Reviewerly/exHarmony huggingface dataset:
Evaluation of SFLA and TLBO Algorithm for Path Planning of Mobile Robots in MATLAB

Autonomous robots are employed in manufacturing units and industries as they can perform complex tasks in hazardous and harmful environments such as space explorations, submarines, and mining work. When a robot can traverse from start to goal location while evading obstacles, it is called an autonomous robot for which path planning is essential. All evolutionary and swarm intelligence approaches are probabilistic-based and entail few to many control parameters based on problem and application area. Shuffled frog leaping algorithm (SFLA) is one such example that requires six problem-specific parameters, while teaching learning-based optimization (TLBO) is a specific parameter-less algorithm. Thus, there is no burden of tuning control parameters in TLBO. In this work, comparison of both these algorithms is done on MATLAB 2018a to demonstrate the results that despite the usage of no specific parameters, TLBO outperforms SFLA algorithm for path planning and obstacle avoidance problem (PPOA).
A Cross-Chain Solutions Based On Proxy Network

Most of the current research on cross-chain technology focuses on the efficiency and security of currency transactions, which cannot be well applied to the non-financial field of storing large-volume engineering documents and data. Cross-chain supervision and storage is more difficult, and data structure conversion is complicated. To solve these problems, this paper applies node performance index and identity evaluation techniques to engineering document data cross-linking, and proposes a cross-linking scheme of the agent network. Based on the government node characteristics of blockchain in the field of engineering document management, an agent node election algorithm is designed and an agent cross-chain network is constructed. Simulation results show that this scheme can complete agent election and cross-chain message forwarding, and has application value.
Comparative Analysis of White Blood Cell Cancer Detection Using Image Processing and IoT

Computerized analysis of white blood cells tumor such as Leukemia and Myeloma is an essential testing biomedical investigate point. Herein, a comparative analysis of image processing algorithms to detect the cancer is made and patient’s health is monitored using IOT also analyzed. The work could be useful for developing and exploring the new applications of image processing in IOT based systems.
The Trajectory of an Undergraduate Researcher

Abstract This article focuses on the progress of an undergraduate English major on the scholarship continuum outlined by Laurie Grobman (2009). The student engaged in authentic research in a research methods course for English majors, a class that also meets a university requirement of “quantitative intensive,” and she completed two research projects of note. Her journey has implications and significance for faculty in designing undergraduate research experiences.
Cost-forced collective potentiality maximization by complementary potentiality minimization for interpreting multi-layered neural networks

The present paper aims to propose a new type of information-theoretic method to augment the ratio of collective potentiality to its cost for the interpretation. Collective potentiality represents how many components, such as connection weights or neurons, are collectively used to deal with relations between inputs and outputs, while individual potentiality shows how an individual component potentially contributes to the inputs or outputs. From our viewpoint, the conventional interpretation methods have focused on the individual potentiality of components, ignoring these collective behaviors. Thus, the present paper stresses that neural networks should try to deal with the collective properties of components and to examine the final results from multiple points of view. For implementing this concept of potentiality maximization, we introduce the complementary potentiality minimization, which aims to reduce the strength of larger weights as much as possible, and at the same time, it can be used for increasing the potentiality. The method was applied to two intuitively interpretable data sets, namely, the absenteeism and online shoppers data sets. With both data sets, experimental results confirmed that the method could increase collective potentiality and considerably reduce the corresponding cost. The new method could extract many groups of weights with the same small strength, all of which tried to respond to the coming inputs, while the conventional methods, including the regularization ones, tried to produce a smaller number of stronger connection weights very selectively. In particular, the present method could produce stable compressed weights similar to the original correlation coefficients of the data sets, meaning that simple, independent, and linear relations could be detected, contrary to the presupposed non-linear ones. Thus, the collective interpretation, applied to two business data sets, revealed a possibility that, behind seemingly complicated non-linear relations between inputs and outputs, neural networks with collective potentiality augmentation with a smaller cost can extract the very simple relations for easy interpretation. All complicated relations can possibly be generated, based on those simple ones.
A Novel Multi-source Domain Learning Approach to Unsupervised Deep Domain Adaptation

Even though it is anticipated that training and test data come from same distribution, but in many practical applications, they usually have different distributions, resulting in poor classification performance. To overcome this distribution difference, various Transfer Learning (TL) approaches such as Domain Adaptation (DA) have been proposed. Many existing DA approaches are capable of minimizing the distribution difference of single source and target domains, resulting in the transfer of useful information from only one source domain to the target domain. However, the useful information can also be transferred from multiple source domains to the target domain for further improving the performance. Therefore, in this paper, we proposed a novel Convolutional Neural Network (CNN) architecture that considers three (or more depending on a number of source domains) stream CNN networks and introduces domain confusion and discriminative losses, to learn a representation that is both semantically meaningful and domain invariant. Extensive experiments on different tasks of domain adaptation dataset such as PIE show that considering multiple sources can significantly boost the performance of deep domain adaptation model. As a result, our approach outperforms existing state-of-the-art methods in cross-domain recognition.
Performance Analysis of Class Imbalance in Link Quality Estimation

In the wireless sensor networks, one of the common reasons that affect the accuracy of link quality estimation models is class imbalance. Machine learning-based methods focus on maximizing the total accuracy over the entire dataset leading to more attention being paid to the majority class samples. Hence, the minority class samples are poorly predicted by the learning model. This paper aims to study the effect of class imbalance on link quality estimation models. The stratified sampling algorithm is used to change the class imbalance ratio to analyze the relationship between the estimator and the class imbalance ratio. We carry out the experiments in real campus parking lots scenario and office scenario. The experimental results show that the relationship between the precision of linear estimator and the class imbalance ratio is concave. The precision of nonlinear estimator is best when the link quality sample is imbalanced.
A Secure and Privacy-Preserving Data Transmission Scheme in the Healthcare Framework

With the continuous development of the Internet of Things (IoT) and people’s demand for telemedicine, smart healthcare technology is developing rapidly. Wearable sensors, the basic components in smart healthcare, contain patients’ sensitive data such as physiological data and personal health information. Meantime, during the transmission and interaction of those sensitive data, patients’ privacy may leak which causes harm to them. Therefore, data security for wearable sensors is essential to protect patient’s privacy. In our paper, a secure and privacy-preserving data transmission scheme in the healthcare framework is proposed. In particular, the k-out-of-n OT technology is introduced, and a lightweight \((OT)^n_k\) protocol is designed to ensure the two-way privacy of the communication parties and reduce the communication overhead during transmission. In addition, theoretical and experimental analyses indicate that the proposed scheme is practical for data transmission with high security and efficiency.
Flat and Shallow: Understanding Fake Image Detection Models by Architecture Profiling

Digital image manipulations have been heavily abused to spread misinformation. Despite the great efforts dedicated in research community, prior works are mostly performance-driven, i.e., optimizing performances using standard/heavy networks designed for semantic classification. A thorough understanding for fake images detection models is still missing. This paper studies the essential ingredients for a good fake image detection model, by profiling the best-performing architectures. Specifically, we conduct a thorough analysis on a massive number of detection models, and observe how the performances are affected by different patterns of network structure. Our key findings include: 1) with the same computational budget, flat network structures (e.g., large kernel sizes, wide connections) perform better than commonly used deep networks; 2) operations in shallow layers deserve more computational capacities to trade-off performance and computational cost. These findings sketch a general profile for essential models of fake image detection, which show clear differences with those for semantic classification. Furthermore, based on our analysis, we propose a new Depth-Separable Search Space (DSS) for fake image detection. Compared to state-of-the-art methods, our model achieves competitive performance while saving more than 50% parameters.
Core Skill Decomposition of Complex Wargames with Reinforcement Learning

In recent years, Reinforcement Learning (RL) agents were able to solve the most challenging games to the extent that they competed and even surpassed the most successful human players. This suggests that RL methods are well suited for wargames where the complexity arises from very long decision horizons, sparse rewards, and large action spaces. Due to the complex nature of wargames, even with RL, convergence to a near-optimum solution requires an immense amount of experience and makes the solution sample inefficient. In order to address the inefficiency, we propose to divide the game into simpler sub-games, where each sub-game covers a core skill of the game. These sub-games have shorter decision horizons and smaller action spaces compared to the main game. We employ a curriculum learning setting with a hierarchical control structure, where the curriculum consist of simpler sub-games. We choose Starcraft II as our test bench as it posses the common features of wargames and it has been extensively used in wargame scenarios. We empirically show that our proposed hierarchical architecture is able to solve a complex wargame environment based on Starcraft 2 game whereas the non-hierarchical agent fails to solve. We further observed that a set of core-skills is sufficient to achieve near-optimal scores and a larger set of skills only marginally improves the performance.
Using Version Control and Issue Tickets to detect Code Debt and Economical Cost

Despite the fact that there are numerous classifications of technical debt based on various criteria, Code Debt or code smells is a category that appears in the majority of current research. One of the primary causes of code debt is the urgency to deliver software quickly, as well as bad coding practices. Among many approaches, static code analysis has received the most attention in studies to detect code-smell/code debt. However, most of them examine the same programming language, although today’s software company utilizes many development stacks with various languages and tools. This problem can be resolved by detecting code debt with Issue/Ticket cards. This paper presents a method for detecting code debt leveraging natural language processing on issue tickets. It also proposes a method for calculating the average amount of time that a code debt was present in the software. This method is implemented utilizing git mining.
The Strategies of Accompanying Reading Mothers Based on Educational Informatization

“Accompanying reading,” as the name implies, means that parents accompany their children to study. There is a story of Meng Mu’s three moves in the ancient times. In fact, Meng Meng is an excellent parent for accompanying students. In order to give her children a good schooling environment, she did not hesitate to move again and again and finally found the treasure of the school and settled down next to it. Mencius had outstanding achievements. Therefore, high-quality accompanying reading is very necessary. In the information age, in order to make accompanying reading more scientific, this article launches a study on the accompanying reading strategies of accompanying mothers based on educational information. This article analyzes through a combination of questionnaire survey and literature review method and finds the shortcomings in traditional accompanying reading through questionnaire survey (decreases children’s consciousness, enhances dependence, reduces learning effect, increases psychological pressure, increases family financial burden and deepens accompanying mother contradictions with children) and then reviews the domestic research data on accompanying reading for these shortcomings and summarizes the accompanying reading strategy for accompanying mothers based on educational information.
Privacy-Preserving Distributed Support Vector Machines

Federated machine learning is a promising paradigm allowing organizations to collaborate toward the training of a joint model without the need to explicitly share sensitive or business-critical datasets. Previous works demonstrated that such paradigm is not sufficient to preserve confidentiality of the training data, even to honest participants. In this work, we extend a well-known framework for training sparse Support Vector Machines in a distributed setting, while preserving data confidentiality by means of a novel non-interactive secure multiparty computation engine, that preserves data confidentiality. We formally demonstrate the security properties of the engine and provide, by means of extensive empirical evaluation, the performance of the extended framework both in terms of accuracy and execution time.
The Application of Computer Electronic Information Technology in Engineering Management

In today's era, the rapid development of network information technology has had a huge impact on people's lives. Various industries have widely used computer electronic information technology, and has had a positive impact, changed people's traditional way of life, and also greatly improved work efficiency. As a complex work, engineering management will make reasonable use of computer electronic information technology, and then the quality and efficiency of the work will be significantly improved, which can achieve scientific management. Therefore, this paper mainly studies the application of computer electronic information technology in engineering management, understands the significance of its application in engineering management, and improves the application effect of computer electronic information technology in engineering management.
Application of KKS Coding and QR Code Technology in Transmission Asset Management

Based on the research and application of transmission equipment asset life intelligent control technology, this paper introduces the current problems of traditional transmission asset management, the construction of transmission asset life cycle information coding system and the expected application results. In order to accelerate the digitization of the new power system and the intelligence of the asset management work of the whole life cycle to improve the flexibility and efficiency of asset management, this paper builds transmission asset life cycle encoding system using Interactive Location Code, Item Information Code, Equipment Information Code and Check Code, uses the application of digital scanning device and QR code technology to break the data barrier of multiple service systems and realize the tracking and recording of transmission asset life cycle information.
Application Analysis of Special Effects Technology in Film and Television Post-Production

With the rapid development of science and technology innovation, people’s spiritual pursuit is constantly improving, we need more and more high-quality visual design and production works. Visual design and production staff began to pay attention to how to make high-definition pictures and strong impact sound effects. The VFX technology of film and television emerges as the times require. Its cool expression techniques enable film and television works to show new visual images. Therefore, the application of VFX technology is becoming more and more popular, even replacing the previous film and television shooting and production. In the post-editing and VFX production of film and TV series, the VFX technology recreates and improves the elements such as picture and sound effects, which can make the content, picture and sound of film and television works have higher artistic value. Therefore, this paper mainly studies the development status, categories and application characteristics of VFX, aiming at the rational use of VFX technology in post-production to create more artistic and ornamental works.
TAX-B2BREC: Multi-Stage Cascade Downstream Company Recommender System Based on Taxation Data

The public crisis triggered by the COVID-19 pandemic has disastrous effects for B2B markets. With the supply chain and trade disrupted, the benefits of the company have been affected to varying degrees. In order to help companies find potential customers and recover the supply chain, we propose a multi-stage cascade downstream company recommender system based on taxation data. The proposed system can recommend potential buyers for upstream companies, which can help upstream companies find new sales channels. This system includes data processing, matching module, ranking module and system deployment. In the match module, we propose a hybrid recall algorithm to generate the candidate enterprises. In the ranking module, we use DCNV2 model to rank the candidate companies. Moreover, the multistage cascade recommendation algorithm achieves better results compared with the traditional algorithm in B2B recommender system.
Research and Hardware Implementation of Binocular Vision Obstacle Avoidance for UAV

We analyze the advantages and disadvantages of the current mainstream UAV obstacle avoidance solutions and focus on the UAV binocular vision obstacle avoidance method for UAV flight safety. A binocular vision hardware system based on the principle of binocular stereo vision and combined with OpenCV is built to realize the obstacle detection function of UAV binocular vision obstacle avoidance. The BM and SGBM algorithms are chosen as the main strategies for the stereo matching step in the obstacle avoidance method, and the Canny operator edge detection is used to constrain the stereo matching process in the system. The experimental results show that the UAV binocular vision obstacle avoidance method has a smooth and clear output parallax map and good obstacle ranging effect after the Canny operator edge detection constraint in the system, and the detection results apply to the subsequent UAV obstacle avoidance behavior, which can effectively guarantee the UAV flight safety.
Fast Calibration with OTS for AR-based Surgical Navigation

With the augmented reality (AR) surgical navigation system, surgeons can more intuitively see the lesions during surgery. In this respect, calibration is an essential step in optical visual system for AR. While there have been various calibration methods to register virtual holographical space and surgical space, the operation is considerably complex and time-consuming for users unfamiliar with these operations. For this, we adopt HoloLens 2 as the AR display and propose a calibration method between an optical tracking system (OTS) and the HoloLens. With this method, the transformation matrix between the calibratiion tool and the camera on HoloLens can be fixed and thus users can quickly complete the calibration task. An AR-based surgical navigation system is further developed. Experiments show that in the circumstances of a relatively satisfying average error of 7.212 ± 1.098 mm, users can finish the process of calibration within 15s.
End-to-End Bangla Speech Synthesis

Text-to-Speech (TTS) system is a system where speech is synthesized from a given text following any particular approach. Concatenative synthesis, Hidden Markov Model (HMM) based synthesis, Deep Learning (DL) based synthesis with multiple building blocks, etc. are the main approaches for implementing a TTS system. Here, we are presenting our deep learning-based end-to-end Bangla speech synthesis system. It has been implemented with minimal human annotation using only 3 major components (Encoder, Decoder, Post-processing net including waveform synthesis). It does not require any frontend preprocessor and Grapheme-to-Phoneme (G2P) converter. Our model has been trained with phonetically balanced 20 hours of single speaker speech data. It has obtained a 3.79 Mean Opinion Score (MOS) on a scale of 5.0 as subjective evaluation and a 0.77 Perceptual Evaluation of Speech Quality(PESQ) score on a scale of [-0.5,4.5] as objective evaluation. It is outperforming all existing non-commercial state-of-the-art Bangla TTS systems based on naturalness.
Non-redundant Prevalent Co-location Patterns

In this chapter, we study the problem of reducing redundancy in a collection of prevalent co-location patterns by utilizing the spatial distribution information of co-location instances. We first introduce the concept of semantic distance between a co-location pattern and its super-patterns, and then define redundant co-locations by introducing the concept of δ-covered, where δ (0≤δ≤1) is a coverage measure. We develop two algorithms RRclosed and RRnull to perform the redundancy reduction for prevalent co-location patterns.