Reviewerly/exHarmony huggingface dataset:
Stabilization enhancement of the ball on the plate system (BOPS) based on Takagi-Sugeno (T-S) fuzzy modeling

After applying the ball on plate stabilization system in practice, a big problem appeared, which is that the servo motors used must be very fast and have large-angle but these motors are rare and expensive Therefore, commonly used servo motors that have large angles and low speed are used in the practical experiment, and this makes the angle of the plate large and this increases the nonlinearity in this system, Therefore, a model was developed that deals with the large nonlinearity in the system, depending on Takagi-Sugeno (T-S) fuzzy to improve the tracking and stabilization of the ball on the plate system (BOPS) and this model not presented in any scientific paper before. This paper introduces the mathematical model of the two degree of freedom BOPS using state space and modeling through Takagi-Sugeno (T-S) fuzzy. The T-S fuzzy is utilized for improving the controller performance by aiding the state feedback controller. The determination of the gains of the controller is performed through the linear quadratic regulator (LQR) method. The proposed controller gives more precisely tracking the desired trajectory of the ball position.
An Extensible Compiler for Implementing Software Design Patterns as Concise Language Constructs

Design patterns are generic solutions to common programming problems. Design patterns represent a typical example of design reuse. However, implementing design patterns can lead to several problems, such as programming overhead and traceability. Existing research introduced several approaches to alleviate the implementation issues of design patterns. Nevertheless, existing approaches pose different implementation restrictions and require programmers to be aware of how design patterns should be implemented. Such approaches make the source code more prone to faults and defects. In addition, existing design pattern implementation approaches limit programmers to apply specific scenarios of design patterns (e.g. class-level), while other approaches require scattering implementation code snippets throughout the program. Such restrictions negatively impact understanding, tracing, or reusing design patterns. In this paper, we propose a novel approach to support the implementation of software design patterns as an extensible Java compiler. Our approach allows developers to use concise, easy-to-use language constructs to apply design patterns in their code. In addition, our approach allows the application of design patterns in different scenarios. We illustrate our approach using three commonly used design patterns, namely Singleton, Observer and Decorator. We show, through illustrative examples, how our design pattern constructs can significantly simplify implementing design patterns in a flexible, reusable and traceable manner. Moreover, our design pattern constructs allow class-level and instance-level implementations of design patterns.
The Mobile Office: A Mobile AR Systems for Productivity Applications in Industrial Environments

This article proposes the Mobile Office, an AR-Based System for productivity applications in industrial environments. It focuses on addressing workers’ needs while mobilizing in complex environments and when workers want to do productive work similar to a workstation setting. For this purpose, the Mobile Office relies on a bi-modal approach (mobile and static modes) to addresses both scenarios while considering critical aspects such as UI, ergonomics, interaction, safety, and cognitive user abilities. Its development relies first on a systematic literature review for establishing guidelines and system aspects. Then AR-UI mockups are presented to incorporate literature and authors’ expertise in this area. Three hardware configurations are also presented for the implementation of the Mobile Office. Validation of the Mobile Office was made through a survey to potential users that provided their feedback about their perceptions of proposed system. The survey results revealed concordance of critical aspects of particular importance for effective productivity support on the Mobile Office’s focus scenarios. We incorporated these critical aspects and suggested future research direction for the Mobile Office’s further development and significance.
A Research Perspective on Security in Fog Computing Through Blockchain Technology

Most modernistic applications comprising of the Internet of Things are deployed on a cloud environment, which offers advantages like virtual infinite resources, massive elasticity of resources on a pay as per usage metric model. Additionally, IoT real-time applications, essentially self-driving cars, smart home security systems etc., require low latency and less response time. To achieve that, the computation and analysis of data needs to be deployed closer to the edge of the network. The extension of computing services to the edge nodes is termed as 'Fog Computing'. Since every technology comes with two-faced implications; fog computing also suffers with few security issues, majorly comprising user authentication, access control, and data protection from security attacks. Blockchain technology is a promising paradigm to address these security flaws. In this paper, an infrastructure based on blockchain technology coupled with fog computing is proposed as a measure to mitigate the security attacks.
Specifying with Interface and Trait Abstractions in Abstract State Machines: A Controlled Experiment

Abstract State Machine (ASM) theory is a well-known state-based formal method. As in other state-based formal methods, the proposed specification languages for ASMs still lack easy-to-comprehend abstractions to express structural and behavioral aspects of specifications. Our goal is to investigate object-oriented abstractions such as interfaces and traits for ASM-based specification languages. We report on a controlled experiment with 98 participants to study the specification efficiency and effectiveness in which participants needed to comprehend an informal specification as problem (stimulus) in form of a textual description and express a corresponding solution in form of a textual ASM specification using either interface or trait syntax extensions. The study was carried out with a completely randomized design and one alternative (interface or trait) per experimental group. The results indicate that specification effectiveness of the traits experiment group shows a better performance compared to the interfaces experiment group, but specification efficiency shows no statistically significant differences. To the best of our knowledge, this is the first empirical study studying the specification effectiveness and efficiency of object-oriented abstractions in the context of formal methods.
Towards Math Terms Disambiguation Using Machine Learning

Word disambiguation has been an important task in natural language processing. However, the problem of disambiguation is still less explored in mathematical text. Similar to natural languages, some math terms are not assigned a unique interpretation. As math text is an important part of the scientific literature, an accurate and efficient way of performing disambiguation of math terms will be a significant contribution. In this paper, we present some investigations on math-term disambiguation using machine learning. All experimental data are selected from the DLMF dataset. Our experiments consist of 3 steps: (1) create a labeled dataset of math equations (from the DLMF) where the instances are (math token, token meaning) pairs, grouped by equation; (2) build machine learning models and train them using our labeled dataset, and (3) evaluate and compare the performance of our models using different evaluation metrics. Our results show that machine learning is an effective approach to math-term disambiguation. The accuracy of our models ranges from 70% to 85%. There is potential for considerable improvements once we have much larger labeled datasets with more balanced classes.
Toward Smart Contract and Consensus Mechanisms of Blockchain

This chapter presents a detailed description of smart contracts and proof-based consensus mechanisms used in blockchain technology. Smart contracts are used in blockchain technology while making the transaction between two parties, and it enables only the validated transaction to be included in the blockchain. However, to validate a transaction Satoshi Nakamoto, who is the inventor of blockchain, introduced a Proof-of-Work (PoW) consensus algorithm while performing transactions among blockchain nodes (i.e., users). Later on, various proof-based consensus mechanisms (e.g., Proof-of-Stake (PoS), Proof-of-Location (PoL), PBFT (Practical Byzantine Fault Tolerance)) are proposed. The main concept of applying proof-based algorithms is that the nodes within the blockchain network that performs and exhibits sufficient proof will get the privilege to append a new block to the main chain and collect the reward. In this chapter, we present a clear explanation of smart contracts and discuss some of the most important consensus algorithms of blockchain that are widely used.
On McEliece-Type Cryptosystems Using Self-Dual Codes With Large Minimum Weight

One of the Round 3 Finalists in the NIST post-quantum cryptography call is the Classic McEliece cryptosystem.Although it is one of the most secure cryptosystems, the large size of its public key remains a practical limitation.In this work, we propose a McEliece-type cryptosystem using large minimum distance error-correcting codes derived from self-dual codes.To the best of our knowledge, such codes have not been implemented in a code-based cryptosystem until now.Moreover, we modify the decryption step of the system by introducing a decryption algorithm based on two private keys.We determine the parameters of binary codes with large minimum distance, which, if implemented into a McEliece-type cryptosystem, would provide a security level respectively of 80, 128, and 256 bits.For the 80-bit security case, we construct a large minimum distance self-dual code of length 1 064, and use it to derive a random punctured code to be used in the corresponding McEliece-type cryptosystem.Compared to the original McEliece cryptosystem, the key size is reduced by about 38.5%, although an optimal decoding set is yet to be constructed to make the new system fully defined and usable.
Continuous Build Outcome Prediction: A Small-N Experiment in Settings of a Real Software Project

We explain the idea of Continuous Build Outcome Prediction (CBOP) practice that uses classification to label the possible build results (success or failure) based on historical data and metrics (features) derived from the software repository. Additionally, we present a preliminary empirical evaluation of CBOP in a real live software project. In a small-n repeated-measure with two conditions and replicates experiment, we study whether CBOP will reduce the Failed Build Ratio (FBR). Surprisingly, the result of the study indicates a slight increase in FBR while using the CBOP, although the effect size is very small. A plausible explanation of the revealed phenomenon may come from the authority principle, which is rarely discussed in the software engineering context in general, and AI-supported software development practices in particular.
Validation of ADS-B Aircraft Flight Path Data Using Onboard Digital Avionics Information

The adoption of Automatic Dependent Surveillance-Broadcast (ADS-B) transponders has given researchers the ability to capture and record aircraft position data. However, due to the ADS-B system's characteristics, missing data may occur due to propagation anomalies and suboptimal aircraft orientation with respect to the ground-based receiver. The nature of general aviation operations exacerbates this problem. As a result, it may be difficult to accurately review a general aviation aircraft’s flight path with an adequate level of precision. To mitigate this, a five-dimensional modified Unscented Kalman Filter (UKF) was developed to produce statistically optimal aircraft position approximations during all flight phases. The researchers validated the UKF algorithm by comparing estimated flight paths to flight data logs from the Garmin G1000 flight instrument systems of Piper Archer aircraft used in flight training operations on February 23, 2021 at the Purdue University Airport (KLAF). Root mean square error (RMSE) was used to measure the filter’s accuracy. The filter was found to accurately compensate for missing data. This research details the formulation, implementation, and validation of the filtering algorithm.
Efficacy of Moriya interaction to free the bound entangled state

The current work shows the efficacy of Dzyaloshinshkii–Moriya interaction to free the bound entanglement. Based on the work (Sharma et al. in Quant. Info. Proc. 15:1539 2016), we present further results in two qutrits bound entangled state proposed by Jurkowski et al. We consider a closed system of two qutrits and an auxiliary qutrit which interacts with either one of the two qutrits in a closed system. We erase the auxiliary qutrit from the system by doing partial trace operation and open system dynamics has been studied. We have found, the probability amplitude of auxiliary qutrit does not affect the system, while DM interaction plays a major role to govern the dynamics. The realignment and CCNR criteria have been used to detect the bound entanglement in the state, while for quantification of entanglement the negativity has been used. We explored the dynamics with all the possible cases of Jurkowski et al. bound entangled state.
Computing Riemann–Roch spaces via Puiseux expansions

Computing large Riemann–Roch spaces for plane projective curves still constitutes a major algorithmic and practical challenge. Seminal applications concern the construction of arbitrarily large algebraic geometry error correcting codes over alphabets with bounded cardinality. Nowadays such codes are increasingly involved in new areas of computer science such as cryptographic protocols and "interactive oracle proofs". In this paper, we design a new probabilistic algorithm of Las Vegas type for computing Riemann–Roch spaces of smooth divisors, in characteristic zero, and with expected complexity exponent 2.373 (a feasible exponent for linear algebra) in terms of the input size.
Track‐to‐track association algorithm for passive multisensor system based on trajectory parameter

Asynchronous sampling and the ghost point are significant issues in the track-to-track association (TTTA) for passive multisensor system. A TTTA algorithm is proposed based on a trajectory parameter to address these issues. According to advantages in which the trajectory information can represent the target motion state and will not change with the sampling time, we turn the TTTA problem into the trajectory parameter matching problem. First, a TTTA model is established by the heading angle and the target velocity. Then, an estimation of target velocity that is suitable for asynchronous sampling is modelled using trajectory parameters from different sensors. Furthermore, to avoid the ghost point in the data association, we use only the bearing measurement of a single sensor to derive the trajectory parameters. Finally, an assignment method is adopted to determine the correspondence between different tracks. The simulation results demonstrate the effectiveness of the proposed method compared with competing algorithms.
Disadvantaged by Disability: Examining the Accessibility of Cyber Security

Today, we are living in a digitally dependent world. Through the use of digital technologies, life is meant to be easier and streamlined. This includes giving access to services that previously were unavailable to many due to disability. Although technology has evolved immensely over the past few decades, reducing the digital divide, authentication methods have changed very little. Authentication is the forefront of securing users’ information, services and technology, yet for many it still poses issues in terms of usability and security, due to specific characteristics of different disabilities. In this paper, drawing upon a literature review, a review of recognized disabilities, and the results of a small questionnaire study, we review the current authentication methods and discuss the potential issues that users with different disabilities face when interacting with these methods. We identify the specific aspects of disabilities that lead users to struggle to authenticate themselves. The results of this study lead to several recommendations, and suggestions for extending the existing inclusive technology framework to the authentication context. Through extending the framework, this could guide the development of future technologies, systems and services, ensuring that they are not only digitally inclusive in their function, but digitally inclusive in their security.
Chinese Emotional Dialogue Response Generation via Reinforcement Learning

In an open-domain dialogue system, recognition and expression of emotions are the key factors for success. Most of the existing research related to Chinese dialogue systems aims at improving the quality of content but ignores the expression of human emotions. In this article, we propose a Chinese emotional dialogue response generation algorithm based on reinforcement learning that can generate responses not only according to content but also according to emotion. In the proposed method, a multi-emotion classification model is first used to add emotion labels to the corpus of post-response pairs. Then, with the help of reinforcement learning, the reward function is constructed based on two aspects, namely, emotion and content. Among the generated candidates, the system selects the one with long-term success as the best reply. At the same time, to avoid safe responses and diversify dialogue, a diversity beam search algorithm is applied in the decoding process. The comparative experiments demonstrate that the proposed model achieves satisfactory results according to both automatic and human evaluations.
Fast diffraction pathfinding for dynamic sound propagation

In the context of geometric acoustic simulation, one of the more perceptually important yet difficult to simulate acoustic effects is diffraction, a phenomenon that allows sound to propagate around obstructions and corners. A significant bottleneck in real-time simulation of diffraction is the enumeration of high-order diffraction propagation paths in scenes with complex geometry (e.g. highly tessellated surfaces). To this end, we present a dynamic geometric diffraction approach that consists of an extensive mesh preprocessing pipeline and complementary runtime algorithm. The preprocessing module identifies a small subset of edges that are important for diffraction using a novel silhouette edge detection heuristic. It also extends these edges with planar diffraction geometry and precomputes a graph data structure encoding the visibility between the edges. The runtime module uses bidirectional path tracing against the diffraction geometry to probabilistically explore potential paths between sources and listeners, then evaluates the intensities for these paths using the Uniform Theory of Diffraction. It uses the edge visibility graph and the A* pathfinding algorithm to robustly and efficiently find additional high-order diffraction paths. We demonstrate how this technique can simulate 10th-order diffraction up to 568 times faster than the previous state of the art, and can efficiently handle large scenes with both high geometric complexity and high numbers of sources.
Introduction into Autonomous Mobile Robot Research and Multi Cooperation

This paper is intended as an introduction into the research area of Autonomous Mobile Robot (AMR). It describes in first part basic design and usage of AMR with related problems and already used solutions and strategies to solve these problems. Second part is dedicated to cooperation among multiple AMRs. Architecture design, communication among AMRs and algorithms which could be used to solve path planning problems.
IOT-Based Personalized products recommendation system

Abstract Recommendation technology is an essential component of the Internet of Things (IoT) services that can help users get information at any time and from any place. Traditional recommendation algorithms, on the other hand, are unable to satisfy the IoT environment’s swift and reliable recommendation criteria. The use of mathematical and information discovery methods to overcome the relationship with target consumers in order to have desired items is known as a recommendation system. In this paper, a recommendation algorithm based on collaborative filtering is proposed. In this sense, the recommendation method (Recommender Systems) was developed; it is focused on the user’s characteristics, such as hobbies, and it is recommended to satisfy the object’s user specifications, also known as customized recommendation system (Personalized Recommender Systems), The majority of modern e-commerce recommender programs tend to recommend the best goods to a customer, believing that each product’s properties remain constant. Some properties, such as price discounts, can, however, be customized to respond to the preferences of each customer..
Block-based Learned Image Coding with Convolutional Autoencoder and Intra-Prediction Aided Entropy Coding

Recent works on learned image coding using autoencoder models have achieved promising results in rate-distortion performance. Typically, an autoencoder is used to transform an image into a latent tensor, which is then quantized and entropy coded. Based on a work by Ballé et al., we adapted the autoencoder with a hyperprior model to code images in a block-based approach. When the autoencoder model is directly applied to code small image blocks, spatial redundancy in the larger image cannot be fully utilized, resulting in a decrease in ratedistortion performance. We propose a method to utilize border information in the entropy coding of latent and hyper-latent tensors, which has achieved promising results. We show that using intra-prediction to help entropy coding is more effective than applying a convolutional autoencoder with hyper priors to intra-prediction residual blocks.