Reviewerly/exHarmony huggingface dataset:
Blockchain Technology and Management Accountants

In this article some of the terms used in the Blockchain Technology are demystified. It discusses on how the Management Accountants can play a major role in enabling the organisations to maintain Cost Accounting Records using the Blockchain Technology. It also discusses on how the cost accounting records maintained in a Triple Entry Accounting (TEA) system can help save a lot of time and effort for the cost auditor and other stakeholders.
Multi-target Tracking Algorithm Based on Self-regulating Sparse Expression and its Application

Target tracking is an important branch in the field of machine vision, which has attracted much attention for a long time. Target tracking is widely used in safety monitoring, national defense construction, intelligent transportation, aviation and astronautics, medicine and health, and national economy. The key to achieve target tracking is to segment the target completely, extract features reasonably, recognize the target accurately, and also consider the time to implement the algorithm to ensure real-time performance. In this paper, sparse expression is combined with adaptive algorithm for target tracking. A multi-target tracking algorithm based on self-regulating sparse expression is proposed. The proposed algorithm is helpful to the accuracy and real time of target tracking and improves the means and level of multi-target tracking.
A Non-local Low Rank and Total Variation Approach for Depth Image Estimation

Accurate depth reconstruction is vital for numerous applications including autonomous vehicles, virtual reality, and robot perception. However, the depth imaging is challenging because of limited hardware operations, resource-constrained limitations, and incomplete data measurements. To address such shortcomings, this paper introduces an imaging model for efficient depth image estimation from incomplete depth pixels using non-local low-rank (NLLR) and total variation (TV) representations. The motivation is that NLLR is used to model global similar structure among depth patches, and the TV is incorporated to capture the correlations among local depth pixels. We reformulate the problem of depth reconstruction as a regularized least squares minimization problem with the non-local LR and TV regularizers. Furthermore, this paper proposes an iterative algorithm using the alternating direction method of multipliers (ADMM) to solve the optimization model, yielding an estimate of the depth map from far reduced data points. Experimental results on benchmark datasets validate the efficiency of the proposed approach.
Weakly Supervised Anomaly Detection for Streaming Data

Anomaly detection for time-series data is an important component for monitoring tasks. Due to the massive and diverse types of streaming sources, it is impossible to manually label training data for large-scale applications. So existing solutions are based on unsupervised learning. However, each time-series data has its own domain specific property. Without incorporating human knowledge, unsupervised learning methods cannot match the performance of supervised learning. We introduce an anomaly detection solution which utilizes human knowledge and is suitable for large-scale applications. The proposed approach starts without any human labeling. Instead, human knowledge is obtained through feedbacks. We iteratively refine the training data to ensure that accurate models can be built. This makes it easily applicable to large-scale and heterogeneous tasks while enjoying the benefits of supervised learning.
Neural Network for Big Data Sets

Machine learning (ML), neural network (NN), evolutionary algorithm (EA), fuzzy systems (FSs), as well as computer science have been very famous and very significant for many years. They have been applied to many different areas. They have contributed much to developments of many large-scale corporations, massive organizations, etc. Lots of information and massive data sets (MDSs) have been generated from these big corporations, organizations, etc. These big data sets (BDSs) have been the challenges of many commercial applications, researches, etc. Therefore, there have been many algorithms of the ML, the NN, the EA, the FSs, as well as computer science which have been developed to handle these massive data sets successfully. To support for this process, the authors have displayed all the possible algorithms of the NN for the large-scale data sets (LSDSs) successfully in this chapter. Finally, they have presented a novel model of the NN for the BDS in a sequential environment (SE) and a distributed network environment (DNE).
A Data Sharing and Integration Technology for Heterogeneous Databases

For a long time, there are a large number of heterogeneous databases on the network, and their heterogeneity is manifested in many aspects. With the development of enterprise informatization and e-government, the system database of each department constitutes a real heterogeneous database framework with its independence and autonomy in the network system of many different functional departments. This paper will design information sharing between heterogeneous databases of network database system of many similar functional departments by using XML data model. The solution of data sharing between heterogeneous databases can accelerate the integration of information systems with departments and businesses as the core among enterprises, form a broader and more efficient organic whole, improve the speed of business processing, broaden business coverage, and strengthen cooperation and exchange among enterprises. In addition, heterogeneous database sharing can avoid the waste of data resources caused by the heterogeneity of database, and promote the availability rate of data resources. Due to the advantages of XML data model, the system has good scalability.
Realizing a Multi-Layered Mobile Strategy for Underserved Areas for Cybersecurity Leadership

This chapter shows the value of grasping a mobile strategy deploying multiple layers of security. Data shows a void in leader understanding regarding safe m-learning, m-learning's safety cost requirements, and enduring sustainability in a cybernated world. Cybersecurity must be infused into processes encompassing m-learning strategy to include international and national insights in underdeveloped areas. Cybersecurity leaders must increase their overall abilities to guide the growth and operation of a cyber-secure mobile strategy. This chapter infuses cybersecurity into processes encompassing m-learning strategy by probing the insights for cybersecurity's influence on m-learning in this 21st century. Cybersecurity leaders and faculty members should amass knowledge to disambiguate obstacles that block secure m-learning realization. Further, procurement leaders should learn information to help them analyze the need to procure m-learning systems enfolded in long-term overall strategic plans applying cybersecurity while balancing economic value and essential controls.
Use of neural networks in the exchange of cryptographic keys

This article proposes the use of neural networks in the exchange of cryptographic keys. An algorithm for generating a secret key based on a neural network has also been developed.
Bayesian nonparametric disclosure risk assessment

Any decision about the release of microdata for public use is supported by the estimation of measures of disclosure risk, the most popular being the number τ1 of sample uniques that are also population uniques. In such a context, parametric and nonparametric partition-based models have been shown to have: i) the strength of leading to estimators of τ1 with desirable features, including ease of implementation, computational efficiency and scalability to massive data; ii) the weakness of producing underestimates of τ1 in realistic scenarios, with the underestimation getting worse as the tail behaviour of the empirical distribution of microdata gets heavier. To fix this underestimation phenomenon, we propose a Bayesian nonparametric partition-based model that can be tuned to the tail behaviour of the empirical distribution of microdata. Our model relies on the Pitman–Yor process prior, and it leads to a novel estimator of τ1 with all the desirable features of partition-based estimators and that, in addition, allows to reduce underestimation by tuning a “discount” parameter. We show the effectiveness of our estimator through its application to synthetic data and real data.
Arabic Words Root Extraction Using Modified Successor Variety

The process of returning words to their linguistic roots is one of the most important stages of preparation for computerizing the Arabic text, this process aims to: Overcoming the multiple morphological forms of the Arabic language, rooting algorithms are used in many applications of language processing, such as information retrieval applications, machine translation, text summarization, optical character recognition, and other applications. In this research, a new algorithm was proposed to extract roots of the Arabic words, depending on the adopted successor variety algorithm, by changing the body of the proposed text in this algorithm, so that it consists of several morphological formulas associated with the word to be rooted, the proposed algorithm has been tested on approximately six thousand derived words from 137 deferent roots, with 44 morphological forms for each root, the ratio of correct extracted roots using the proposed algorithm was about 98.5%.
A Study on the Response Strategy of Korea Companies to the U.S. - China Trade Dispute

Purpose The purpose of this study is to seek a strategy for Korean companies to respond to trade disputes between the U.S. and China. Design/Methodology/Approach Through the current status of trade retaliation between the U.S. and China, the impact of trade retaliation on the U.S. and Chinese economies was investigated. Findings As a result of the research in this paper, the U.S.-China trade conflict will intensify in the long run rather than ease, forcing it to stand on the side of the U.S. Therefore, Korean companies that have entered the U.S. should check whether Chinese companies or the Chinese government are included in the supply chain of product production. In addition, various response strategies should be prepared in preparation for China’s trade sanctions. Research Implications As the U.S.-China trade conflict continues, it is necessary to pursue a strategy centered on the Korea-U.S. alliance along with a strategy to minimize economic losses caused by China’s trade sanctions.
Distributed Deep Learning with Model Parallelism and Updating Parameters Synchronous

Abstract We proposed a distributed deep learning way with model parallelism. This way can realize model parallelism and parameters synchronous update, and improve the efficiency of deep learning. First of all, we analyze the model parallelism theoretically. It is analyzed that the interdependent gradients between workers are the key factor affecting the speed of distributed training, and then a method of giving priority to calculating the independent gradients between workers are proposed to realize model parallelism. In theory, it ensures the normal operation of distributed training. Then, the proposed method is verified by experiments, MLP and Resnet network are trained. The experimental results show that the Neural Network model trained by our proposed method is basically the same as the single machine training model. When training Resnet Neural Network with more parameters, the time of distributed training decrease by 0.19X to 0.5X than single machine training. We also use two types of distributed training ways and compare them with our way. In terms of training accuracy, compared with the latest distributed training algorithm, our training method ensures the synchronous updating of parameters, and has a higher training accuracy after each training cycle. In terms of training time, compared with the classical algorithm, our training method has a shorter training time.
Video Coding Pre-Processing Based on Rate-Distortion Optimized Weighted Guided Filter

In video coding, it is always an intractable problem to compress high frequency components including noise and visually imperceptible content that consumes large amount bandwidth resources while providing limited quality improvement. Direct using of denoising methods causes coding performance degradation, and hence not suitable for video coding scenario. In this work, we propose a video pre-processing approach by leveraging edge preserving filter specifically designed for video coding, of which filter parameters are optimized in the sense of rate-distortion (R-D) performance. The proposed pre-processing method removes low R-D cost-effective components for video encoder while keeping important structural components, leading to higher coding efficiency and also better subjective quality. Comparing with the conventional denoising filters, our proposed pre-processing method using the R-D optimized edge preserving filter can improve the coding efficiency by up to −5.2% BD-rate with low computational complexity.
Haze removal in Remote sensing 2-D information: Methods and Analysis

Haze reduces an image's quality, reducing its aesthetic appeal and visibility in outdoor locations. This is because of the presence of smog, fog, haze etc. Therefore, image de hazing is one of the important considerations. Dehazing mechanisms are widely used in military and civil regions, such as, monitoring of traffic, remote sensing, identification of targets etc. This study examines the key de haze approaches that have been developed over the last decade. This paper conducts a thorough examination of dehazing techniques in order to demonstrate that they may be used effectively in real-world situations. In this paper, image dehazing is classified into two sections, first section is on image enhancement and second is explained on restoration model. According to principles and characteristics, all approaches are analyzed and corresponding sub-categories are presented. Following that, many quality assessment methodologies are explained, sorted, and examined in depth. The aim of the paper is to analysis recent advances in image de hazing in a short manner, as well as to give one concise knowledge of image de hazing methodologies.
Alleviating Catastrophic Interference in Online Learning via Varying Scale of Backward Queried Data

In recent years, connectionist networks have become a staple in real world systems due to their ability to generalize and find intricate relationships and patterns in data. One inherent limitation to connectionist networks, however, is catastrophic interference, an inclination to lose retention of previously formed knowledge when training with new data. This hindrance has been especially evident in online machine learning, where data is fed sequentially into the connectionist network. Previous methods, such as rehearsal and pseudo-rehearsal systems, have attempted to alleviate catastrophic interference by introducing past data or replicated data into the data stream. While these methods have proven to be effective, they add additional complexity to the model and require the saving of previous data.
PSS: Point Semantic Saliency for 3D Object Detection

Efficient fusion on LiDAR-camera data for 3D object detection is a challenging task. Although RGB image provides sufficient texture and semantic features, some of them are unrelated to the targeted objects, which are useless and even misleading for detection task. In this paper, point semantic saliency (PSS) is proposed for precise fusion. In this scheme, physical receptive field (PRF) constraint is built to establish the relation of 2D and 3D receptive fields from camera projection model. To increase the saliency of the pixel from targeted object, we propose PSS to extract salient point feature with the guidance of RGB and semantic segmentation images, which provides 2D supplementary information for 3D detection. Comparison results and ablation studies demonstrate that PSS improves the detection performance in both localization and classification. Among the current single stage detectors, our method improves APs by \(0.62\%\) for hard level and mAP by \(0.31\%\).
Modern cryptanalysis of the Truppenschlüssel

Truppenschlüssel (troop cipher) was a manual cipher used by the German Army during World War II. Based on more than a hundred authentic messages that survived the war, a cryptanalysis is performed. The exact encryption procedure is investigated via two plaintext-ciphertext compromises. A specific ciphertext-only breaking tool is developed, utilizing the hill climbing technique. This leads to successful breaks of most of the messages.
Domain-Specific On-Device Object Detection Method

Object detection is a significant activity in computer vision, and various approaches have been proposed to detect varied objects using deep neural networks (DNNs). However, because DNNs are computation-intensive, it is difficult to apply them to resource-constrained devices. Here, we propose an on-device object detection method using domain-specific models. In the proposed method, we define object of interest (OOI) groups that contain objects with a high frequency of appearance in specific domains. Compared with the existing DNN model, the layers of the domain-specific models are shallower and narrower, reducing the number of trainable parameters; thus, speeding up the object detection. To ensure a lightweight network design, we combine various network structures to obtain the best-performing lightweight detection model. The experimental results reveal that the size of the proposed lightweight model is 21.7 MB, which is 91.35% and 36.98% smaller than those of YOLOv3-SPP and Tiny-YOLO, respectively. The f-measure achieved on the MS COCO 2017 dataset were 18.3%, 11.9% and 20.3% higher than those of YOLOv3-SPP, Tiny-YOLO and YOLO-Nano, respectively. The results demonstrated that the lightweight model achieved higher efficiency and better performance on non-GPU devices, such as mobile devices and embedded boards, than conventional models.
Monitoring the readiness of leaders and teachers of educational organizations for inclusive education

The article highlights the issues of determining and developing special conditions in educational institutions of different levels of education for children with disabilities. The results of studying the state of readiness of leaders and teachers of educational organizations for inclusive education are presented. Based on the results of the monitoring, conclusions were drawn and key problems of staffing of educational organizations were identified, personnel shortages of each educational organization were identified and directions for the development of management and pedagogical teams on the organization and implementation of inclusive education were described.
Research on the Construction of Domain Sentiment Lexicon Based on Label Propagation Algorithm

With the rapid development of the Internet and the explosive growth of online information, timely monitoring and guidance of public opinion is the key to maintaining a safe online environment. Sentiment lexicon is an important corpus resource in fields such as opinion analysis, and when faced with tasks in different domains, generic sentiment lexicon has been difficult to meet the demand, so researchers have remembered to focus on domain sentiment lexicon. In this paper, we propose a method based on improved label propagation to achieve automatic construction of Chinese sentiment lexicon from the sentence-level text. The key idea is to use lexical rules and sentiment association corrections to take the computational approach of optimizing mutual information of points, both of which improve the algorithm's effectiveness in analyzing complex sentences and thus enhance the accuracy of sentiment word recognition. The experimental results show that the method can automatically build a sentiment lexicon according to the corpus, with high quality and certain domain adaptability.
An Improved Cuckoo Search Algorithm

Swarm intelligence optimization algorithm is a new type of optimization algorithm, which connects individuals into groups through team cooperation, generates swarm intelligence, and to solve practical problems. Cuckoo search algorithm is one of the typical swarm intelligence algorithms. It has the characteristics of high efficiency and simple implementation, but it has the problems of slow convergence speed and low accuracy. To overcome these shortcomings, this paper proposes a cuckoo search algorithm based on multi nest update, which uses three better nests to update other nests, so as to improve the optimal value accuracy and convergence speed of the algorithm. Experimental results show that the performance of the improved cuckoo search algorithm is significantly improved compared with the original algorithm.
ALLPC: A Lightweight Block Cipher Based on Generalized Feistel Networks for IoT

As millions of intelligent devices are gradually deployed at the edge of networks, a large amount of private and sensitive data generated at those devices need to be transmitted, stored and processed safely among these devices, which become a serve challenge because these resource constrained IoT devices cannot satisfy the requirements of conventional encryption ciphers. In this paper, we propose the extended Type-1 generalized Feistel networks (Type-1 EGFNs) and instantiate it into a lightweight block cipher called ALLPC. We analyze performance of the cipher in security, software implementation and hardware implementation. The Type-1 EGFNs overcomes the shortcomings of slow diffusion of Type-1 GFNs. Compared with other existing lightweight block ciphers, experimental results shows that ALLPC cipher can achieve compact hardware and low-performance software (e.g. low memory) while having low execution time, high throughput and low energy consumption on 32-bit ARM-Cortex M3 STM32F103 platform. In our security analysis, ALLPC cipher can attain adequate security level against many different attacks.
Improvement of AHMES Using AI Algorithms

This research aims to improve the rationality and intelligence of AUTOMATICALLY HIGHER MATHEMATICALLY EXAM SYSTEM (AHMES) through some AI algorithms. AHMES is an intelligent and high-quality higher math examination solution for the Department of Computer Engineering at Pai Chai University. This research redesigned the difficulty system of AHMES and used some AI algorithms for initialization and continuous adjustment. This paper describes the multiple linear regression algorithm involved in this research and the AHMES learning (AL) algorithm improved by the Q-learning algorithm. The simulation test results of the upgraded AHMES show the effectiveness of these algorithms.
Bebop Drone GCS Forensics Using Open-Source Tools

Parrot is one of the pioneer drone manufacturers providing drones with extensive capabilities that are capable of fulfilling various purposes of users based on their needs such as aerial mapping and drone photography. Bebop drone from Parrot is a very robust drone controlled by a ground control station (GCS) which is the single device responsible for control and communication and also serves as storage for flight logs, telemetry, GPS coordinates media files, etc. To prevent drone-related misuse, it is important that the GCS which is used to control the drone is analyzed and evidentiary data is recovered from it. This research lays special emphasis on forensic of the GCS of a Parrot Bebop drone using different open-source tools to extract artifacts relating to location, telemetry, and user credentials so that the perpetrator of the crime can be identified.