Reviewerly/exHarmony huggingface dataset:
Causal Knowledge Modelling for Agile Development of Enterprise Application Systems

Experience shows that Agile project management tools such as Atlassian Jira capture the state of EAS projects by relying solely on expert judgement that is not supported by any knowledge model. Therefore, the assessment of project content against strategic objectives and business domain features are not supported by any tool. This is one of the reasons why Agile project management still does not provide sufficient EAS project delivery results. In order to address this problem, the Enterprise Application Software (EAS) development using Agile project management is summarized in a conceptual model. The model highlights the knowledge used and indicates its nature (empirical or causal digitized). The modified Agile management process we have developed and described in previous works is based on causal knowledge models that supports EAS development and Agile management processes. The purpose of this article is to specify knowledge repository to ensure the Agile management solutions of an EAS project are aligned with strategic goals and business domain causality. It is worth noticing that strategic goals have been identified and specified as capabilities using some enterprise architecture framework (NAF, MODAF, ArchiMate, etc.). The novelty of the proposed method is incorporating the business domain causal knowledge modelling approach into the Agile project management process. The causal knowledge unit is considered as a Management Transaction (MT), which includes closed loop dependence of its components. The modified Agile activity hierarchy (theme, initiative, epic, user story) defines the required content of their mutual interactions. An important new results obtained are the conceptual model of causal knowledge base (KB) and specification of enhanced Agile management tool components: project management database and project state assesment knowledge base. Causal KB includes specification of causal knowledge unit (MT metamodel) and specifications of traditional and causal Agile hierarchy meta-models. These conceptual models define the causal knowledge components necessary to evaluate the state of Agile activities in the EAS development project using intelligent Agile project management tool.
Physical Modeling of Transport Processes—Simulation Modeling, Training Complexes

The high complexity of transport processes.
Addressing the Challenges of Biological Passport Through Blockchain Technology

A biological passport collects the physiological parameters of athletes, through various blood and urine tests over a period of time. This passport includes two modules: the hematological and the endocrine (which also includes the steroidal data). A mathematical algorithm is applied to these data to calculate the profile of the person. This profile establishes the range of values within the biological controls and tests should move and if there were any anomaly in order to detect doping since the normal limits are exceeded. This work describes a distributed registration method based on blockchain technology to provide a solution to the previous challenges with clear benefits for all participants. The purpose of the proposed blockchain network is to consolidate a tamper-proof and distributed registry database, where anti-doping processes can be implemented through the development of distributed applications and smart contracts. Blockchain technology is a promising disruptive technology to address government policies and access control rules by a consortium of sports organizations in order to conduct the continuous doping controls and health revisions of athletes.
An Interactive Augmented Reality Tool to Enhance the Quality Inspection Process in Manufacturing—Pilot Study

Quality inspection processes are an essential part of most industrial systems. These are repetitive and precise operations that are often very complex and require multiple steps to be performed correctly by different inspectors or operators. Augmented Reality (AR), one of the most promising and enabling technologies for assisting employees and engineers in the manufacturing workplace, has the potential to help operators to better focus on tasks while having virtual data at their disposal. Therefore, it is important to verify whether, with the support of this technology, it is possible to help workers perform these activities faster, more efficiently, and with less mental effort than with traditional paper-based documents. This paper describes a pilot interactive AR tool designed to support quality controllers in their inspection of welded products in the manufacturing environment. This tool is designed to guide the employee through the inspection process, provide them with all relevant information and help them find any discrepancies or deviations. The presented AR tool is created with the game engine Unity 3D and SDK Vuforia to assure compatibility with commonly used devices, such as tablets or smartphones. It does not use markers, but uses the object tracking method instead. A pilot study was conducted with a group of five probands to test the usability and functionality of the solution using SUS (System Usability Scale) standard questionnaires. The average SUS value rated by the probands was 78, confirming both a high level of usability and user satisfaction.
Stability and Availability Optimization of Distributed ERP Systems During Cloud Migration

Enterprise resource planning is a powerful software package that enables businesses to integrate a variety of disparate functions, it is a concept whereby an organization enables effective communication and data exchange within the business units and associated third parties such as vendors and banks. SAP is the de-facto ERP software for a company that manages business operations and customer relations. Many organizations have started migrating their ERP to infrastructure-as-a-service (IAAS) providers like amazon web services and Microsoft Azure. Though there is a significant increase and positive sentiment to migrate productive workloads to the cloud, the availability and reliability of applications remain a huge concern. No comprehensive study specifically examines the stability and availability of productive ERP workload in the cloud with large databases. Cloud brings specific advantages like faster time-to-value, moving out of expensive hardware, ease of spinning up virtual machines, and accessibility of the latest technologies in one place. There is a need for a study to specifically look into moving productive ERP workload to the cloud and a roadmap to achieve optimum stability and availability while leveraging all the advantages we get from cloud computing. The paper’s objective is to take in the experience of migrating several productive ERP landscapes with a database size ranging from 2 TB to 20 TB and propose a design roadmap to attain optimum Stability and 99.9% Availability. The design was implemented and measured for its effectiveness post-migration by comparing the application with the data before migration. SAP pre-go-live and post-go-live reports show a 33% reduction in dialog response time and a 40% improvement in response time during peak hours. The overall database grew from 8449 GB to 8703 GB during the analysis. When we extrapolate the analysis to 21 days post-migration, we see that the response time improves even further. We see 100% applications available during the analysis period.
Cybersecurity of Distributed Systems and Dispersed Computing

This paper will describe the most important aspects of cybersecurity systems oriented for distributed communication as well as dispersed computation. Especially the most important ways how to guarantee the highest level of security will be describes, and issues how to prevent cyberattacks. Especially human factors which have influence for general security levels also will be considered.
Multi-agent Deep Q-Learning Based Navigation

Navigating an unknown indoor environment like a building is quite challenging as Global Positioning System-based solutions are hard or near impossible. However, it is a crucial problem as it has many applications, such as search and rescue. The rescue operations should start at the earliest to avoid casualties. Hence, the navigation process should be completed quickly. The navigation process can be done and accelerated with multiple agents/robots. The Q-learning-based approach can help navigation, but it is suitable only if state and action spaces are low. This paper suggests a distributed multi-agent deep Q-learning algorithm for indoor navigation in a complex environment where the number of states is large.
A Proposed Approach to Detect Incident and Violation Through CCTV Using Convolutional Neural Network

It is very challenging to predict a crime scene only by machine without human intervention. This research has tried to make that possible. Convolutional neural network (CNN) has been used to detect 4 objects which are handgun, fire, knife, and accidents. By detecting these objects easily with the help of CCTV cameras, the machine can predict the crime scene. Machines will be able to identify crimes swiftly and intervene based on situations like accidents or violence. This research has adopted a variety of techniques to reach the pinnacle of implementation and success. The model used here has been built with the help of CNN, and there are 4 objects to classify which are mentioned earlier. This research has succeeded in predicting crime scenes through CCTV cameras which may bring prosperity to the country and the nation.
A CNN-Based Underage Driver Detection System

The road accidents caused by driving of young and inexperienced drivers have been increasing day by day. Due to the rise in catastrophic accidents, there have been several serious injuries and damages. Therefore, age detection utilizing deep learning is established here in order to alert and stop these massive calamities. In this system, the CNN model is used to predict the age from a face image, and the age classifications task is developed as a classification problem. By predicting the age from the faces and barring the children from driving, the proposed method aids in keeping our children safe. Here first detect faces from the input video stream, then extract the face Region of Interest. Then, in order to estimate the age of a person, an age detector algorithm is employed here. In this system for face detection, Open CV’s DNN module and DNN face detector are used. In this proposed system, the face is detected from the real-time video frames via a web camera. Then predict the age of the person from the face. If minor aged person is detected, then car cannot be started by that person and the system alert the guardians by alarm. If the detected person’s age is above underage, then the car will start automatically. The system will also send an alerting SMS to the guardians using GSM module. The proposed system is intended to identify underage drivers and stop them from operating a vehicle.
Reifying Kintsugi Art in Post-covid Era: A Remote Smart Working Model, Augmented Intelligence-Based, for Antifragile Companies

The aim of this conceptual paper is to understand if augmented intelligence may be considered a driver of antifragility that can be allegorically represented by the Japanese art of Kintsugi, which consists of the use of gold or silver to repair broken objects in ceramic to get a better aesthetic form. Covid-19, like a black swan, represented, for many companies, understood as systems, a complex situation capable of upsetting their equilibrium. It had thus forced them to accelerate the digitization process. Digitalization, based on artificial intelligence (AI) tools, brings in many fields new perspectives, such as new business scenarios and models. By using the Viable System Approach (vSa) lens, we investigated the impact of smart working, widely spread to manage a complex situation (Covid-19), in allowing companies to cope with changes and to be antifragile. A remote smart working model is proposed, as an evolution of smart working, based on a new culture of “doing business” to search for new viable conditions. It can allow companies a more efficient resources management, an endless orientation towards results, but also new synergies in new contexts thanks to new and increased networks, for new collaborations and new forms of interactions, as well as more profitable relationships with employees, based on a strong relationship of trust and on better opportunities for work-life balance.
Nexus: Proxy Service for the Web of Things

The W3C Web of Things (WoT) architecture is a model for handling Things (i.e. devices) as Web pages. The WoT proxy is a central building block of the architecture. Its primary role is to translate the devices’ communication protocols to HTTP(S) so that they can connect to the application server (in the cloud). Nexus promotes this idea even further and proposes that the WoT proxy be converted into a Multi-Context Service Oriented Architecture (SOA) for all device-specific operations that would otherwise be performed on the application server. The advantage is the decoupling of the Proxy and application server roles. WoT proxy becomes application-independent. Different applications can use Nexus as a single point of access to their network devices. Nexus can manage users, devices, and the environment of various applications according to their roles and access rights. Experimental results show that Nexus scales well and responds in real time under heavy workloads.
Design of a High-Speed and Low-Power AES Architecture

Data security is becoming a major concern in recent years due to the proliferation of interconnected devices. With the number of devices connected to the Internet of things (IoT) growing at a very rapid pace, design of speed and power efficient crypto systems is very essential. This paper explores the design of a high-speed and low-power advanced encryption standard (AES) architecture to be used as a hardware accelerator in various cryptographic systems. The use of Rijndael S-Box for byte substitution, merging of ShiftRows operation with the State Register updation, implementation of MixColumns transformation through substructure sharing, and clock gating methods are investigated in this work for obtaining a high-speed device with low-power consumption. The proposed design is designed using Verilog and co-simulated in MATLAB and implemented as an ASIC using 90 nm GPDK technology. It is seen that the proposed design has 83% increase in speed, 39% decrease in area, and 82% reduction in power dissipation as compared to a standard AES design implementation.
Behaviors of Modern Game Non-playable Characters

The recent trends in the gaming world have been more inclined to shooter games which covers a wide range of audiences including streamers and many more people. People expect the games to be closer to reality for a lively experience. Behaviors of non-playable characters (NPC) in various games like Grand Theft Auto are studied and various methods of defining behaviors to non-playable characters like behavior trees and Q-Learning behavior tree are compared for performance and activities. The comparisons of the resultant agents are made using certain performance criteria like the closeness of AI behavior to the humans, latency to respond to events etc.,
A Performance Evaluation Study to Optimize Encryption as a Service (EaaS)

Cloud computing is becoming adopted increasingly by organizations all around the world. This is because the cloud is proven more dependable, affordable, elastic, and secure than traditional storage. However, this rapid and huge growth has opened the door for a great number of threats against the data in the cloud. Therefore, EaaS "Encryption as a service" was proposed to protect the data at all times. In this paper, we study, evaluate, and analyze the performance of three cryptographic algorithms, 3DES, AES, and Blowfish in terms of encryption time, decryption time, and memory consumption (Throughput). The aim is to provide more insights into the implementation of each and determine which provides more security with lower memory cost and execution time to be used in the EaaS solution that depends on crypto-algorithms to secure data and privacy in a cloud environment.
Integration and Evaluation of Blockchain Consensus Algorithms for IoT Environments

IoT devices are increasingly used, leading to the need to ensure the privacy and security of user data. Blockchain can assure the security and immutability of data generated by IoT devices, sensors, and actuators. In Blockchain, consensus algorithms provide the security of creating and recording new blocks. However, choosing which algorithms can be used in the context of IoT is a challenge as they have advantages (e.g., security) and limitations (e.g., long response time). Thus, integrating consensus algorithms into a single solution can be a viable alternative, as it allows taking advantage of the strengths of more than one algorithm and mitigating the weaknesses. Therefore, this paper proposes integrating some consensus algorithms that can be used in IoT. These integrations are evaluated regarding CPU and memory consumption, network traffic, and time to create new blocks. Finally, the feasibility of using integrations of consensus algorithms for IoT is discussed.
Improved Logistic Map and DNA-Based Video Encryption

In the recent era, data security is important for multimedia communication such as image and video. Secured communication and confidentiality of data play an important role in many online services like authentication, video conferencing, online classes, etc. In this paper, a video encryption technique using improved logistic map and DNA sequencing has been proposed to encrypt the video. The proposed video encryption technique has mainly three phases, firstly key is generated using SHA-256. Second, permutation of the video frame is completed using improved logistic map and SHA-256. In the third step, diffusion on permuted video frame is performed using SHA-256 key and DNA sequencing. Efficiency of the encryption technique is measured through PSNR, entropy, correlation coefficients and NPCR. The presented encryption technique is resistant to the brute force and statistical attack.
Adaptive Inference on Reconfigurable SmartNICs for Traffic Classification

Traffic classification is an essential part of network management and monitoring, and has been widely used to help improve security and detect anomalies. Convolutional neural networks (CNN) have shown good performance for many of these traffic classification tasks, at the cost of a heavy computational burden. To efficiently classify incoming packets, hardware accelerators such as FPGAs, which are integrated with smart network cards, can provide the required parallelism with energy efficiency to execute such CNNs. Optimization techniques such as pruning can further improve inference speeds at the cost of some accuracy, but are usually static (i.e., cannot change after deployment). Given that, this work implements an adaptive framework to process different traffic classification tasks efficiently. It works by generating multiple pruned CNN hardware models during design time; and at run-time exploits the accuracy vs throughput trade-off of pruning by dynamically and automatically switching between the pre-generated models according to the number of incoming packets and the different classification tasks at a given moment. Compared to the regular (static) solution, our implementation improves the Quality of Experience up to $$1.14\times $$ , executing up to $$1.51\times $$ more inferences and improving energy efficiency per inference up to $$1.35\times $$ .
Smart Door Unlocking System

Face recognition-based phone unlocking was first introduced by Apple on its iPhones in 2017 and has since become a disruptive breakthrough in the smartphone business. Home security, monitoring, and automation technologies, meanwhile, have recently become an important part of many people’s daily lives. This Face Recognition and Voice Recognition Smart Door Unlock System aims to increase security. In this system, a camera sensor is utilised to capture the face, and an image matching algorithm is used to recognise authorised faces, as well as a voice sensor to record sound. Only the person with the matching face or voice may unlock the door. The security system is also built by including maintenance into the design.
A Robust Ensemble Learning Model for Fine-Grained Detection of Cyber Harassment

Due to innovative growth in cyberspace and portable penetration, kids in India are at risk of cyber harassment. A review of 174 central graders in Delhi exposed that a total of 8% pandered in cyber harassment and 17% described being offended by such acts. However, the occurrence of in-person harassment, hostility, and discrimination is also happening. Amid all the digital platforms, Instagram positions developed with extreme cyber harassment. Over the preceding span, it brought substantial developments in the grounds of machine learning (ML) which have been efficaciously functional in fields associated with cyber harassment findings, such as buzz recognition, sentimentality study, and forged broadcast discovery. In machine learning, methods are provided for detective work and the classification of online harassment. Researchers have conducted experiments using comparable datasets. To build an ensemble learning model for detecting and classification different categories of online harassment from social media platforms. In our proposed work, a robust method of detecting online harassment (cyberbullying) on the Instagram dataset is used. The attributes of abusive words are initially analyzed from feature selection and pre-trained word embedding language models like BERT and ELMO. The harassment words are detected using unsupervised machine learning techniques such as association rule classifier, latent semantic analysis (LSA), and clustering technique. Then, a novel ensemble typical model is planned for categorizing the different types of online harassment using extreme gradient boosting (XGBoost) learning method. Hence, our robust method of ensemble model for detecting and classification of online harassment provides much better results with high accuracy and lesser loss function.
Expectation-Maximization Estimation for Key-Value Data Randomized with Local Differential Privacy

This paper studies the local differential privacy (LDP) algorithm for key-value data that are pervasive in big data analysis. One of the state-of-the-arts algorithms, PrivKV, randomizes key-value pairs with a sequence of LDP algorithms. However, most likelihood estimation fails to estimate the statistics accurately when the frequency of the data for particular rare keys is limited. To address the problem, we propose the expectation-maximization-based algorithm designed for PrivKV. Instead of estimating continuous values $$[-1,1]$$ in key-value pairs, we focus on estimating the intermediate variable that contains the encoded binary bit $$\in \{1,-1\}$$ . This makes the problem tractable to estimate because we have a small set of possible input values and a set of observed outputs. We conduct some experiments using some synthetic data with some known distributions, e.g., Gaussian and power-law and well-known open datasets, MoveLens and Clothing. Our experiment using synthetic data and open datasets shows the robustness of estimation with regards to the size of data and the privacy budgets. The improvement is significant and the MSE of the proposed algorithm is $$602.83 \times 10^{-4}$$ (41% of PrivKVM).