Reviewerly/exHarmony huggingface dataset:
ROOA: CloudIDE framework for extension development

The Program is a mediator as all IDEs are mediator programs, in the sense that IDEs like Eclipse, Eric IDE, and net beans are considered as a mediator between a programmer and Interpreter or the programming language compiler. In fact, in this paper we can say that all things the programs do can be done with a simple text editor and we can see the Program output by direct contact with interpreter or compiler. The only difference between the Program provided by me and other IDEs is that the environment is special for building the extension. It means that instead of being an interface between user and interpreter or compiler, standards provided by other applications are usually different. The program tries to fill in the absence of an Efficient IDE in the field of building an extension. On the other hand, it reduces the cost of extension standard providers for presenting an IDE specific to that standard. In fact, in the second target the program provided will be a platform that has the ability to mount the next standards. Standard providers should only conduct a Part of coding. A constant part is common in all standards. We should find a solution for them in all IDEs, such as file management and information management that have been implemented in constant part of the IDE. The variable and non-similar part is implemented as classes. If needed to implement, it should be inherited from them and some of their functions must be overridden.
OkkhorNama: BdSL Image Dataset For Real Time Object Detection Algorithms

In recent years, lots of researches are being conducted to interpret Bangladeshi Sign Language (BdSL) to the means that general people can communicate with people having a hearing impairment and reduce the verbal gap between them. Computer Vision is playing a vital role in this regard by developing a sustainable system to understand the signs for machine translations. To obtain optimal performance, along with the state-of-the-art CNN model, the requirement of a high-quality sign language dataset cannot be foreseen. In this paper, we have introduced a new image dataset OkkhorNama for Fingerspelled Bangladeshi Sign Language including all 46 signs with images over 12K. In each of the images, bounding boxes are carefully annotated and labeled. OkkhorNama contains images of high resolution, good quality, and adequate variation making it ideal to train object detection and localization algorithms that would perform well on real-world applications. The OkkhorNama dataset is compared with other datasets where OkkhorNama significantly outperforms other datasets in number and trained model performance. The dataset is publicly available for future research and development.
Federated Model Search via Reinforcement Learning

Federated Learning (FL) framework enables training over distributed datasets while keeping the data local. However, it is difficult to customize a model fitting for all unknown local data. A pre-determined model is most likely to lead to slow convergence or low accuracy, especially when the distributed data is non-i.i.d.. To resolve the issue, we propose a model searching method in the federated learning scenario, and the method automatically searches a model structure fitting for the unseen local data. We novelly design a reinforcement learning-based framework that samples and distributes sub-models to the participants and updates its model selection policy by maximizing the reward. In practice, the model search algorithm takes a long time to converge, and hence we adaptively assign sub-models to participants according to the transmission condition. We further propose delay-compensated synchronization to mitigate loss over late updates to facilitate convergence. Extensive experiments show that our federated model search algorithm produces highly accurate models efficiently, particularly on non-i.i.d. data.
Enterprise financial risk intelligent control system based on artificial intelligence algorithm

In order to overcome the problems of low precision and poor stability of financial risk data mining in traditional enterprise financial risk control system, this paper proposes a new enterprise financial risk intelligent control system based on artificial intelligence algorithm. Combined with B/S three-tier structure and MVC architecture, the overall structure of the system is designed. According to the overall structure of the system, the system hardware is designed to meet the operation and use needs of the system. The system hardware module mainly includes user registration module, financial risk degree judgment module and data warehouse module. Through the coordination of each module, the overall operation performance of the system can be improved. In the software part of the system, the artificial intelligence algorithm is used to fully mine the enterprise financial risk data, and the financial risk management and control model is constructed to realize the intelligent management and control of financial risk. The experimental results show that, compared with the traditional risk management and control system, the designed system has higher accuracy of financial risk data mining, and can improve the stability of the system.
Dual Attention Guided R2 U-Net Architecture for Right Ventricle Segmentation in MRI Images

Right ventricle segmentation plays an important role in the computer-aided diagnosis of heart diseases. However, due to the small area of right ventricle and limited dataset, the performances of the existing deep learning segmentation methods are not good enough. For some small areas of right ventricle that are difficult to segment, we apply a novel dual attention module on the decoding path of Dilated R2 U-net to extract better feature representations in this work. The dual attention module in this work is divided into position attention module and channel attention module. The positional attention module suppresses the irrelevant feature representations in the feature map and enhances the useful feature representations to improve the sensitivity and prediction accuracy of the model. The channel attention module enhances the interdependence of the feature representation of channels by gathering the information of the associated channels in the feature map. We use dilated convolutions to expand the receptive field of the model. By adding dual attention modules, our model shows higher precision than Dilated U-net on the Right Ventricle Segmentation Challenge (RVSC) test dataset.
A hybrid spatial prediction algorithm for lossless compression of CT and MRI medical images

To improve the lossless compression performance of medical image, a hybrid spatial prediction algorithm is proposed in this paper. The input image is first divided into the objective region and the background region. For the objective region, we propose a block-based spatial prediction scheme, which makes full use of the structural similarities and symmetries of the objective region. For the background region, traditional pixel-based prediction scheme is applied. The proposed hybrid prediction scheme combines block-based and pixel-based prediction scheme to enhance the performance of the lossless compression. Experimental results show that the proposed scheme has a comparable performance compared with other state-of-the-art methods.
On Artemov and Protopopescu’s Intuitionistic Epistemic Logic Expanded with Distributed Knowledge

Artemov and Protopopescu (2018) introduced a Brouwer-Heyting-Kolmogorov (BHK) interpretation of knowledge operator to define the intuitionistic epistemic logic IEL, where the axiom \(A\supset KA\) is accepted but the axiom \(KA\supset A\) is refused. This paper studies the notion of distributed knowledge on an expansion of the multi agent variant of IEL. We provide a BHK interpretation of distributed knowledge operator to define the intuitionistic epistemic logic with distributed knowledge DIEL. We construct Hilbert system and cut-free sequent calculus for \(\mathbf {DIEL}\) and show that they are sound and complete for the intended Kripke semantics.
Leukocyte classification based on statistical measures of radon transform for monitoring health condition

In the medical field, automated and computerised analytic tools are essential for faster disease diagnosis. The main objective of this research work is to classify the leukocytes accurately into four different subtypes based on the pattern of the nucleus. The features are extracted from the segmented nucleus, which play a vital role in the pattern recognition. The technique comprises a novel idea of computing the statistical measures such as peak difference and standard deviation of the radon transformed graph for a single angle of rotation along with other features. Three Gray Level Co-occurrence Matrix (GLCM) based features, two geometric features and four RST moment invariants are also extracted for feature fusion. The fused feature vectors are trained and evaluated using random forest classification algorithm.This method provides an overall accuracy of 97.61% and it is able to determine the lymphocyte, neutrophil and eosinophil with 100% accuracy. The classification without incorporating radon transform features is also performed which provides an accuracy of only 80.95%.
Negative Sampling for Knowledge Graph Completion Based on Generative Adversarial Network

Knowledge graph, a semantic network, to organize and store data is increasingly interested in the research community and businesses such as Google, Facebook, Amazon. For the machine learning models to work well in this data, we need to prepare good quality negative samples. Generating these negative examples is challenging in the knowledge graph because it is pretty hard to determine whether a link that does not appear in the graph is a negative or positive sample. In this paper, we apply the generative adversarial network to the ConvKB method to generate negative samples, thereby producing a better graph embedding. Experiments show that our approach has quality improvement compared to the original method on well-known datasets.
A Novel Approach for Face Mask Detection using Tensorflow for Covid-19

Corona Virus Disease (COVID-19) pandemic has become a cause world health crisis. It is a disease that can spread from humans to humans through physical contact with the infected droplets or via airborne. It has been scientifically proven that wearing a face mask is the most effective method against the virus. This paper's aim is to develop a face mask detector which could be used to make mitigation, evaluation, prevention, and action plans against COVID-19 pandemic by the authorities. In this study, the face mask detection is developed based on the image classification method called Mobile_NetV2. The pseudo-steps for making the detector model are accumulating data, pre-processing, breakdown of the data, training the model, and implementation of the model. The proposed model is able to detect the people with or without a face mask with an accuracy of 96.85 percent. The experimental results of the model have been performed on real-time applications. The mask detector is also able to detect the face mask on a moving subject with expected accuracy.
Analysis of public-key cryptography using a 3-regular graph with a perfect dominating set

Research on post-quantum cryptography (PQC) to improve the security against quantum computers has been actively conducted. In 2020, NIST announced the final PQC candidates whose design rationales rely on NP-hard or NP-complete problems. It is believed that cryptography based on NP-hard problem might be secure against attacks using quantum computers. N. Koblitz introduced the concept of public-key cryptography using a 3-regular graph with a perfect dominating set in the 1990s. The proposed cryptosystem is based on NP-complete problem to find a perfect dominating set in the given graph. Later, S. Yoon proposed a variant scheme using a perfect minus dominating function. However, their works have not received much attention since these schemes produce huge ciphertexts and are hard to implement efficiently. Also, the security parameters such as key size and plaintext-ciphertext size have not been proposed yet. We conduct security and performance analysis of their schemes and discuss the practical range of security parameters. As an application, the scheme with one-wayness property can be used as an encoding method in the white-box cryptography (WBC).
A Detailed Comparative Study and Performance Analysis of Standard Cryptographic Algorithms

Nowadays the most important issue we face during data transmission and exchanging of data is security of data. The cryptographic algorithms play very significant role to secure data. By using different algorithms, it improves security of data by making data unreadable, only the authenticate users can read data after decryption using keys. All algorithms perform same work, but consume different volume of computing properties such as time of CPU, memory utilization, throughput time, encryption and decryption time, simulation etc. Also different algorithms use various size of keys, size of block and cipher type. So we need to compare the cryptographic algorithms to choose the best one. In this paper, we have done the evaluation of both block cipher (AES, DES, 3DES, Blowfish) as well as stream cipher (SALSA20) cryptographic algorithms is shown by taking different size of audio files. This comparison of different algorithms has been conducted to evaluate parameters such as encryption/decryption time, throughput time, memory utilization, scalability and ratio. Simulation results are given to demonstrate the efficiency of each. This research contributes to identify state of the art cryptographic technique.
Modeling Dynamics of Traffic for the E-Commerce Website in the Process of its Search Engine Optimization During the COVID-19 Pandemic

The process of search engine optimization (SEO) websites at the supporting stages is considered. The impact of the COVID-19 pandemic on customers' buying habits is taken into account. Mathematical model of the dynamics organic traffic website at the supporting stages of SEO using the method of reduction is built.
Adoption intention of virtual reality in choosing travel destinations in the context of a developing economy

The role and influence of virtual reality (VR) in the context of tourism and destination marketing has been prevalent in the academic literature since the mid-nineties. The literature confirms that...
Independent natural extension for choice functions

We introduce an independence notion for choice functions, which we call ‘epistemic independence’ following the work by De Cooman et al. [17] for lower previsions, and study it in a multivariate setting. This work is a continuation of earlier work of one of the authors [29], and our results build on the characterization of choice functions in terms of sets of binary preferences recently established by De Bock and De Cooman [11]. We obtain the many-to-one independent natural extension in this framework. Given the generality of choice functions, our expression for the independent natural extension is the most general one we are aware of, and we show how it implies the independent natural extension for sets of desirable gambles, and therefore also for less expressive imprecise-probabilistic models. Once this is in place, we compare this concept of epistemic independence to another independence concept for choice functions proposed by Seidenfeld [28], which De Bock and De Cooman [2] have called S-independence. We show that neither is more general than the other.
Joint Communication and Motion Planning for Cobots

The increasing deployment of robots in co-working scenarios with humans has revealed complex safety and efficiency challenges in the computation of the robot behavior. Movement among humans is one of the most fundamental —and yet critical—problems in this frontier. While several approaches have addressed this problem from a purely navigational point of view, the absence of a unified paradigm for communicating with humans limits their ability to prevent deadlocks and compute feasible solutions. This paper presents a joint communication and motion planning framework that selects from an arbitrary input set of robot's communication signals while computing robot motion plans. It models a human co-worker's imperfect perception of these communications using a noisy sensor model and facilitates the specification of a variety of social/workplace compliance priorities with a flexible cost function. Theoretical results and simulator-based empirical evaluations show that our approach efficiently computes motion plans and communication strategies that reduce conflicts between agents and resolve potential deadlocks.
Learning a Robot's Social Obligations from Comparisons of Observed Behavior

We study the problem of learning a formal representation of a robot's social obligations from a human population's preferences. Rigorous system design requires a logical formalization of a robot's desired behavior, including the social obligations that constrain its actions. The preferences of the society hosting these robots are a natural source of these obligations. Thus we ask: how can we turn a popu-lation's preferences concerning robot behavior into a logico-mathematical specification that we can use to design the robot's controllers? We use non-deterministic weighted automata to model a robot's behavioral algorithms, and we use the deontic logic of Dominance Act Utilitarianism (DAU) to model the robot's social and ethical obligations. Given a set of automaton executions, and pair-wise comparisons between the executions, we develop simple algorithms to infer the automaton's weights, and compare them to existing methods; these weights are then turned into logical obligation formulas in DAU. We bound the sensitivity of the inferred weights to changes in the comparisons. We evaluate empirically the degree to which the obligations inferred from these various methods differ from each other.
Dynamic Multi-level Feedback Queuing based Packet Scheduling for Datacenter Networks

When the size information of flows is unknown, how to set the priorities of flows to reduce the latency of small flows is a challenging problem. Most of the existing works exploit multi-level feedback queue (MLFQ) to assist the priority setting of the different sizes of flows with the global data center traffic distribution. However, this can lead to a mismatch between link-level flows and MLFQ threshold settings. In this paper, we propose a dynamic MLFQ based host-to-host packet scheduling scheme, which can effectively reduce the link-level flows mismatch. First, we deploy an MLFQ at the sender to ensure small flows have higher priority than larger flows in general. Second, we design a clustering algorithm to calculate the MLFQ threshold based on the link flows information and adjust priority dynamically at the receiver. The experimental results show that our scheme can reduce the tail delay of small flows by about 13% and does not affect the performance of large flows.
Blockchain-Based Criminal Record Database Management

With rapid urbanization and the advancement of cities and towns, the graph of crime rates is increasing gradually. Blockchain can replace those piled up criminal records with a network where documents are easily accessible and could not be tampered with, making them safe and Secure. Blockchain is a P2P (peer-to-peer network) that helps in the decentralization of data. This system will be based upon the immutability characteristic of blockchain to ensure the integrity and security of data. This blockchain-based process can reduce corruption risk factors by making it easier for third parties to monitor tamper-evident transactions and enabling greater objectivity and consistency, thus enhancing criminal record transparency and accountability. Furthermore, timely access of authentic criminal records to respective administrative authorities will make law enforcement effective.
ADVANTAGES AND DISADVANTAGES OF BLENDED LEARNING

The paper deals with the terms of blended learning as a combination of the traditional school system and modern digital educational technologies. The advantages and disadvantages of methods of blended learning in the studying process are described.
Decision Combination in Classifier Committee Built on Deep Embedding Features

In this paper we leverage voting rules as an aggregation technique in classifier combination. We propose a voting rules-based algorithm to improve the recognition performance of human action recognition on raw depth maps. The recognition is performed by a classifier committee that is built on deep embedding features. It consists of softmax logistic regression classifiers delivering well calibrated outputs. They have been trained as one-vs-all on the concatenated class-specific features and features common for all classes. Various ranked voting rules as the aggregation technique have been investigated in decision making. The results achieved by such algorithms were compared with results achieved in optimized weighted voting. Differential evolution and sequential least squares programming have been selected for optimized weighted voting. We demonstrate experimentally that both optimized weighted voting and voting rules improve results achieved by soft voting. The best results have been achieved using voting rules determined by Coombs algorithm. We demonstrate experimentally that on SYSU 3DHOI dataset the proposed algorithm outperforms by a large margin all recent algorithms.
VRTactileDraw: A Virtual Reality Tactile Pattern Designer for Complex Spatial Arrangements of Actuators

Creating tactile patterns on the body via a spatial arrangement of many tactile actuators offers many opportunities and presents a challenge, as the design space is enormous. This paper presents a VR interface that enables designers to rapidly prototype complex tactile interfaces. It allows for painting strokes on a modeled body part and translates these strokes into continuous tactile patterns using an interpolation algorithm. The presented VR approach avoids several problems of traditional 2D editors. It realizes spatial 3D input using VR controllers with natural mapping and intuitive spatial movements. To evaluate this approach in detail, we conducted a user study and iteratively improved the system. The study participants gave predominantly positive feedback on the presented VR interface (SUS score 79.7, AttrakDiff “desirable”). The final system is released alongside this paper as an open-source Unity project for various tactile hardware.
Method for Detecting and Tracking Foreign Objects in Substation Videos Based on Embedded AI

Foreign object monitoring is very important to maintain the safe running of substations. The development of intelligent video monitoring technology provides a solution for real-time warning and foreign object tracking in substations. Intelligent video recognition technology is gradually replacing manual monitoring. However, the current video recognition algorithms, such as moving object detection, deep learning object detection and tracking, have their own advantages and disadvantages, which cannot meet the targeted recognition of foreign objects of interest in substations or it is difficult to achieve real-time detection on embedded devices. Moving object detection is fast. YOLOv4 can locate and recognize the objects of interest with high recognition rate. And tracking algorithm has fast speed as well as good tracking continuity. Combining these three algorithms, this paper proposes a method based on YOLOv4 for foreign object monitoring and tracking in substation videos. And experiments show the method performs well in the detection and tracing of ILSVRC VID dataset and actual substation videos. It can realize front-end detection and real-time monitoring on NVIDIA Jetson Xavier NX embedded development board.