Reviewerly/exHarmony huggingface dataset:
Construction and Application of NLP-based Knowledge Graph in CNC Equipment Fault Field

Computer Numerical Control (CNC) equipment is a technology-intensive mechatronics complex system cov-ering multidisciplinary knowledge. How to effectively convert historical fault data into useful fault knowledge base is an urgent problem to be solved. A construction technology of CNC equipment fault knowledge graph based on Natural Language Processing (NLP) is proposed. BERT deep learning classification technology is used to build the sample classification model, the named entity recognition model is trained based on the Bi-LSTM technology, as well as the CNC equipment fault corpus sample data is trained, recognized, and modeled. Finally, Neo4j is used to form a knowledge graph model. It has been verified that the recognition rate of the model basically meets the requirements, and on this basis, a recommended solution for repairing equipment faults is proposed, which realizes the effective use of fault knowledge.
Deep Learning-Driven Structured Energy Efficient Affordable Ecosystem for Computational Learning Theory

Advancement in machine learning (ML) field is rising computational demands during deep learning (DL) applications development process. Emphasis on statistical, theoretical, and computational aspects of learning and graphical models, networked with sparse analysis, tensor, and topological methods, numerical simulation at various abstraction levels holds paramount importance while analyzing performance of a learning algorithm. The demand for high-end deep learning oriented, compute intensive, specialized accelerated hardware along with performance libraries is essential to train DNN models. Availability of such cost-effective computation ecosystem for research and development is challenging because of limiting factors like the financial viability, lack of appropriate technical hardware, and software knowledge while procuring, less expertise while determining and installing required computing environment, performance libraries, frameworks, tools, etc. With the availability of big data, innovation in chip design technology, significant improvement in neural network design, improvement in speed and accuracy of deep learning model has made it possible to avail necessary DL development ecosystem to common masses under affordable cost. The aim of this paper is to present the study of ready-to-use, end-to-end deep learning solution, suitable for simulation, modeling, and analysis, having latest generation of processor, accelerated computing software development environment for DNN applications development, video analytics, linear algebra, sparse matrix operations, multi-GPU communication, training-inference engine development. This enables users at grass root level to do R&D irrespective of their geographical location and environmental conditions while building technical capabilities to solve India specific problem statements.
DP-BEGAN: A Generative Model of Differential Privacy Algorithm

In recent years, differential privacy has gradually become a standard definition in the field of data privacy protection. Differential privacy does not need to make assumptions about the prior knowledge of privacy adversaries, so it has a more stringent effect than existing privacy protection models and definitions. This good feature has been used by researchers to solve the in-depth learning problem restricted by the problem of privacy and security, making an important breakthrough, and promoting its further large-scale application. Combining differential privacy with BEGAN, we propose the DP-BEGAN framework. The differential privacy is realized by adding carefully designed noise to the gradient of Gan model training, so as to ensure that Gan can generate unlimited synthetic data that conforms to the statistical characteristics of source data and does not disclose privacy. At the same time, it is compared with the existing methods on public datasets. The results show that under a certain privacy budget, this method can generate higher quality privacy protection data more efficiently, which can be used in a variety of data analysis tasks. The privacy loss is independent of the amount of synthetic data, so it can be applied to large datasets.
Implementasi Flutter Framework &amp; Metodologi Lean UX Kanban Dalam Aplikasi Reservasi The Bbalbong

In this millennial era, people are very happy to be able to get food at the restaurant of choice, therefore the number of visitors to the restaurant has increased rapidly, especially during meal times and holidays, many customers are disappointed because they cannot get a place at their favorite restaurant, this will affect the rating of the restaurant and also in the restaurant service process, several problems arise such as customers who wait too long for food menus, and lack of information on the availability of menus. Based on the problems, we make an application for customers can reserve a place and food as a solution. The Applications made using the dart programming language with a flutter framework and the Lean UX KANBAN methodology so that the application is more effective and efficient and focuses on the division of tasks in groups. Flutter Framework is a product from Google that is suitable for application developers who have a focus on UI and UX design, for the methodology used, namely Lean UX Kanban, with the LEAN UX methodology can measure success so that the design and development of system products can be effective in a short time, Kanban methodology also helps in the division of team tasks and prioritizes to be able to speed up the application creation process.
Improved Anomaly Detection based on Loss Prediction

In the field of anomaly detection, it is challenging to collect and annotate enough samples of anomalies. Unsupervised learning is therefore frequently employed in anomaly detection and localization. The existing deep learning models for anomaly detection leverage various loss functions to train the neural networks, and they have achieved excellent performance. However, these methods are only aware of the function that loss values serve in back-propagation, but ignore that the loss value itself can partially represent how anomalous the input sample is. Consequently, we proposed a novel framework that constructs a network branch for loss prediction based on an anomaly detection network, i.e. the base model. This loss prediction branch predicts the loss value in forward-propagation, and the predicted loss value is further integrated with the output of the base model for more accurate anomaly detection. On the MVTec AD dataset, the experimental results reveal that the proposed framework outperforms other mainstreaming methods. The proposed framework can achieve AUC metrics of 98.1 % at the pixel level and 99.5% at the image level, which are 0.2% and 0.4 % higher than the base model, respectively.
Voices and images of hope: the rebirth of educational television in Ecuador in times of COVID-19

The Ministry of Education provided essential content, and UNICEF and Diners Club contributed with funding and assistance, with which IPANC and the team of experts in educational television put learning situations in television formats, aided by actors/presenters. In author's country, right now, education cannot continue without educational television. And the authors are all eager to hug each other again, albeit they want, after this experience, to remember how important educational television is and can be, in the future, to support, reinforce, and universalize education on a truly national level. Many countries of their region, including Ecuador, had its own platforms with digital educational resources; all those countries had to, abruptly, deal with the fact that a good portion of its population did not have internet access at home. That is why educational television started to trend again, which is a positive outcome of a terrible situation, they maintain.
Tackling the design and evaluation of a theater-based intelligent system to monitor audience experience in virtual public speaking settings

COVID-19 has brought about a sharp increase in the use of videoconferencing tools. In education, this complicates the monitoring of student experience, which is essential to perform adequate classroom management. Researchers have designed tools to aid teachers within on-site settings, but they focus only on student engagement and are not suitable for virtual environments.In this paper, we present our system’s architecture and evaluation. First, we adapted a theater-based framework to measuring audience experience beyond engagement in online settings. Secondly, we designed a proof-of-concept computer vision system and a companion video conferencing tool to automatically measure audience experience in the classroom and present near real-time feedback. We also describe the experiment we conducted to obtain a dataset to test our system and present the results. Although the predictive accuracy of our proof-of-concept system is limited, it opens several directions for future research.
Investigating the Economic Impact of Organizational Adoption of AI-Enabled Blockchain in 6G-Based Supply Chain Management

Artificial Intelligence (AI)-enabled blockchain technologies (BCT) in 6G-based Supply Chain Management (SCM) is a disruptive technological achievement that can affect business models and initiatives significantly. It provides various advantages such as developing innovative applications, lowering operation costs, increasing collaboration among supply chain partners, agility, transparency, accountability, decentralization, security, and interoperability. However, such novel technologies need to be adopted and used successfully by organizations to benefit the global economy. Despite their prospective advantages, even the most state-of-the-art technologies are susceptible to becoming obsolete if organizations do not adopt them. A theoretical understanding is necessary to define the underlying constructs encouraging or discouraging adoption and usage. Accordingly, this chapter investigates the constructs affecting the organizational adoption and usage of AI-enabled BCT in 6G-based SCM systems and the economic impact of this adoption. A systematic literature review method was followed to find out fundamental constructs in the literature. Then, a theoretical research model consisting of 17 constructs was developed by following the Technology-Organization-Environment Framework. After then, the economic impact of the organizational adoption of AI and BCT integration on the 6G-based SCM was analyzed.
Introduction of a New Method for Preventing Recipient Unapproved Transactions to Bitcoin Wallet

Bitcoin is one of the typical cryptocurrencies. Cur-rently, Bitcoin allows unilateral sending of currency by the sender (payer of currency) regardless of the presence or absence of intention of the recipient (payee of currency). Previous works pointed out that this could induce the sending of currency used in crimes, etc., which could lead to problems. This problem is called the recipient unapproved problem. Bitcoin users use a function called Bitcoin wallet in the platform software such as Bitcoin Core to perform transactions. In this paper, we try to incorporate a new transaction creation method into the Bitcoin wallet in order to resolve the recipient unapproved problem. The new transaction creation method also allows the recipient's Bitcoin wallet to participate in the creation of the transaction, and adds the recipient's digital signature to prove that the transaction was also approved by the recipient and a new address that cannot be used except for approved transactions. In experiments for investigating the overhead of the new transaction creation method, results showed that a transaction can be created with about twice creation execution time of the conventional transaction creation method.
Deep Rapid Class Augmentation A Progressive Learning Approach for Deep Neural Networks

This paper provides additional background on a new progressive learning approach for deep neural networks called Deep Rapid Class Augmentation (Deep RCA). The goal of progressive learning is to reuse knowledge acquired from previous training to reduce the training time required to add additional classes to an existing model. A key benefit of the Deep RCA approach is that it only requires training data from the new class in order to optimally update all classes in the existing model. This allows Deep RCA to train large models with new classes much faster than conventional training techniques. Specifically, this paper shows a 1700x reduction in training time when Deep RCA is used to augment a new class onto a 19-class base model compared to the time required to train a similar model from scratch without leveraging any form of progressive or transfer learning. In addition, Deep RCA shows a 35x reduction in training time over current progressive learning techniques that employ a stochastic gradient descent style of optimization. The results show that Deep RCA provides a faster augmentation method than current training techniques, which is expected to be beneficial for applications that require real-time, continuous learning of new classes for perception tasks.
FPGA-based unscented Kalman filter for target tracking

In this paper, the unscented Kalman filter (UKF) is used to estimate the states of a vehicle while it is moving on a road with different speed. Field programmer gate array (FPGA) prototyping board is used to implement the filter. The resources of FPGA is optimized using different techniques. The overall filter performance is examined in further details.
A Self-adaptive Indicator Selection Approach for Solving Credit Risk Assessment

Credit risk assessment, which aims at identifying high-risk users, plays a critical role in financial institutions. A common method is to use the greedy strategy to generate an interpretable rule set to classify all the users into high-risk or non-risk users. During each iteration, the greedy strategy utilizes a pre-defined indicator function to evaluate which rule is the best and then adds it to the rule set. However, in reality, the indicator function is designed manually and requires much domain knowledge and expert experience. Worse still, we need to design a suitable indicator for every situation, which is tedious and time-consuming work. This motivates us to propose a self-adaptive indicator that can be adapted to different situations without too much human intervention. In this paper, we see the indicator as a weighted sum of several sub-indicators. By tuning the weights, the indicator can be adapted to different situations automatically. That is, we transform this indicator selection problem into a weights tuning problem. To find the best weight of self-adaptive indicators, machine learning methods and black-box optimization are utilized. The experimental results demonstrated that our self-adaptive indicator can select a better rule set to identify more high-risk users compared to the human-defined indicator.
Principal component signatures for correlation-and-bit-aware spread spectrum steganography

This paper introduces principal component signature design for correlation-and-bit-aware embedding. Simulation studies show superior performance in terms of bit error rate for L1-norm and L2-norm based signatures versus arbitrary signatures.
Secure Mobile Agents on Embedded Boards: a TPM based solution

Security can be considered one of the essential aspects of any software system today. The current landscape is constantly evolving and new computing models are appearing at the same time as different attacks emerge. All this means that there is an increasing need for new security solutions. Among the different aspects that are opening up, this work focuses on the protection of sensitive data. In particular, an environment based on mobile agents is considered, which contains sensitive information that needs to be protected. To simulate an Internet of Things (IoT) environment, the agencies on which the agents run are deployed on Raspberry Pi devices.
Submodular Optimization via Reinforcement Learning for Active Control of Sensor Networks

Autonomous heterogeneous sensor networks (HSNs) consist of platforms equipped with various sensors. Platforms and sensors have limited range, so they must make decisions on where and when to point these sensors, what information to share among the cooperating platforms, and how to exploit multi-modal dependencies for accurate detection, tracking, and classification (DTC) of time-critical objects. We posit that cooperating platforms within HSN s that share and fuse multimodal data among themselves (using constrained inter-platform communication) will be more effective in accomplishing their required tasks and have more accurate target detection and recognition. We study a scenario where a group of cooperating platforms, each equipped with multi-modal sensors are collecting measurements of an object and the measurements are transmitted over a bandwidth-limited communication channel to a centralized node for processing. The communication channel presents an information transfer bottleneck as the sensors collect measurements at a much higher rate than what is feasible to transmit over the communication channel. In order to minimize the estimation error at the centralized node, only a carefully selected subset of measurements should be transmitted. We propose a unique synthesis of novel submodular optimization based reinforcement learning (RL) for active sensor control/management. Our approach solves the measurement selection problem by employing a model-free RL with sub modular objective functions, where instead of maximizing the cumulative reward, the goal is to maximize the objective value induced by a sub modular function. We make evident the effectiveness of this approach with simulated and real data (the latter including ESCAPE Government-furnished information which we have been extensively using on our previous efforts).
Mid–Point Sorting Algorithm:A New Way to Sort

In today’s era, one of the primary and crucial issues in computer science is all about systematic ordering of a set of items. Ordering forms a fundamental operation in different practical fields of Computer Sciences, Database Applications, Networks and Artificial Intelligence, etc. thereby attracting a massive amount of research in this field. Although, there exist a huge number of sorting algorithms to enhance the computational performance, there are several other factors that must be taken into account: time complexity, space complexity, stability. This paper presents a new sorting algorithm that takes into account the mid-point of the unsorted array and checks for its neighbors in every iteration.
Platform Construction of Intelligent Machine Algorithm and Practical Guidance Course for Overlapping Data Monitoring Guiding Russian-Chinese Translation

Based on overlapping data monitoring, this paper studies the platform construction of intelligent machine algorithms and practical guidance courses for Russian-Chinese translation. In the part of constructing the intuitive mode of the system, firstly, through the analysis of the existing Russian-Chinese machine translation system, several typical types of the current system are obtained. In order to solve the problem of sexuality, draw on the analysis results of the machine translation system in the previous chapter, and put forward its own design concept for the construction of the system, including the construction of dictionaries and corpora. The multi-party collaboration and value co-creation of typical enterprises in the industry, specialized information technology companies, and professional scientific research institutions on campus provide MOOC practice teaching that is closer to the practice process of enterprises for students majoring in science and engineering.
The Relationship between Financial Inclusion and Sudanese Economic Growth 2006-2020

The paper shed light on the causal relationship between financial inclusion, and economic growth in Sudan from 2006 to 2020. The paper seeks to answer the following question: What is the impact of financial inclusion indicators on economic growth in Sudan? Therefore, In order to answer this question, the paper aimed to find out the causal relationship between some indicators of financial inclusion including (the number of ATMs, the number of Sudanese bank branches and the ease of doing business index) in addition to the growth of the Sudanese economy proxies by RGDP to test the causal relationship between the variables of the study, the paper employed the Vector Auto regression Model (VAR) methodology and also adopted the Angel Granger Causality Test through which the outputs of the Eviews10 showed that the number of bank branches and the ease of doing business index affect the growth of the Sudanese economy, and also showed that there is a relationship between the number of ATMs and economic growth, and accordingly the paper recommended the provision of banking services represented in facilitating the requirements for opening bank accounts as well as facilitating services for obtaining business licenses, in addition to the delivery of banking services to all regions of Sudan. Keywords: Financial Inclusion, VAR Model, Economic Growth
The Importance of Graph Databases in Detection of Organized Financial Crimes

This paper aims to reveal how important and irreplaceable graph databases are in discovering and preventing organized financial crimes, with their reasons. In order to find out organized financial crime, its perpetrators, and victims, it is necessary to uncover the multi-layered and hidden relationships between different entities. It is almost impossible to achieve this by developing a software solution on a relational database due to the large volume of data and intricate multi-layered relationships, and it is insufficient at many points. In this paper, we propose to overcome this by visualizing these complex relationships on graph databases. Graph databases have much more natural analytical power for social networks than relational databases. Graph databases are designed to model, store and query social networks with multiple complex relationships. Each entity in the social network can act as an individual entity and interact with other entities. While there are several different ways to reveal these illegal relationships, the most obvious, most used, and most effective method is to track money transfers between different entities. In this paper, we will display money transfers between different entities in a graphical database.
Hardware-aware Neural Architecture Search with segmentation-based selection

Hardware-aware Neural Architecture Search (HW-NAS) has been drawing increasing attention since it can auto-matically design deep neural networks optimized in a resource-constrained device. However, it requires enormous amount of computations, which is not affordable for many. Thus, we propose an efficient method for searching promising neural architectures in HW-NAS. We can significantly reduce computing cost of search using both an accuracy predictor and a latency estimator and sharing pre-trained weights of a super-network. Overall searching procedure takes under 1 minute on a single CPU, which is tremendous improvement compared to general NAS work which requires several days or weeks on a single GPU. To search neural architectures under multiple objectives, we propose segmentation-based selection in search stage. The experimental results show our approach is very competitive compared with other multi-objective optimized methods. For a target hardware, we experimented on Field Programmable Gate Array (FPGA) and compared the results with modern CPUs.
INFORMATION SYSTEMS LABORATORY. MAIN SCIENTIFIC RESULTS IN 2021

This paper presents main scientific results of Information systems laboratory in 2021