Reviewerly/exHarmony huggingface dataset:
A Heterogeneous Solution to the All-pairs Shortest Path Problem using FPGAs

Heterogeneous systems present exciting new opportunities for graph and Machine Learning applications. This paper presents a novel approach for the All-pairs Shortest Path (APSP) computation using a heterogeneous CPU-FPGA Accelerator sys-tem. It is based on a recursive variant of Kleene’s APSP algorithm. Carefully re-engineering the algorithm to exploit parallelism in both the Floyd-Warshall algorithm and the general Kleene algorithm to perform Floyd-Warshall and Matrix-Multiply on the FPGA while the CPU efficiently balances the communication and computation between the kernels, improves state-of-the-art performance on FPGAs for APSP, while achieving near-GPU levels of performance, with less power and hardware resources, and out-performs the CPU-only solution by over 137x for a 8192x8192 problem size. When adjusted for power draw differences in process nodes, it also surpasses the GPU implementation in terms of performance per Watt by over 13%.
Conduct Security Control Testing

Security assessment and testing programs are the two main tasks of an information security team.
SP 800–22 and GM/T 0005–2012 Tests: Clearly Obsolete, Possibly Harmful

When it comes to cryptographic random number generation, poor understanding of the security requirements and “mythical aura” of black-box statistical testing frequently leads it to be used as a substitute for cryptanalysis. To make things worse, a seemingly standard document, NIST SP 800–22, describes 15 statistical tests and suggests that they can be used to evaluate random and pseudorandom number generators in cryptographic applications. The Chi-nese standard GM/T 0005–2012 describes similar tests. These documents have not aged well. The weakest pseudorandom number generators will easily pass these tests, promoting false confidence in insecure systems. We strongly suggest that SP 800–22 be withdrawn by NIST; we consider it to be not just irrelevant but actively harmful. We illustrate this by discussing the “reference generators” contained in the SP 800–22 document itself. None of these generators are suitable for modern cryptography, yet they pass the tests. For future development, we suggest focusing on stochastic modeling of entropy sources instead of model-free statistical tests. Random bit generators should also be reviewed for potential asymmetric backdoors via trapdoor one-way functions, and for security against quantum computing attacks.
A machine learning approach to modelling and simulating the behaviour of systems with deformable particles

The paper presents an approach to model and simulate a discrete system with deformable particles. The observed system is represented and considered as a system with concentrated parameters (particles) connected by elastic springs and damping. The system model thus created is used for learning the graphical neural network, which we intend to use to obtain the results of the simulation with less CPU power. The mathematical model of this system was implemented by creating a computer solution using the Java programming language and the OpenGL 3D graphics standard. Using this model, the problem analysis was performed and the simulation results are presented graphically.
Using Artificial Intelligence Assisted Learning Technology on Augmented Reality-Based Manufacture Workflow

The manufacturing process is defined by the synchronous matching and mutual support of the event logic and the task context, so that the work task can be completed perfectly, by executing each step of the manufacturing process. However, during the manufacturing process of the traditional production environment, on-site personnel are often faced with the situation that on-site advice is required, due to a lack of experience or knowledge. Therefore, the function of the manufacturing process should be more closely connected with the workers and tasks. To improve the manufacturing efficiency and reduce the error rate, this research proposes a set of manufacturing work knowledge frameworks, to integrate the intelligent assisted learning system into the manufacturing process. Through Augmented Reality (AR) technology, object recognition technology is used to identify the components within the line of sight, and the assembly steps are presented visually. During the manufacturing process, the system can still feedback to the user in animation, so as to achieve the function equivalent to on-the-spot guidance and assistance when a particular problem is solved by a specialist. Research experiments show that the operation of this intelligent assisted learning interface can more quickly recognize how the manufacturing process works and can solve problems, which greatly resolves the issue of personnel with insufficient experience and knowledge.
Face Expression Neutralisation Using Improved Variational Autoencoder

There is a high demand for realistic facial expressions in modern computer graphics and multimedia research. Unfortunately, synthesizing face expressions takes time, effort, and hard labour since high naturalism of face expressions is required. Despite recent advances in synthesizing realistic facial images, current generative models conflict to catch more involved image types, potentially due to their latent space simplified architecture. Furthermore, the union of a Generative Adversarial Network (GAN) with a Variational Autoencoder (VAE) by using usually the Adam optimizer has recently been studied and investigated in cutting-edge research. Hence, at tiem of learning the generator takes a long time and then overfit for a particular time instance. So, in this work, we will suggest integrating the VAE to GAN&#x2019;s architecture by using the Ranger optimizer into an unsupervised generative model that at once learns to encode, generate and compare dataset samples to bring out various real neutral faces in a reduced and optimal time. Next, we proposed the probability distributions called Kullback-Leibler Divergence (KLD) as an objective weighting scheme that helps us to measure just how much information we lose when we choose an approximation during training. Also, the Mean Square Error (MSE), the most commonly used loss function for regression is used as a metric of evaluation for the experiment tests.
A Linear Quadratic Regulator Based Speed Control for Remote-Controlled Racing Cars

This paper introduces a Linear Quadratic Regulator (LQR) based speed control for remote-controlled (RC) car racing. When it comes to RC car racing, the objective is to be the fastest by reducing the laps time. This means that the race driver model must be as fast as possible on the track while staying within the track's boundaries. In this paper, a linearized vehicle dynamics model is used to develop speed controller for regulating the car speed to the speed target which is generated based on a friction circle model. The friction circle model, which calculates the speed limit based on tyre grip and track curvature, is used to develop the speed target. The Proportional-Integral (PI) controller was also used to compare the speed control obtained by the LQR. Given that all trajectories are not equivalent in terms of time efficiency, this study of speed controllers is performed on an optimized trajectory which is a compromise between speed and distance. In comparison to a Proportional-Integral (PI) controller's performance, the speed regulated by LQR resulted in a time advantage of 1.58 seconds over four laps and a fastest lap of 25.926s on the fourth lap.
Semantic Mapping of Definitions for Constructing Ontologies of Business Processes

The paper grounds the topicality of developing natural language (NL) processing systems for constructing business processes models, in particular, their ontologies. It introduces an original algorithm of semantic mapping of the notions’ definitions in NL (on the example of English language). A definition in NL is transformed into its semantic representation being an expression of an SK-language (standard knowledge language). The class of SK-languages is introduced in the author’s theory of K-representations (knowledge representations). The suggested algorithm AlgSemDef1 is able to process the notions’ definitions including attribute clauses, infinitives, the logical connectives ‘not", "and’, "or".
Novel automatic traffic sign classification system using a semi-supervised approach

Automatic traffic sign classification plays a crucial role in identifying relevant signage that contributes to moving safely autonomous vehicles. Its application is manifold and could also be extended for driving assistance system solutions to help protect the driver and prevent automobile accidents. In this paper we develop an automatic traffic sign classification system using semi-supervised learning, a type of machine learning. The semi-supervised learning lies at the intersection between supervised and unsupervised learning by the fact that the training dataset contains both labeled and unlabeled data. It is a well-known problem that in practice collecting large amounts of labeled samples to train deep learning classifiers is time-consuming and expensive. The semi-supervised learning approach resolves these issues through the use of a partially labeled training dataset. In our experiments we used the SimCLR framework: we pretrained an encoder using the contrastive learning technique on a large set of unlabeled images, and we fine-tuned the encoder on the labeled images. The core idea behind contrastive learning is to learn an embedding space where the objective function minimizes the distance between representations of similar images and maximizes the distance between representations of different images. The dataset we used for training the traffic sign classifier is the German Traffic Sign Recognition Benchmark (GTSRB) dataset which contains 43 traffic sign classes with unbalanced class frequencies. Our work proposes the usage of a novel technique for performing traffic sign classification, semi-supervised learning using the SimCLR framework.
Porcellio scaber Algorithm with t-Distributed Elite Mutation for Global Optimization

As the convergence accuracy of the Porcellio scaber algorithm (PSA) is low, this study proposes an improved algorithm based on the t-distribution elite mutation mechanism. First, the improved algorithm applies a t-distribution mutation to each dimension of the optimal solution of each generation. Using the dominant information of the optimal solution and t-distribution characteristics, the result of the mutation is then employed as the updated location of the selected porcellio; thus, the algorithm enhances the ability to jump out of local extreme values and improves the convergence speed. Second, the updated iterative rule of PSA may lead to the loss of information of the elite porcellio in the last generation. To solve this problem, the judgment mechanisms of the current and previous optimal solutions are included in the algorithm process. Finally, dynamic self-adaptive improvement is applied to the weight allocation parameters of PSA. Simulation results on 24 benchmark functions show that the improved algorithm has significant advantages in convergence accuracy, convergence speed, and stability when compared with basic PSA, PSO, GSA, and FPA. This indicates that the improved algorithm has certain advantages in terms of optimization. An optimal solution with good practicability is obtained by solving three practical engineering problems: three-bar truss, welded beam, and tension/compression-spring design.
P‐101: Self‐Capacitive Ring Like Touch Sensor Design and Algorithm for OLED On‐Cell Touch Panel

In this paper, the self‐capacitive ring like touch sensor design and algorithm for OLED on‐cell touch panel is proposed by rearranging the touch sensor pad, and using hybrid algorithm to calculate touch position. Base on ring like touch sensor design and hybrid algorithm, we can improve the touch accuracy of ring region and realize sliding function on smart watch edge without real rotating bezel mechanical parts.
MedBuds: In-Ear Inertial Medication Taking Detection Using Smart Wireless Earbuds

Wireless earbuds are gaining in popularity these days, especially for smart mobile phone pairing. Some of these devices are getting smart as they embed motion sensors to monitor head and mouth movements. The embodiment of these sensors can enable important mobile health applications such as medication adherence monitoring. Existing solutions are often focused on capturing hand gestures associated with medication retrieval and thus they are inaccurate and do not detect med-ication ingestion. Other solutions use neck-worn systems which make them uncomfortable and socially unacceptable. In this paper, we present MedBuds, a smart system for medication-taking activity detection using earbud embedded IMUs and a pairing device (e.g., a smartphone). To evaluate our approach, we conducted preliminary experiments examining semi-medication-taking activities (i.e. swallowing) and non-medication-taking activities (speaking and chewing). Our results show the possibility of distinguishing between these activities with more than 84 % accuracy. We believe that by coupling MedBuds with other monitoring techniques (e.g. smart pill bottles), the overall performance of medication adherence monitoring systems can be improved.
AI-based Sound-Squatting Attack Made Possible

Domain squatting is an efficient attacking technique that relies on the similarity between domain names to trick users. Sound-squatting is a type of domain squatting that explores the similarity in the pronunciation of domains. Sound-squatting requires better approaches to protect users, and indeed it demands more research attention due to popularization of intelligent speakers and the increase of voice-based navigation. In this work we propose an AI-based methodology to automatically build sound-squatting candidates. We leverage recent results of AI, namely the ability to translate text, to automatically generate possible sound-squatting candidates. We evaluate our methodology by verifying the generated candidates and classifying them according to their threat class. We generate over twenty thousand candidates from popular domains, out of which, 7% are found active at the time of the analysis. Active domains include “Parked/Ads/For-Sale” domains. We thus show that automatic sound-squatting generation is useful to proactively check and limit the abuse of such offences.
Digital Competences of Music Culture Teachers during the COVID-19 Pandemic

The European Framework for the Digital Competence of Educators (DigCompEdu) is a framework describing what it means for educators to be digitally competent. This framework was the starting point for the research carried out in this paper regarding digital competences of Music Culture teachers of two Croatian counties, and special emphasis was put on the COVID-19 pandemic. The research results showed Music Culture teachers’ usage of the digital tools during COVID-19 pandemic and their familiarity with specialized digital tools such as Finale, Sibelius, Encore etc. According to the research results, almost half (47.6%) of Music Culture teachers included in the research used some of digital tools in every lecture. The majority of Music Culture teachers strongly advocated OERs – Open Educational Resources for Music Culture education, which they could easily adjust to and use in the teaching process. Also, the majority of Music Culture teachers were aware of the ongoing need for lifelong learning and professional training, especially regarding specialized digital tools for teaching Music Culture and the development of their digital learning strategies in a virtual or traditional environment.
DETECTION AND MONITORING MULTIPLE SHILLING ATTACK IN ONLINE MANAGEMENT SYSTEMS PREDICATED ON BISECTING K-MEANS CLUSTRING ALGORITHM

While existing methods for detecting shillings attacks in online recommendation system are efficient in detecting individuals’ offenders, they are not as effective at detecting group shilling operations. Using the bisecting K-means clustering technique, we offer a method for detecting coordinated shilling attacks. To begin, we take the ratings for each item and split them into groups based on a predetermined amount of time. Second, we suggest using the proportion of product concentration and usage data to determine the degree of suspicion around potential groupings. Research performed on the Netflix and Amazon data sets validate the superiority of the suggested strategy over the gold standard techniques. KEYWORDS: ORS, bisecting K-means clustering technique, shilling attacks, dataset
Constructing a Data-Driven Model of English Language Teaching with a Multidimensional Corpus

In this study, a multidimensional corpus English teaching model is constructed using the data-driven model. This study uses a data-driven collection of massive amounts of data to generate a multidimensional corpus. The data-driven generation of a multidimensional corpus to build a teaching model is studied, and the principle of data driven, the computational process, and the characteristics of the corpus are analyzed. Due to the deficiency of data-driven modeling without correlating process variables with quality variables, this study adopts an artificial intelligence algorithm and analyzes the basic principle, computational process, and advantages and disadvantages of the method. The model is simulated and verified for multidimensional corpus and English teaching. To address the shortcomings of the AI algorithm, which has a complex computation process and no orthogonal decomposition of the data space, the autoregressive latent structure projection algorithm is designed by integrating the autoregressive idea with the artificial intelligence (AI) algorithm. This algorithm can orthogonally decompose the sample data space and simplify the modeling process. Finally, the algorithm is validated by simulation. To verify the results of the teaching model, the fuzzy C-means clustering algorithm is combined with the autoregressive latent structure projection algorithm in this study. The sample data used in the modeling are divided into categories, and the affiliation function is calculated for each category. The affiliation function is used to calculate the affiliation of the online calculation results for each category, and the final evaluation results are obtained based on the fuzzy comprehensive evaluation method. Finally, taking junior students as an example, the simulation is carried out to verify the effectiveness of the English teaching model. The research results show that the corpus-based English flipped classroom teaching model improves English teaching methods, enhances students’ English proficiency and independent learning ability, and provides a practical basis for English teaching model exploration.
ESUM: an efficient UTXO schedule model

The current size of UTXO set in Bitcoin is large and continues to grow, resulting in high memory usage. This paper proposes an efficient UTXO schedule model to lower memory usage based on the observation of UTXO lifespan. Full nodes with the UTXO schedule model store the full UTXO set in disk database and schedule a subset of full UTXO set in memory, converting UTXO querying and accessing into multi-layer activities. Machine learning methods are applied to train a well-performed model based on historical transaction data and market trade data. Experiment results indicate that the UTXO schedule model could reduce nearly 90% memory usage compared to original Bitcoin nodes, allowing more nodes with different memory capability to participate in the blockchain.
On Some Cubic Exponential Sums

By numerical experiments, some unexpected structures in the distribution of cubic additive exponential sums in finite fields are discovered. A preliminary classification and some conjectures are presented.
A Web-Based Hybrid Blockchain Network Model for Securing University Academic Records

Data security is one of the top challenges facing most institution of higher learning that rely on internet services, these data use in making decisions are being stored using the centralized method of data storage system where a resource system feeds other systems/devices connected to it within the same network. As this method has a single point of failure, which when attacked cripples or disrupts the activities of the institution. When students’ vital information such as results stored therein are compromised, unmerited class of degrees may be awarded to them at the point of graduation. This keeps the integrity of the university at stake, as the quality of graduates being produced will be questionable. This research tends to develop a web-based hybrid blockchain network to secure this sensitive information of an institution by creating a decentralized and transparent system using nodes in its operations. Software as a Service (SaaS) platform which is powered by APIrone was used in this design adopting objected-oriented and design methodology. This research has shown that blockchain technology is not only used in cryptocurrency, but can also be used by institutions through the creation of a hybrid blockchain with nodes as it is not possible to attack all the nodes at a time.
Methodological features of tourist and land knowable activity in general secondary education institutions

The purpose of the work is to systematization and analysis of literature data reveals the organizational and methodological foundations of tourism and local lore work of schoolchildren, which are effective, efficient and very powerful means of educating a comprehensive and harmoniously developed creative personality of schoolchildren. The results of modern scientific research show that a significant amount of empirical and theoretical and methodological material has been accumulated, which shows that the use of various forms of tourism and local lore provides a comprehensive nature in the education, upbringing and rehabilitation of schoolchildren. The study of psychological and pedagogical foundations of tourist and local lore work is primarily related to improving the educational process of secondary school, systematization of content, forms and main tasks of tourist and local lore work of schoolchildren. On the basis of the conducted research it is established that tourist and local lore activity has an effective contribution to the development of personal potential of students; is an important direct extracurricular education and upbringing. It is aimed at involving children in active activities to study the history of the native land, environment, geography, historical places and phenomena of public life and the formation of a fully educated personality.
Dynamic Generator of Permissible Trajectories for UAVs

Deterministic non-smooth reference actions are fed to the unmanned aerial vehicle control system in real-time from an autonomous source. To process them, we have designed a generator with sigmoidal controls in the form of a dynamic equation of motion model. The generator smooths out the reference signal in a natural way and generates realizable trajectories for the control plant.