing unit one of normal arithmetic. This isounit is used as a shared secret isokey for encryption and decryption as is the case in a symmetric cryptographic system. In addition, our proposed cryptographic system is asymmetric with a public isokey used for encryption and a private isokey used for decryption. We thus obtain a hybrid public-key private-key cryptographic system with a substantial increase in security due to this additional freedom of unit. The private isokeys and ciphertexts produced by this scheme are highly secure. In addition, the ability to use isokeys of ten thousand bits or more with minimum additional computational cost makes this cryptographic scheme produce highly secure encrypted information. Also, applications and implementations of the Iso-ElGamal scheme on messages represented by positive integers and data or information encoded into binary digits or bits are presented. A wide range of other applications in security services and security mechanisms, which include authentication, data confidentiality, data integrity, digital signature, and others can be implemented using our proposed scheme.
The Miwah high sulphidation epithermal Au–Ag deposit, Aceh, Indonesia: Dynamics of hydrothermal alteration and mineralisation interpreted from principal component analysis of lithogeochemical data

The principal component analysis (PCA) of whole-rock major and trace element data has unravelled some of the dominant geochemical processes and identified the most relevant component as an exploration vector for many types of magmatic-hydrothermal ore deposits. An important exception is the high-sulphidation (HS) epithermal class of deposits. Here, the results of a PCA of different segments of the Miwah HS epithermal Au–Ag deposit in Indonesia show that the geochemical signature of the deposit varies from a tight group of Au, Ag, Cu and As in PC1 for the predominantly hypogene main zone to a moderately correlated assemblage of Au, As, Cu and Sb in PC1 for the variably oxidised rocks along the southern periphery to an association of Au, Ni, Cr, Co and Mo in PC1 for the overlying rocks in the north. PC2 in all zones represents the largest variability of Pb and Zn and has moderate contributions from Au and As in the periphery and the highest Au contribution in the north. By contrast, PC3 in the main and peripheral zones, shows a high variability of Cu. Representatives of auriferous vuggy-massive silica rocks from the periphery are in equilibrium with hydrothermal fluids with δ18O values of +6.18, +7.48 and +9.28‰, whereas oxidised, silicified rocks from the analogous northern zone are in equilibrium with hydrothermal fluids with δ18O values of +8.0 and +8.6‰. The results of the above PCA, complemented by centred-log-ratio bivariate diagrams of grade-scaled variables, mineralogical relationships and oxygen isotopes, are interpreted to indicate that PC1 in the main zone is consistent with the deposition of Au and related metals from the condensation of magmatic gas mixtures during the early-stage silicification of protoliths. By contrast, PC2 is related to late-stage Pb- and Zn-rich fluids that migrated away from the main conduits. A hybrid process, intermediary between PC1 and PC2, leached Cr, Ni and Co from the adjacent ultramafic rocks and superimposed the metals on the Au + Cu mineralised rocks. Processes related to PC3 and other smaller PC occurred at later and lower temperature stages in which steam-heated oxidation led to the local destruction of sulphide and arsenide phases and the resulting acid fluids decoupled Au-Ag alloys and enargite to produce separate Au- and Ag-based minerals and covellite. The PCA of individual alteration types shows metal associations that mimic those of the main-stage mineralisation, thereby providing a useful exploration tool. The ability of PCA to separate Au and the associated metals from the Cr + Ni + Co assemblage can be used to identify areas that have been affected by hyperacid fluids originating from epithermal systems; hence, such areas become exploration targets not only in the vicinity of Miwah but also throughout Sumatra or elsewhere.
On the Computational Complexity of 2D Filtering by Winograd Method

Increasing the speed of digital image processing is an important problem in various fields. This paper proposes a way to increase the speed of 2D image filtering using the Winograd method. New algorithms for image processing based on the Winograd method have been proposed. The theoretical analysis results showed that an increase in the number of pixels in the resulting image fragment leads to a decrease in multiplications and the computational complexity of digital filtering. Winograd methods reduced multiplications to 4 times by increasing additions to 2.88 times using filter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathrm{x}3$</tex> . The results obtained can be used in various image processing fields that require real-time processing using modern microelectronic devices.
Improving Inclusivity in Robotics Design: An Exploration of Methods for Upstream Co-Creation

Disabled people are often involved in robotics research as potential users of technologies which address specific needs. However, their more generalised lived expertise is not usually included when planning the overall design trajectory of robots for health and social care purposes. This risks losing valuable insight into the lived experience of disabled people, and impinges on their right to be involved in the shaping of their future care. This project draws upon the expertise of an interdisciplinary team to explore methodologies for involving people with disabilities in the early design of care robots in a way that enables incorporation of their broader values, experiences and expectations. We developed a comparative set of focus group workshops using Community Philosophy, LEGO® Serious Play® and Design Thinking to explore how people with a range of different physical impairments used these techniques to envision a "useful robot". The outputs were then workshopped with a group of roboticists and designers to explore how they interacted with the thematic map produced. Through this process, we aimed to understand how people living with disability think robots might improve their lives and consider new ways of bringing the fullness of lived experience into earlier stages of robot design. Secondary aims were to assess whether and how co-creative methodologies might produce actionable information for designers (or why not), and to deepen the exchange of social scientific and technical knowledge about feasible trajectories for robotics in health-social care. Our analysis indicated that using these methods in a sequential process of workshops with disabled people and incorporating engineers and other stakeholders at the Design Thinking stage could potentially produce technologically actionable results to inform follow-on proposals.
Multi-Class Document Image Classification using Deep Visual and Textual Features

The digitalization era has brought digital documents with it, and the classification of document images has become an important need as in classical text documents. Document images, in which text documents are stored as images, contain both text and visual features, unlike images. Therefore, it is possible to use both text and visual features while classifying such data. Considering this situation, in this study, it is aimed to classify document images by using both text and visual features and to determine which feature type is more successful in classification. In the text-based approach, each document/class is labeled with the keywords associated with that document/class and the classification is realized according to whether the document contains the related key-words or not. For visual-based classification, we use four deep learning models namely CNN, NASNet-Large, InceptionV3, and EfficientNetB3. Experimental study is carried out on document images obtained from applicants of the Kocaeli University. As a result, it is seen ii that EfficientNetB3 is the most superior among all with 0.8987 F-score.
Optimal Feature Set Selection for IoT Device Fingerprinting on Edge Infrastructure using Machine Intelligence

The widespread deployment of IoT devices globally has spurred the need for protecting these devices from various cyber security attacks. IoT device fingerprinting is the method of identifying IoT devices by analyzing network traffic. Fingerprinting helps to determine if devices have been subverted by an attacker by separating anomalous behavior from normal behavior. This kind of device identification is carried out on some edge device with the help of machine learning. With the growing number of IoT devices and the large volumes of traffic generated by them, it is essential to determine an optimal feature set for device classification. An optimal feature set not only helps to identify the most important features useful in creating a device fingerprint, but also makes the classification model light weight by eliminating the redundant ones and suitable for deployment on edge devices with low computational power. In this paper, we present a feature reduction method using three popular machine learning techniques and apply it for classifying IoT devices. Our feature reduction method identifies the most important features by isolating them from the non-essential ones, thereby giving a reduced feature set that can provide a classification accuracy comparable to the original feature vector. Moreover, we use the reduced feature set thus obtained to identify new IoT devices introduced in the network. We have performed experiments using an open source IoT device dataset. The experimental results show that we are able to identify the optimal features that constitute only 19% of the original feature vector. Moreover, the absence of 81% of the initial features does not compromise the performances of device classification as well as new device identification.
Unmanned Aerial Vehicle Control Using Hand Gestures and Neural Networks

Neural Networks are a series of data manipulations inspired by how neurons perceive information in the brain. This technology is useful for accomplishing tasks that conventional computers do poorly, but people do accurately. Neural Networks are utilized in this project to control an Unmanned Aerial Vehicle (UAV) with hand gestures. For this case the model produced by TensorFlow will take twenty-one different hand points on a user’s hand using MediaPipe and distinguish which of eight gestures the user is signaling. This data is received through a camera on the UAV and once ran through the model the flight path will be controlled. The hand points are logged as two-dimensional coordinates in relation to the pixel they are in the frame. This creates a model with forty-two inputs and nine outputs. The model can run at around twenty frames per second due to the low number of inputs. The UAV can handle efficiently due to an acceptable processing time of its commands.
CLEAR: A Fully User-side Image Search System

We use many search engines on the Internet in our daily lives. However, they are not perfect. Their scoring function may not model our intent or they may accept only text queries even though we want to carry out a similar image search. In such cases, we need to make a compromise: We continue to use the unsatisfactory service or leave the service. Recently, a new solution, user-side search systems, has been proposed. In this framework, each user builds their own search system that meets their preference with a user-defined scoring function and user-defined interface. Although the concept is appealing, it is still not clear if this approach is feasible in practice. In this demonstration, we show the first fully user-side image search system, CLEAR, which realizes a similar-image search engine for Flickr. The challenge is that Flickr does not provide an official similar image search engine or corresponding API. Nevertheless, CLEAR realizes it fully on a user-side. CLEAR does not use a backend server at all nor store any images or build search indices. It is in contrast to traditional search algorithms that require preparing a backend server and building a search index. Therefore, each user can easily deploy their own CLEAR engine, and the resulting service is custom-made and privacy-preserving. The online demo is available at https://clear.joisino.net. The source code is available at https://github.com/joisino/clear.
Dino: A Block Transmission Protocol with Low Bandwidth Consumption and Propagation Latency

Block capacity plays a critical role in maintaining blockchain security and improving transactions per second (TPS). Increasing block capacity can help attain higher TPS, but it also prolongs block propagation latency and degrades system security. In the present paper, we argue that existing work compressing the block size to shorten block propagation latency introduces an undesired side effect, which is that the size of compressed blocks will increase with transaction volume. Instead, we propose Dino, a new block transmission protocol between two peers. Once a node receives a Dino block, it can recover the original block with that Dino block and transactions in its transaction pool. Since Dino transmits block construction rules instead of compressed block content, it has good scalability to transmit blocks with larger transaction volume. We deploy Dino into Bitcoin and Bitcoin Cash to compare it with the state-of-art protocols: Compact, XThin, and Graphene. For a block with 3,000 transactions, its corresponding Dino block is no more than 1 KB in size, which is only 4% of a XThin block, 5% of a Compact block, and 20% of a Graphene block. The size of Dino blocks stays constant when the transaction volume reaches Bitcoin and Bitcoin Cash’s protocol limit. Simulation experiments show that Dino scales well with higher transaction generation rates and can reduce block propagation latency.
Joint modulation of 3-PPM and quantum squeezed states in communication systems

Pulse position modulation(PPM) is an excellent modulation form that is widely used for deep-space information transmission and also in quantum modulation form. Lots of related work proved that the squeezed state is superior to the traditional coherent state modulation in 3-PPM modulation. In this paper, a new modulation method is proposed, that is, two squeezed states are applied to 3-PPM modulation. Compared with one squeezed state modulation, it is found that the two squeezed state modulation has lower error probability.
NeuroMessenger: Towards Error Tolerant Distributed Machine Learning Over Edge Networks

Despite the evolution of distributed machine learning (ML) systems in recent years, the communication overhead induced by their data transfers remains a major issue that hampers the efficiency of such systems, especially in edge networks with poor wireless link conditions. In this paper, we propose to explore a new paradigm of error-tolerant distribute ML to mitigate the communication overhead. Unlike generic network traffic, ML data exhibits an intrinsic error-tolerant capability which helps the model yield fair performance even with errors in the data transfers. We first characterize the error tolerance capability of state-of-art distributed ML frameworks. Based on the observations, we propose NeuroMessenger, a lightweight mechanism that can be built into the cellular network stack, which can enhance and utilize the error tolerance in ML data to reduce communication overhead. NeuroMessenger does not require per-model profiling and is transparent to application layer, which simplifies the development and deployment. Our experiments on a 5G simulation framework demonstrate that NeuroMessenger reduces the end-to-end latency by up to 99% while maintaining low accuracy loss under various link conditions.
Mobile Remote Assistance with Augmented Reality Applied in a Power Distribution Utility: A Qualitative Study

In Industry 4.0 scenario, new technologies have emerged to improve daily industrial activities. Among these technologies, Remote Assistance (RA) techniques adoption is growing in several sectors, in order to improve cooperation between staffs. This approach combined with Augmented Reality (AR) can be even more effective and assertive during the interactions. In the specific context of Power Distribution Utilities (PDU), an efficient communication between the electricians is usually required. With this in mind, this paper presents a qualitative analysis of the testing, deployment and operation of a RA with AR system applied in a PDU from São Paulo Metropolitan Region. This evaluation was performed through a survey with a group of 98 electricians over four months using the technology. The survey included questions about adequacy, usability, communication, applicability and acceptance of the system. The results showed more than 80% of positive and neutral rates of the technology in all evaluated qualitative features.
An RFID and Computer Vision Fusion System for Book Inventory using Mobile Robot

Mobile robot-assisted book inventory such as book identification and book order detection has become increasingly popular in smart library, replacing the manual book inventory which is time-consuming and error-prone. The existing systems are either computer vision (CV)-based or RFID-based, however several limitations are inevitable. CV-based systems may not be able to identify books effectively due to low accuracy of detecting texts on book spine. RFID tags attached to books can be used to identify a book uniquely. However, in high tag density scenarios such as library, tag coupling effects of adjacent tags may seriously affect the accuracy of tag reading. To overcome these limitations, this paper presents a novel RFID and CV fusion system for Book Inventory using mobile robot (RC-BI). RFID and CV are first used individually to obtain book order, then the information will be fused by the sequence based matching algorithm to remove ambiguity and improve overall accuracy. Specifically, we address three technical challenges. We design a deep neural network (DNN) model with multiple inputs and mixed data to filter out interference of RFID tags on other tiers, and propose a video information extracting schema to extract book spine information accurately, and use strong link to align and match RFID- and CV-based timestamp vs. book-name sequences to avoid errors during fusion. Extensive experiments indicate that our system achieves an average accuracy of 98.4% for tier filtering and an average accuracy of 98.9% for book order, significantly outperforming the state-of-the-arts.
Photo-realistic image synthesis from lines and appearance with modular modulation

The image-to-image translation task has made significant progress by relying on conditional generative adversarial networks. However, for many tasks, multiple condition images are required. This paper considers a very classic application scenario, using lines and appearance to synthesize photo-realistic images, describing structure and appearance information, respectively, for example, generating realistic face images from portrait drawings and color scribbles, and generating photos from sketches and texture patches. The key to this type of task is how to fuse the two conditional information. We propose an image translation system driven by line and appearance images, introducing a modular architecture for condition fusion. Unlike the previous condition fusion schemes, its main body of the generator is composed of stacked modulation units (MUs). Here, structural features and appearance features are progressively incorporated via cascaded MUs, each of which pays attention to the local regions. The visualization experiment shows that such a scheme lets the network automatically learn to decompose the fusion process as multiple sub-steps in latent spaces. Our model produces higher quality results quantitatively and qualitatively compared to the state-of-the-art method on different tasks and datasets. The ablation study demonstrates the effectiveness of the MUs and intuitively explains the process of feature fusion through visualization.
DRESS-ML: A Domain-specific Language for Modelling Exceptional Scenarios and Self-adaptive Behaviours for Drone-based Applications

Drones are gaining attention due to its possibility to support wide different types of applications. Since they can operate in different environments, it is possible to encounter uncertainties and exceptional situations, not initially predicted, during the use of drone-based applications. In this realm, self-adaptive strategies have been successfully used to guarantee resilience and continuous execution of such applications despite environment changes. Although some modelling approaches emerged to represent drone concepts, they are limited to model only expected flight plans or include few environmental conditions and drone resources, which restrict considerably their use. To mitigate those problems, this work proposes a domain-specific language, called DRESS-ML, which allows modelling exceptional situations and self-adaptive behaviours for drone-based applications. It relies on the Given-When-Then template used in the Behaviour-driven development (BDD) technique and the some of the main Aspect-oriented Programming concepts. We validate the applicability of our language through a proof of concept regarding an example application that uses a drone to monitor a forest to search for fire spots. Drones are gaining attention due to their possibility of supporting diverse applications and environments, such as search-and-rescue, surveillance, and goods delivery. Due to this variety of applications, drones can face both uncertainties and exceptional situations that they did not initially foresee during flight plan. In this sense, providing the ability to monitor the system and its environment and to change the system to ensure resilience and continuous execution are benefits of self-adaptation strategies. Although some studies have proposed approaches to model drone concepts, they are limited to model only expected flight plans or include few environmental conditions and drone resources, which restrict considerably their use. This work proposes a domain-specific language, called DRESS-ML, which allows modelling exceptional scenarios and self-adaptive behaviors to mitigate those problems. The language is based on an well known structure used for specifying system behaviour and the main concepts of a programming paradigm that injects encapsulated behaviours in the system. A practical example that uses a drone to monitor a forest to search for fire spots is used to evaluate the proposed language, demonstrating its applicability to model various exceptional scenarios.