Reviewerly/exHarmony huggingface dataset:
Analysis and Development of the Model for Google Assistant and Amazon Alexa Voice Assistants Integration

Voice assistants enable various task execution. In addition, voice assistants provide application programming interfaces for designing additional skills and intents. However, the problem is that each assistant defines its own programming interface and there is no interface compatibility. The aim of this research is the analysis and development of a software library for the integration of Google Assistant and Amazon Alexa voice assistants. In this context, similarities and differences were observed, and an API for the integration of voice assistants was defined accordingly. To verify the validity of the model, a project management software system was developed in the Jakarta Enterprise Edition environment. The software system supports interaction with Google Assistant and Amazon Alexa assistants.
LSTM Encoder Decoder Based Text Highlight Abstraction Method Using Summaries Extracted by PageRank

Automatic highlighting from texts is an abstractive summarization problem that is frequently focused on in natural language processing. In encoder-decoder architectures, developed for abstractive summarization, as the size of the input array increases, the learning ability of the architecture becomes difficult. To solve this problem, the focus is on minimizing this disadvantage of encoder – decoder architectures by using the Attention mechanism. In this study, we used an LSTM encoder – decoder with an attention mechanism to perform the highlight abstraction process. In addition, we used an extractive summarization step as a preprocess to increase the learning ability of the encoder – decoder architecture and reduce the input text size. We preferred the PageRank method in the extractive summarization process here. In the PageRank method, sentence vectors were extracted by using Glove embeddings to calculate similarities of text sentences. The proposed approach achived the extractive summarization by 67.6% and abstractive summarization by 59.6% in ROUGE-1 score.
Reservoir Splitting method for EEG-based Emotion Recognition

This paper presents a novel reservoir splitting method to train an efficient Reservoir Computing model for Emotion Recognition using Electroencephalogram (EEG) signals. Since different brain lobes have distinct functions and combining these functions results in the final decision, we propose splitting a single reservoir into multiple reservoirs dedicated to distinct lobes and integrating them to imitate the human brain functioning. We utilise the EEG signals from the publicly available GAMEEMO dataset for experiments. EEG signals are input to the single reservoir and proposed multiple reservoir configurations to obtain representations. Various classifiers: Ridge regression, Support Vector Machine (SVM), Gradient Boosted Classifier (GBC), and Random Forest are trained on the representations obtained from the reservoirs. We follow Leave-One-Subject-Out (LOSO) strategy to train these classifiers. Comparing the classification accuracy, we notice that representations from the proposed models of multiple reservoirs outperform the single reservoir model, and SVM performs better than the other classifiers. Furthermore, on increasing the reservoir size, the testing accuracy for all the classifiers attains a peak for both the Valence and Arousal domain. The proposed reservoir splitting method can be extended to explore diverse splitting configurations for future work.
Establishing Security Controls For Blockchain Technology In P2P Energy Trading

Blockchain technology creates a distributed ledger of transactions through interconnected blocks which are decentralized, transparent, immutable, and automated. The use of blockchain solutions and applications is growing rapidly which include finance, supply chain management, digital identity, energy, healthcare, and real estate. Due to the rapid economic recovery, global weather variations, maintenance delays caused by the pandemic, and earlier decisions by oil and gas companies to cut investments, energy costs have increased since 2021. Through limiting consumer prices and compensating energy providers for the shortfall, governments are aiming to lessen the impact of increase energy prices on citizens and businesses. The utilization of energy using Blockchain technology, where peer-topeer (P2P) energy trading on local and global energy markets could be enabled, can be an enabler to resolve energy shortage and increasing prices. Currently, National Information Security Standards and International Information Security Standards lack a framework to manage and govern information security controls for P2P energy trading using Blockchain technology. This paper proposes information security controls to complement existing Standards that will enable proper governance of P2P energy trading and discusses associated risks.
Inverse-Dynamic Neural Controller Simulation

The simulation process was provided in the system using an adaptive neural controller, performed on the basis of the inverse dynamics of the control object the train function using. It was established that an increase in the number of educational sequences taken in the form of smooth or jump-like functions provides a significant reduction in the duration of the adaptation procedure and an improvement in its convergence, and, therefore, effective tracking of the output value followed the input value.
A New Hybrid Method for Predicting Recommendations for Collaborative Recommender Systems

In this article analyzes the current state of models and methods for building recommender systems. The main classes of problems solving recommender systems are identified. The features of the application of the method of collaborative (general) filtering are shown. A mixed numerical-categorical clustering method has been developed to search for user groups using numerical rating and demographic characteristics of users; a hybrid method for searching user groups has been developed using the sparsity coefficient of the user-subject matrix.
Application of Global Optimization Toolbox for Identification of Parameters of Interval Nonlinear Models of Static Systems

The problem of parametric identification of interval models of static systems as mathematically is an optimization problem with a set of solutions - interval estimates of parameters. When the models are nonlinear functions relative to parameters, it is almost impossible to obtain a set of solutions in the form of interval parameter values. The article is proposed that in such cases, instead of a set of parameters, only one vector to calculate and an interval model of the static system to build based on its. At the same time, the problems of parametric identification of interval nonlinear models of a static system are reduced to the problem of minimizing the mean square deviation between the results of modeling the characteristics of the object and the values that belong to the numerical intervals of this characteristic that obtained experimentally. In this way, a multidimensional optimization problem with a nonlinear multi-extremal objective function is obtained. To solve this problem, we're proposed using the standard tools of MATLAB Global Optimization Toolbox. The effectiveness of the existing tools of MATLAB global optimization for solving this problem is shown.
Assistive Technical Communication of Montenegrin eServices: a Case Study

With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as digital literacy in the country is still in a developing phase in comparison with most countries of the EU, a higher portion of its citizens may rely on assistive technical communication. In this paper, we provide a case study on the assistive technical communication of Montenegrin eServices. The work addresses the sectors of online banking, healthcare services, higher education, national administrative services, and utilities. Our findings highlight the diversity in terms of implemented assistive technical communication, and distinguish usage contexts on the level of adequacy. Yet as the functionalities of Montenegrin eServices are expected to gradually expand in the upcoming years, assistive technical communication must aim to handle the higher extents of service complexity.
A Blockchain-Based Approach to Management of University Diploma Authenticity

This paper focuses on solving digital transformation challenges in educational system. In addition to traditional information systems employed in education there is a trend of adding new features based on the latest megatrends such as artificial intelligence and blockchain. Specifically, this paper discusses an approach on how blockchain technology can be utilized to implement a system for keeping student records and diploma verification. Including blockchain technology in the process of keeping records on students’ diplomas may bring many benefits. It provides students with control of their academic identity and provides means for displaying verified credentials in their electronic records, which may simplify later use of their electronic CVs or copies of diplomas. The use of blockchain should increase trust into electronic records to all of the stakeholders involved in the process and could in the near future minimize the possibility to modify or falsify student credentials.
Binary Watermark Application in Color Image Protection

The original concept of color image protection against unauthorized copying and distribution is presented in this paper. The protection is performed by inserting the binary image into the chrominance part of the carrier image using the block Singular Value Decomposition (SVD). The proposed algorithm belongs to the class of blind algorithms and solves the false positive problem. A binary image obtained by encoding the desired information with the Quick Response (QR) code is used as a watermark. In the experimental part of the work, the limit of the differential threshold T which determines the strength of watermark inserting, was determined. The results obtained in the experimental part of the work confirm the high quality of the proposed algorithm. The embedded watermark does not cause noticeable image degradation while ensuring error-free watermark extraction. The QR code can be used to encode various information about ownership or about the place and time the image was created. The presented algorithm can also be effectively used in copyright protection.
Vision-based Vehicle Speed Estimation Using the YOLO Detector and RNN

The paper deals with vehicle speed estimation using video data obtained from a single camera. We propose a speed estimation method which uses the YOLO algorithm for vehicle detection and tracking, and a recurrent neural network (RNN) for speed estimation. As input features for speed estimation, we use the position and size of bounding boxes around the vehicles, extracted by the YOLO detector. The proposed method is trained and tested on the recently proposed VS13 dataset. The experimental results show that the box position does not bring any improvement in the speed estimation performance. The proposed RNN-based estimator gives an average error of 4.08 km/h using only the area of bounding box as input feature, which significantly outperforms audio-based approaches on the same dataset.
A Software Complex for Researching Algorithms for Working with Graphs

A software complex has been developed for the study of algorithms for working with graphs. The complex is built on the basis of an object-oriented approach, which makes it relatively easy to include subsystems for solving various types of problems for working with graphs. One of the components of this software complex is the developed and implemented subsystem for solving the problem of finding the shortest path on a graph.
An Natural Language Processed Web Application that Interpret and Convert English to Python Code

As the exchange between natural language and program code gradually becomes the need of industry, more and more interpreters and translators are required. Such natural language interpreters and converters can benefit society in a variety of fields, such as service industry, communication industry, and engineering industry [6]. Concise and accurate language processors will greatly boost the productivity of bottom repetitive works, provide examples and inspirations for students and industry workers, and become the tendency of the future. This paper introduces an application using natural language processing and neural network to effectively interpret and translate English to Python code, and detailly present the structural flow of the application [7]. This paper will also introduce the structure of the neural network, its validity, and how the python torch was applied and integrated. Furthermore, it will demonstrate the application and limitation of this model as well as its future improvements. We applied our application to educational needs and conducted qualitative evaluation of the approach. The result shows a beneficial and potential effect that is applicable to a greater field.
Emotional Music Generation: An Analysis of Effectiveness and user Satisfaction by using Python and Dart

An issue that is prevalent in today’s society is the need for new music to be generated. More people are uploading videos and other forms of content to the internet through social media, and videos can often be enhanced by adding music to them [6]. However, creating music can be a time-consuming and expensive process. Therefore, an application was created that can generate music using emotions as inputs for the music generation model. To test how well the method of music generation through sentimental analysis works, an experiment was conducted that tests how accurately a sample of participants believe that the generated music was on a scale from one to ten [7]. According to the results of the experiment, the application appears to do fairly well at generating music that accurately represents the sentiment that was intended in the inputted message. A survey was also conducted to test user satisfaction when working with the application to generate music. The feedback from the participants indicated that they were generally satisfied with how well the generated music matched their intent in the inputted message, and they also seemed to be very satisfied with how convenient the application was to use and how intuitive the user interface was [8]. However, as the ratings for convenience were much higher than the ones regarding the effectiveness of the music generation itself, this may indicate that the application still has room for improvement when it comes to recognizing the sentiment of the inputted message.
Advance fee scams

Internet fraud, also known as online fraud, covers the umbrella of fraudulent actions, invitations, requests, notifications, and offers using the Internet. Internet fraud typically leverages personal information or money to exploit others. Criminals who commit Internet fraud use specific methods and techniques to defraud individuals or organizations, giving rise to various subtypes of fraud. "Get rich quick frauds" refer to a phenomenon wherein individuals, typically either "greedy" or naïve, are baited at the outset by fraudsters with promising offers on the premise of instant gratification. This study focuses on advance fee scam, in particular, Nigerian letter scams (419 scam) to illustrate a wide range of these "get rich quick frauds." This research explores the definitions, geographical patterns, profiles of victims and offenders, crime commission strategies, and prevention strategies for each type of fraud.
A robust and automatic CT‐3D ultrasound registration method based on segmentation, context, and edge hybrid metric

The fusion of computed tomography (CT) and ultrasound (US) image can enhance lesion detection ability and improve the success rate of liver interventional radiology. The image-based fusion methods encounter the challenge of registration initialization due to the random scanning pose and limited field of view of US. Existing automatic methods those used vessel geometric information and intensity-based metric are sensitive to parameters and have low success rate. The learning-based methods require a large number of registered datasets for training.The aim of this study is to provide a fully automatic and robust US-3D CT registration method without registered training data and user-specified parameters assisted by the revolutionary deep learning-based segmentation, which can further be used for preparing training samples for the study of learning-based methods.We propose a fully automatic CT-3D US registration method by two improved registration metrics. We propose to use 3D U-Net-based multi-organ segmentation of US and CT to assist the conventional registration. The rigid transform is searched in the space of any paired vessel bifurcation planes where the best transform is decided by a segmentation overlap metric, which is more related to the segmentation precision than Dice coefficient. In nonrigid registration phase, we propose a hybrid context and edge based image similarity metric with a simple mask that can remove most noisy US voxels to guide the B-spline transform registration. We evaluate our method on 42 paired CT-3D US datasets scanned with two different US devices from two hospitals. We compared our methods with other exsiting methods with both quantitative measures of target registration error (TRE) and the Jacobian determinent with paired t-test and qualitative registration imaging results.The results show that our method achieves fully automatic rigid registration TRE of 4.895 mm, deformable registration TRE of 2.995 mm in average, which outperforms state-of-the-art automatic linear methods and nonlinear registration metrics with paired t-test's p value less than 0.05. The proposed overlap metric achieves better results than self similarity description (SSD), edge matching (EM), and block matching (BM) with p values of 1.624E-10, 4.235E-9, and 0.002, respectively. The proposed hybrid edge and context-based metric outperforms context-only, edge-only, and intensity statistics-only-based metrics with p values of 0.023, 3.81E-5, and 1.38E-15, respectively. The 3D US segmentation has achieved mean Dice similarity coefficient (DSC) of 0.799, 0.724, 0.788, and precision of 0.871, 0.769, 0.862 for gallbladder, vessel, and branch vessel, respectively.The deep learning-based US segmentation can achieve satisfied result to assist robust conventional rigid registration. The Dice similarity coefficient-based metrics, hybrid context, and edge image similarity metric contribute to robust and accurate registration.
Smart Contract Security Vulnerabilities

A smart contract is a concept of computer protocols that helps to facilitate blockchain technology. This blockchain-based smart contract is a public ledger of all participating transactions. It is considered a self-executable application and contains predetermined rules. It also operates by decentralizing networks that are shared between all parties, and this execution of contracts between parties could be securely done without a middleman or a third party. With blockchain technology, developers could provide an efficient framework and ensure security issues. While the new blockchain has successfully been developed to prevent the problems of fraud and hacking, there is still a considerable risk concerning security and confidentiality. Therefore, we should not underestimate this matter. This study aims to review the potential risks that may take place on blockchain-based smart contracts. In addition, the options that may assist application developers in order to provide viable guidance, and to avoiding these security vulnerabilities.
The Performance of Distributed Applications: A Traffic Shaping Perspective

Widely used in datacenters and clouds, network traffic shaping is a performance influencing factor that is often overlooked when benchmarking or simply deploying distributed applications. While in theory traffic shaping should allow for a fairer sharing of network resources, in practice it also introduces new problems: performance (measurement) inconsistency and long tails. In this paper we investigate the effects of traffic shaping mechanisms on common distributed applications. We characterize the performance of a distributed key-value store, big data workloads, and high-performance computing under state-of-the-art benchmarks, while the underlying network's traffic is shaped using state-of-the-art mechanisms such as token-buckets or priority queues. Our results show that the impact of traffic shaping needs to be taken into account when benchmarking or deploying distributed applications. To help researchers, practitioners, and application developers we uncover several practical implications and make recommendations on how certain applications are to be deployed so that performance is least impacted by the shaping protocols.
Is Sharing Caring? Analyzing the Incentives for Shared Cloud Clusters

Many organizations maintain and operate large shared computing clusters, since they can substantially reduce computing costs by leveraging statistical multiplexing to amortize it across all users. Importantly, such shared clusters are generally not free to use, but have an internal pricing model that funds their operation. Since employees at many large organizations, especially Universities, have some budgetary autonomy over purchase decisions, internal shared clusters are increasingly competing for users with cloud platforms, which may offer lower costs and better performance. As a result, many organizations are shifting their shared clusters to operate on cloud resources. This paper empirically analyzes the user incentives for shared cloud clusters under two different pricing models using an 8-year job trace from a large shared cluster for a large University system.
A Method to Evaluate the Performance of Predictors in Cyber-Physical Systems

Cyber-Physical Systems (CPS) rely on sensing to control and optimize their operation. Nevertheless, sensing itself is prone to errors that can originate at several stages, from sampling to communication. In this context, several systems adopt multivariate predictors to assess the quality of the sensed data, to replace data from faulty sensors, or to derive variables that cannot be directly sensed. These predictors are often evaluated based on their accuracy and computing demands, however, such evaluations often do not consider the system's architecture from a broader perspective, ignoring the way components are interconnected and how they cascade as inputs of other Machine Learning (ML) models. In this work, we introduce a method to evaluate the performance of interdependent predictors based on the stability of the estimation error dynamics in faulty scenarios. The proposed method estimates the ability of a predictor to produce accurate predictions while accounting for the impacts of cascading predicted values as its inputs. The prediction correctness is estimated based solely on information acquired during the training of the multivariate predictors and mathematical properties of the ML activation functions. The proposed method is evaluated with a meaningful dataset in the scope of monitoring and control of a Cyber-Physical System, and the evaluation demonstrates the ability of the proposed method to account for the interdependence of data predictors.