Reviewerly/exHarmony huggingface dataset:
Blockchain Based Secure Lottery Platform by Using Smart Contract

This paper presents an approach for building a robust lottery platform using a smart contracts and blockchain. This lottery platform is deployed on the rinkeby test network and the smart-contracts are created using the solidity programming language. Solidity is an object-oriented programming language which is useful for deploying smart contracts. A combination of blockchain and smart contracts provides security and transparency. Blockchain is a decentralized technology in which no single entity has complete authority. The suggested lottery system involves selling, buying, registration, and the winner's announcement. The study also discusses the differences between traditional and blockchain-based lottery systems.
Comparison between Line-Followers and Free Movement Robots in Tasks Execution in a Simulated Environment

In Industry 4.0, there is a constant pursuit for efficiency and productivity, and, searching to achieve this, the usage of multiple robots to attend to several tasks simultaneously is usual. Nowadays, there is plenty of robotic architectures, having their navigation system based on numerous different sensors. This work compares the line-follower robots, having a determined path, with the free-moving robots that navigate based on odometry, for load collecting and delivery tasks in specific locations. For this, four simulated experiments are run in two scenes, with two and four tasks, respectively. The comparison is based on a difference in robot quantity, with more line-followers than free-moving ones, aiming to collect values such as idle time, task wait time, and the number of tasks done for each machine. The results show advantages in using less but more intelligent robots with free movement. Such robots are not bound to one unique task and can use their idle time to roam and complete other processes. However, the task wait time is a concerning issue, as too few robots for overcharged situations can increase execution delays.
Multi-Criteria Decision Method Applied to Path Planning for Mobile Robots

This article presents a proposal for path planning of mobile robots using an approach based on the Analytic Hierarchy Process (AHP) multi-criteria decision model. Like other multi-criteria decision methods, the AHP considers and evaluates different candidates/alternatives under different criteria and defines the alternative that best satisfies the relationship between the criteria. The algorithm proposes the evaluation of alternatives based on four criteria: the distance to the target, the angle to the target, the possibility of collision with obstacles and deviation from the planned path. To this end, a global movement planning is performed and will be used by the AHP to define its point-to-point movement. To validate and perform a practical/real analysis of the proposed algorithm, simulations are performed in the CoppeliaSim environment in conjunction with Matlab, using the Pioneer 3-DX mobile robot.
Semi-Supervised Learning for Intelligent Surveillance

Semi-supervised Learning (SSL) has shown promising improvements when used for the Object Detection (OD) task. Existing works detected some unique challenges associated with this setting and discussed how they differ from the classification paradigm. In this work, we revisit two of the first approaches used for OD, namely, Self-Training and Augmentation driven Consistency (STAC) and Unbiased Teacher. Our work was made in partnership with ALTAVE, a Brazilian company focused on intelligent surveillance. By using their in-house dataset we show that relative performance refinement follows the findings from the original authors and propose an improved augmentation method that boosts both approaches, obtaining an overall improvement of 4.4% in the mAP, while using no additional labels for training.
Memory-based Deep Reinforcement Learning for Humanoid Locomotion under Noisy Scenarios

This paper proposes a model-free memory augmented Deep Reinforcement Learning (DRL) method that can deal with noisy sensors in humanoid locomotion. DRL-based agents are promising for automatically learning how to control robots in complex simulated environments. However, they are still not fully addressed with model-free control algorithms in challenging noisy scenarios for humanoid robots. This work shows how the Soft Actor-Critic (SAC) algorithm can benefit from the memory effect introduced by LSTMs to mitigate the side effects of Partially Observed Markov Decision Processes (POMDP). We demonstrate that LSTM-SAC is a viable path towards DRL for POMDP by applying it in a bipedal locomotion task with the NAO Robot in various noisy scenarios.
Path planning for ground robots based on radio signal strength

Communication is one of the most critical areas in robot operations, especially in environments unsuitable for humans, such as confined spaces. Maintaining a constant connection between robot and base ensures that these operations go as expected, reducing the risk of accidents, damage, and the need to rescue the robot. Thus, this paper proposes a path planner that ensures that the robot travels only through locations with the best radio signal. For this, we construct a graph using the information from the fusion of a radio signal map and an occupancy grid map. We then use bidirectional Dijkstra's algorithm to compare two paths computed using different metrics: one associated with a shorter distance and another aiming for radio signal quality improvement. Finally, we present the results of tests performed in a laboratory, showing the feasibility of the path planner based on the radio signal quality.
Comparison of the YOLOv3 and SSD Models Using a Balanced Dataset with Data Augmentation, for Object Recognition in Images

There are several models for object detection, among them the SSD and YOLO computer vision tools. These recognition systems are used to detect and classify objects in images or video frames in real time, with good performance. This article studies and compares the YOLOv3 and SSD MobileNet v2 algorithms for identifying objects in images. In order to achieve the intended objective, at first, the algorithms were trained and compared without data augmentation. After, the data augmentation was executed for improving the performance of the algorithms. Analyzing the results, a slightly better performance of the YOLOv3 model was observed, without performing data augmentation, although this model takes more time to complete the training for the same number of steps compared to the SSD MobileNet v2 model. On the other hand, when performing data augmentation, the SSD model is favored.
Epistemic uncertainty estimation with evidential learning on semantic segmentation of underwater images

As the evolution of robotics occurs, it has become increasingly common for robotic systems to be integrated with intelligent systems. Despite the relevant advance in the area of neural networks, what happens inside a network is still not completely comprehended. The uncertainties in the neural network prediction can be epistemic, which is related to the model, or aleatory, which is related to the data. The reliability of network predictions can be increased with uncertainty estimation. Making the fusion of sensor data with trustful predictions, robotic systems could operate in more critical environments, where high accuracy is required, as well as maintaining navigation in the case of sensor fault. Underwater environments suffer from some disturbances that affect the acquisition of images due to light interference or wave movement, and these perturbations make difficult the use of neural networks for underwater robots, due to doubtful predictions. Taking this into account, this paper proposes a methodology to estimate the epistemic uncertainty with the case study in semantic segmentation for underwater images, where the behavior of uncertainty during training is observed, resulting in a qualitative analysis of epistemic uncertainty for Semantic Segmentation. The results suggest estimated epistemic uncertainty is directly connected with the parameters, amounts of epochs, size, and quality of the dataset.
Project of a Sentinel Robot Controlled with a Tracking Algorithm

This work aimed to design and build a sentinel robot, with two degrees of freedom for camera vision. Developed a pixel-based position control by integrating the engine control with a tracking algorithm based on the Google MediaPipe algorithm. The user must indicate a person and the prototype identifies and chases the target leaving it centered on the screen. This article will describe and analyze the construction and integration of results.
Semantic SuperPoint: A Deep Semantic Descriptor

Several SLAM methods benefit from the use of semantic information. Most integrate photometric methods with high-level semantics such as object detection and semantic segmentation. We propose that adding a semantic segmentation decoder in a shared encoder architecture would help the descriptor decoder learn semantic information, improving the feature extractor. This would be a more robust approach than only using high-level semantic information since it would be intrinsically learned in the descriptor and would not depend on the final quality of the semantic prediction. To add this information, we take advantage of multi-task learning methods to improve accuracy and balance the performance of each task. The proposed models are evaluated according to detection and matching metrics on the HPatches dataset. The results show that the Semantic SuperPoint model performs better than the baseline one.
TextBack: Watermarking Text Classifiers using Backdooring

Creating high performance neural networks is ex-pensive, incurring costs that can be attributed to data collection and curation, neural architecture search and training on dedi-cated hardware accelerators. Stakeholders invested in any one or more of these aspects of deep neural network training expect as-surances on ownership and guarantees that unauthorised usage is detectable and therefore preventable. Watermarking the trained neural architectures can prove to be a solution to this. While such techniques have been demonstrated in image classification tasks, we posit that a watermarking scheme can be developed for natural language processing applications as well. In this paper, we propose TextBack, which is a watermarking technique developed for text classifiers using backdooring. We have tested for the functionality preserving properties and verifiable proof of ownership of TextBack on multiple neural architectures and datasets for text classification tasks. The watermarked models consistently generate accuracies within a range of 1 - 2% of models without any watermarking, whilst being reliably verifiable during watermarking verification. TextBack has been tested on two different kinds of Trigger Sets, which can be chosen by the owner as preferred. We have studied the efficiencies of the algorithm that embeds the watermarks by fine tuning using a combination of Trigger samples and clean samples. The benefit of using TextBack's fine tuning approach on pre-trained models from a computational cost perspective against embedding watermarks by training models from scratch is also established experimentally. This watermarking scheme is not computation intensive and adds no additional burden to the neural architecture. This makes TextBack suitable for lightweight applications on edge devices as the watermarked model can be deployed on resource-constrained hardware and SoCs when required.
Mobile Systems Secure State Management

Today's mobile devices are equipped with sophis-ticated chain-of-trust mechanisms, able to successfully mitigate tampering of critical software components. However, this tech-nology, on the one hand, hinders the permanence of malware, thus raising the complexity for developing rootkits. On the other hand, the freedom of the end-user is limited. In fact, with all the security features enabled, one could not run any privileged code without it being signed by the Original Equipment Manufacturer; modifying any component of the root partition would cause a device read error and small modifications could be even rolled back automatically. Original Equipment Manufacturers typically provide mechanisms to (partially) disable these security features. However, they usually require two conditions: every unlock request must be approved by them, e.g. for warranty implications; secondly, to preserve the device security level, each time a security feature is disabled, the user data must be completely erased. We analyze several bootloader related vulnerabilities which allow to bypass these two requirements by exploiting design and implementation flaws in smartphones from different vendors. We then propose a novel architecture for secure device status storage and management. Our proposal relies only on commodity hardware features, which can be found on most mobile platforms. Furthermore, differently from many commercial implementations, we do not consider the storage device firmware as trusted, this makes our attack surface smaller than all of the examined alternatives.
SAT-based Exact Synthesis of Ternary Reversible Circuits using a Functionally Complete Gate Library

The problem of synthesis and optimization of reversible and quantum circuits have drawn the attention of researchers for the last two decades due to increasing interest in quantum computing. Although lot of works have been done on the synthesis of binary reversible circuits, very less works have been reported on the synthesis of ternary reversible circuits. Ternary circuits have lower cost of implementation as compared to their binary counterparts. However, the synthesis approaches that exist for ternary reversible circuits either use too many circuit lines (qutrits) or too many gates. Only one prior work has discussed the problem of generating cost-optimal ternary reversible circuits, but for a very restrictive gate library, which limits the approach to a specific subset of ternary reversible functions and often the solution becomes sub-optimal due to the imposed restrictions. The present paper overcomes that restriction, and uses multiple control ternary Toffoli gates with all possible ternary target operations as the gate library. This gate library is functionally complete and can be used to synthesize any arbitrary function. The proposed SAT-based synthesis approach provides low cost solutions in terms of the number of gates for any arbitrary ternary reversible function. Experimental results on various randomly generated permutations as well as standard ternary benchmarks establish this claim. The results can be used as template for other synthesis approaches by observing how far they deviate from the optimal solutions.
Evaluating Cryptographic Extensions On A RISC-V Simulation Environment

Due to the security requirement in the widely-deployed embedded applications, lightweight cryptographic ci-phers have been offered and used in resource-constrained devices in the last decades. In addition to the intrinsic low-cost properties of these ciphers, implementation-and architecture-specific techniques can make the implementation of these ciphers even more efficient. In this paper, we propose a simulation environment for the open-source RISC-V Instruction Set Architecture (ISA) implementing the base RISC-V ISA as well as the “bit manipulation” instruction set extension (ISE), which facilitates the imple-mentation of (lightweight) symmetric cryptography algorithms on resource-constrained devices efficiently. For demonstration pur-poses, we implement the lightweight block ciphers LEA, SIMON, and SPECK on our simulator and evaluate the performance of these ciphers on RISC-V architecture implemented with and without bit manipulation instructions. We define the performance of the lightweight ciphers as the total number of clock cycles required to encrypt one block of plaintext successfully. The performance of lightweight ciphers gives us an insight on how the performance of a cipher can be improved by using specific bit manipulation instructions. Our results show an average 38 % improvement in the total number of clock cycles required to run lightweight ciphers while using bit manipulation instructions.
Technology Mapping for PAIG Optimised Polymorphic Circuits

The concept of polymorphic electronics allows to efficiently implement two or more functions in a single circuit. It is characteristic of that approach that the currently selected function from the set of available ones depends on the state of the circuit operating environment. The key components of such circuits are polymorphic gates. Since the introduction of polymorphic electronics, just a few tens of polymorphic gates have been published. However, a large number of them exhibit parameters that fall behind ubiquitous CMOS technology, which makes their utilization for real applications rather difficult. As it turns out, the synthesis of polymorphic circuits achieves a significantly higher degree of complexity in comparison to the ordinary digital circuit. In past, many of the previously reported polymorphic circuits were designed using evolutionary principles (EA, CGP, etc.). It has been shown that the problem of scalable synthesis techniques suitable for large-scale polymorphic circuits could be addressed by the adoption of multi-level synthesis techniques such as And-Inverter-Graphs. The PAIG (Polymorphic And-Inverter-Graphs) concept and synthesis techniques based on it seem to be a promising approach. This paper shows how modern polymorphic gates could be used in combination with a PAIG-based synthesis tool to obtain an efficient implementation of complex polymorphic circuits.
METHOD OF CLASSIFICATION OF APPLICATIONS TRAFFIC OF COMPUTER NETWORKS ON THE BASIS OF MACHINE LEARNING UNDER UNCERTAINTY

The paper proposes a method for classifying applications of computer network traffic based on machine learning in conditions of uncertainty. Modern methods of classification of computer network traffic applications (such as the classification of transport layer protocols by port numbers) have significant shortcomings, which leads to and is the reason for the growth of research in the direction of classification of computer network traffic applications. The rapid growth in recent years of the types and number of transport layer network protocols increases the relevance of research in this area, the development of appropriate algorithms and methods for classifying applications of computer network traffic, which reduce computational complexity. At the present stage, the problem that needs to be urgently addressed is the classification of computer network traffic applications using appropriate protocols and encryption algorithms. A promising area of classification of computer network traffic applications is statistical methods, which are based on the analysis and identification of statistical characteristics of IP traffic. The most promising are the intellectual analysis of data flow, as well as machine learning technologies, which are currently widely used in related fields of science. The problem of research and training according to precedents is solved - classification of computer network traffic applications on the basis of pre-known set of attributes of their features, in order to improve the technical base of computer networks and theoretical base, while ensuring high performance and quality networks. example of using transport layer protocols (TCP / IP stack). The result of solving this problem is to assign the application, in accordance with the rules of the educational sample, to one of the outstanding classes, which are predetermined, which contains the relevant, but already classified applications. Statistical analysis and research of the attributes of Internet applications showed that the most important attributes associated with changes in the volume of Internet traffic flow are exponential. Fisher's criterion can be used to calculate anomalous changes in the amount of Internet traffic of applications to calculate averages. To classify Internet applications in data streaming mode, an algorithm for detecting the offset of the concept (drift) of data flow traffic is proposed for continuous data flow. Fisher's drift detector is based on the statistical characteristics of the attributes of Internet applications, analyzed using sliding windows that monitor changes in traffic current statistical characteristics of the attributes of applications.
A Discussion of Classifying Open Systems Problem and Automated Action Plan Selection

We discuss how to classify an open systems problem and automated action plan determination. In the current situation where problems of open systems are a regular occurrence, there is a strong demand for automation of failure action plans. First, we propose the way how to evaluate and classify all problems that happens in open systems. Next, we provide a way to link problem classes to unique action plan. This enables automated action plan determination. Finally, we analyze the relation of wrong DOA detection and inappropriate action plan determination.
International Financial Regulation of Cryptoassets and Asset-Backed Tokens

Cryptoassets have recently attracted the attention of national and international financial regulators. Since the mid-2010s blockchains have increasingly been adapted to automate and replace many aspects of financial intermediation, and by 2015 Ethereum had created the smart contract language that underpins the digitization of real assets as asset-backed tokens (ABTs). Those were initially issued by FinTech companies, but more recently banks active on international capital and financial markets, and even central banks, for example, the Bank of Thailand, have developed their own digital platforms and blockchains. A wide variety of real and financial assets underpins ABTs, viz., real-estate, art, corporate and sovereign bonds, and equity. Consequently, owing to the significant market capitalization of cryptocurrencies, the Basel Committee on Banking Supervision (BCBS) published two consultative papers delineating its approach on cryptoasset regulation. In this study, the authors analyze the mechanics of ABTs and their potential risks, relying on case studies of recent issuance of tokens in equity, real-estate, and debt markets, to highlight their main characteristics. The authors also investigate the consequences of the increasingly oligopolistic structure of blockchain mining pools and Bitcoin exchanges for the integrity and security of unregulated distributed ledgers. Finally, the authors analyze the BCBS’ regulatory proposals, and discuss the reaction of international financial institutions and cryptocurrency interest groups. The main findings are, firstly, that most ABTs are akin to asset-backed securities. Secondly, nearly all ABTs are “off-chain/on-chain,” that is, the underlying is a traditional asset that exists off-chain and is subsequently digitized. The main exception is the World Bank’s bond-i that is genuinely native to the blockchain created by the Commonwealth Bank of Australia, and has no existence outside it. Thirdly, all ABTs are issued on permissioned blockchains, where anti-money laundering/anti-terrorist funding and know-your-customer regulations are enforced. From a prudential regulatory perspective, ABTs do not appear to pose serious systemic risks to international financial markets. This may account for the often negative reactions of banks, banking associations, and cryptocurrency interest groups to the BCBS’ 2021 proposals for risk-weighted capital provisions for cryptoassets, which are viewed as excessive. Finally, we found that issuance of ABTS and other smart contracts on permissionless blockchains such as Bitcoin and Ethereum could potentially generate financial instability. A precedent involving Ethereum and The DAO in 2016 shows that (i) there is a significant accountability gap in permissionless blockchains, and (ii) the core developers of blockchains and smart contract technology, and Bitcoin mining pools, exercise an unexpectedly high- and completely unregulated-amount of power in what is supposedly a decentralized network.