Reviewerly/exHarmony huggingface dataset:
Implementation of Asset Inventory Model Based on Intelligent Algorithm

Asset inventory is an important part of internal management in an organization, and it is of great significance for the smooth operation and normal development of the organization. The traditional manual inventory method has problems of low inventory efficiency and low accuracy. The asset inventory model based on intelligent algorithms can overcome the shortcomings of traditional methods and improve inventory efficiency and accuracy. This article introduces the implementation process of an asset inventory model based on deep learning algorithms. Firstly, the YOLOv4 algorithm is used for object detection to obtain the position and category information of the target object in the asset image. Then, the Faster R-CNN algorithm is used to locate the target, further improving accuracy. Finally, a neural network model trained on large-scale data is used to classify, recognize, and count assets. Using the actual data, this paper constructs the optimal portfolio model based on different risk measures and termination conditions: the maximization model of unit risk return, the VaR risk control model, and the risk return preference model. Genetic algorithm and Simulated annealing algorithm are used to optimize the model. Through experimental verification, the recognition rate of this model reaches over 90%, and the inventory efficiency and accuracy are greatly improved compared to traditional manual inventory methods. In summary, asset inventory models based on intelligent algorithms have unique advantages in inventory efficiency and accuracy, and have broad application prospects in the future.
Low-Latency Preprocessing Architecture for Residue Number System via Flexible Barrett Reduction for Homomorphic Encryption

Data privacy has become a significant concern due to the rapid development of cloud services, Internet of Things, edge devices, and other applications. Homomorphic encryption (HE) addresses the issue by enabling computations to be performed without the decryption of the encrypted message. However, the bottleneck of designing homomorphic encryption hardware is the complexity of computation. To tackle the long integer arithmetic, the residue number system based on the Chinese remainder theorem is used. In this paper, we propose a novel modular reduction architecture that computes the mapping of residual polynomials in parallel with high speed and low latency. We implement our proposed design in the Xilinx Ultrascale+ FPGA board (VCU118). When the input sizes are 360-bit (1440-bit), the frequency is 180MHz (168MHz) with 4 pipelining stages. Also, the area delay product (ADP) of DSP blocks of our design is reduced by 23 and 31 percent, respectively, for 360 and 1440 bits, compared to prior work.
Explainable Cyberbullying Detection in Hinglish: A Generative Approach

The escalating prevalence of online cyberbullying and trolling across various social media platforms has becomes a pressing concern. Extensive research demonstrates the detrimental impact of cyberbullying on the mental well-being of its victims. Given the sheer volume of online content, manual identification of cyberbullying instances proves unfeasible, necessitating the development of automated cyberbullying detection methods. This challenge has attracted considerable attention within the natural language processing (NLP) community, owing to advancements in machine learning techniques. However, most of the methods fail to provide reasoning for their decisions which warrants the use of interpretable models that can explain the model’s output in real-time. Interpretable models rather than black-box models with high performance are becoming popular adhering the “right to explanations” laws. Motivated by this, we create a cyberbullying corpus <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BullyExplain</i> in code-mixed language, where a post has been annotated with four labels, i.e., bully, sentiment, target, and rationales (explainability). Current work addresses the task of explainable cyberbully detection and proposes a unified generative framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BullyGen</i> by redefining this multitask problem as a text-to-text generation task. Our framework is capable of not only detecting whether the text is a cyberbully or not but also provides reasoning by predicting rationale, target group and sentiment of the text. Experimental results illustrate the efficacy of our proposed model by outperforming the state-of-the-art and several baselines by a significant margin and conclude that text-to-text generation model could be a good alternative for multitask classification problems.
Using knowledge graphs to enhance keyword search efficiency of the online encyclopedia and taxonomy system of the Office of the Royal Society

The Office of the Royal Society which is the organization to publish encyclopedias and taxonomies aims to digitize books and provide access to the online encyclopedia and taxonomy. So, we developed the online encyclopedia and taxonomy system. However, the online encyclopedias and taxonomies have many transliterated words and terminology. Hence, the word often occurs in many articles due to the relation of events. It causes the search result to be inconsistent with the search query. This paper proposes a method to construct knowledge graphs to enhance keyword search efficiency in the online encyclopedia and taxonomy system. To enhance the keyword search and disambiguation result, we construct a knowledge graph from texts and prioritize the search result by the relation in the knowledge graph. The result shows that the search results have been rearranged to the relatedness of the search query and boosts the relatedness of the result by around 16 percent overall.
Hybrid-Fusion Transformer for Multisequence MRI

Medical segmentation has grown exponentially through the advent of a fully convolutional network (FCN), and we have now reached a turning point through the success of Transformer. However, the different characteristics of the modality have not been fully integrated into Transformer for medical segmentation. In this work, we propose the novel hybrid fusion Transformer (HFTrans) for multisequence MRI image segmentation. We take advantage of the differences among multimodal MRI sequences and utilize the Transformer layers to integrate the features extracted from each modality as well as the features of the early fused modalities. We validate the effectiveness of our hybrid-fusion method in three-dimensional (3D) medical segmentation. Experiments on two public datasets, BraTS2020 and MRBrainS18, show that the proposed method outperforms previous state-of-the-art methods on the task of brain tumor segmentation and brain structure segmentation.
Categorical composable cryptography: extended version

We formalize the simulation paradigm of cryptography in terms of category theory and show that protocols secure against abstract attacks form a symmetric monoidal category, thus giving an abstract model of composable security definitions in cryptography. Our model is able to incorporate computational security, set-up assumptions and various attack models such as colluding or independently acting subsets of adversaries in a modular, flexible fashion. We conclude by using string diagrams to rederive the security of the one-time pad, correctness of Diffie-Hellman key exchange and no-go results concerning the limits of bipartite and tripartite cryptography, ruling out e.g., composable commitments and broadcasting. On the way, we exhibit two categorical constructions of resource theories that might be of independent interest: one capturing resources shared among multiple parties and one capturing resource conversions that succeed asymptotically.
Load balancing model for cloud environment using swarm intelligence technique

A distributed system with a shared resource pool offers cloud computing services. According to the provider’s policy, customers can enjoy continuous access to these resources. Every time a job is transferred to the cloud to be carried out, the environment must be appropriately planned. A sufficient number of virtual machines (VM) must be accessible on the backend to do this. As a result, the scheduling method determines how well the system functions. An intelligent scheduling algorithm distributes the jobs among all VMs to balance the overall workload. This problem falls into the category of NP-Hard problems and is regarded as a load balancing problem. With spider monkey optimization, we have implemented a fresh strategy for more dependable and efficient load balancing in cloud environments. The suggested optimization strategy aims to boost performance by choosing the least-loaded VM to distribute the workloads. The simulation results clearly show that the proposed algorithm performs better regarding load balancing, reaction time, make span and resource utilization. The experimental results outperform the available approaches.
Reply to Comments

Abstract: This is a reply to the comments by Daniel Bella, Lorina Buhr, Andrea Blättler, and Ivo Eichhorn. The individual responses attempt to identify the main challenges of the pieces and sketch answers to questions or elaborations on issues raised.
A comprehensive evaluation method of heat source tower heat pump applicability considering regional climate differences

The heat source tower heat pump system is widely used in large and medium-sized air conditioning systems due to its good energy-saving advantages. However, there is no relatively reasonable evaluation system for the applicability of heat source tower heat pumps due to significant regional climate differences. Therefore, in order to better comprehensively evaluate the applicability of the heat source tower heat pump system, a comprehensive evaluation index system for the applicability of the heat source tower heat pump system was first constructed. On the basis of this evaluation index, an applicability evaluation model based on backpropagation neural network is constructed. In response to the slow convergence speed and susceptibility to local values in the application process of this evaluation model, particle swarm optimization algorithm is used to improve it. A comprehensive evaluation model for the applicability of heat source tower heat pumps based on improved backpropagation neural networks has been constructed. For the evaluation model constructed in the study, experimental data from four different regions were selected for validation. The experimental results show that in the training set, the F-Measure value of the evaluation model reaches 0.949, and in the test set, the F-Measure value of the model reaches 0.973. The comprehensive evaluation data from four regions indicate that the heat source tower heat pump system can achieve different heating and cooling effects in different regions. This indicates that the proposed comprehensive evaluation model for the applicability of heat source tower heat pumps based on this improved method has good evaluation results. It can conduct a good analysis of the applicability of the heat source tower heat pump system, providing effective support for developing reasonable and energy-saving refrigeration and heating methods in different regions.
Machine Learning Clustering Analysis Towards Educator’s Readiness to Adopt Augmented Reality as a Teaching Tool

The advanced digital revolution has shifted conventional teaching and learning into digital education. In consistency with digital education, Augmented Reality (AR) applications started to shine in the education industry for their ability to create conducive teaching and learning environments, especially in remote learning during the COVID-19 pandemic. Movement Control Order (MCO) implemented in the year 2020 has led to emergency remote teaching and learning without much preparation for all educators and learners. Throughout these few years, most educators got familiar with digital teaching tools and online teaching platforms. Hence, this study aims to explore educators’ readiness to adopt AR as a teaching tool in their teaching during the endemic period. A quantitative approach via questionnaire has been distributed to the Private Higher Education Institutions (PHEIs) in the states of Selangor and Kuala Lumpur. Machine learning using a clustering technique was used to find patterns between the demographics of educators towards the AR perception of educators. The results revealed that educators' perceptions of AR technology are influenced by their familiarity with it, their personal beliefs, and their attitudes toward technology. This study provides an insightful overview of the benefits of AR applications in education and the implications of the adoption of AR in Malaysian schools and educational institutions. It also highlights the importance of motivating educators and students to embrace AR as an enhancement learning tool, providing a valuable discussion for the government, learning institutions, and educators on the implementation of AR in Malaysia.
Answering to the Italian colleagues

First of all, I would like to say that I am glad to discuss with colleagues a text that I prepared alone, and I thank you for your careful reading and your willingness to engage in this kind of exchange. As Luca Leoncini observes, the notion of tenderness, as it is normally used and as it appears in dictionaries, may suggest only fragility or a small resistance to pressure, contained in the expression he mentions: ‘So tender you can cut it with a breadstick’. However, the notion of tenderness in Ferenczi is not that of the dictionaries. [...]
A Parallel Approach for RegularSearch Algorithm

The process of finding minimal subsets of features that can differentiate objects belonging to different classes, known as typical testors, is an exponential complexity problem. Several algorithms have been proposed to improve the search process efficiency by utilizing different properties and techniques, including parallelism. In this paper, we propose a parallel version of the RegularSearch algorithm to find all typical testors related to a supervised classification problem. The proposed algorithm is compared with the most recent algorithm in the literature, and the comparative analysis shows the advantages of our proposal over the compared methods, using both synthetic and real problem datasets.
SASFF: A Video Synthesis Algorithm for Unstructured Array Cameras Based on Symmetric Auto-Encoding and Scale Feature Fusion

For the synthesis of ultra-large scene and ultra-high resolution videos, in order to obtain high-quality large-scene videos, high-quality video stitching and fusion are achieved through multi-scale unstructured array cameras. This paper proposes a network model image feature point extraction algorithm based on symmetric auto-encoding and scale feature fusion. By using the principle of symmetric auto-encoding, the hierarchical restoration of image feature location information is incorporated into the corresponding scale feature, along with deep separable convolution image feature extraction, which not only improves the performance of feature point detection but also significantly reduces the computational complexity of the network model. Based on the calculated high-precision feature point pairing information, a new image localization method is proposed based on area ratio and homography matrix scaling, which improves the speed and accuracy of the array camera image scale alignment and positioning, realizes high-definition perception of local details in large scenes, and obtains clearer synthesis effects of large scenes and high-quality stitched images. The experimental results show that the feature point extraction algorithm proposed in this paper has been experimentally compared with four typical algorithms using the HPatches dataset. The performance of feature point detection has been improved by an average of 4.9%, the performance of homography estimation has been improved by an average of 2.5%, the amount of computation has been reduced by 18%, the number of network model parameters has been reduced by 47%, and the synthesis of billion-pixel videos has been achieved, demonstrating practicality and robustness.
A Bibliometric Analysis of Knowledge Sharing in the Field of Management, Business and Accounting Based on Scopus Database

As the global economic landscape has transitioned from an industrial economy to a knowledge-based one, primarily driven by the rapid advances in science and technology, it is highly advisable for businesses to incorporate knowledge management and sharing as integral components of their strategic approach. To achieve this objective, this bibliometric analysis focused on 5341 journal papers related to knowledge sharing published from 2013 to October 2023, sourced from the Scopus database, and harnessed the power of VOS viewer software. The study aims to offer insights into the volume, trends, geographic distribution, influential journals, highly-cited organizations, key authors, and predominant themes within the knowledge sharing domain. The review underscores the emergent significance of knowledge sharing and highlights its potential as a subject deserving of further scholarly exploration. It is anticipated that this research will serve as a valuable foundation for future researchers delving into the intricacies of knowledge sharing.
Evaluating random sampling bias in sentiment analysis of social media data

In this age marked by a wealth of information, the relevance of social networks has increased in a manner that is analogous to an exponential growth curve. Notably, the content that is shared on these platforms has the potential to act as a reflection of the emotional states that people are now experiencing. The importance of emotions is brought to light in the research presented here, which makes use of a technique based on a review of the relevant literature to analyze the problem of random sample bias and the effects that it has on sentiment analysis. It is possible to draw the conclusion, on the basis of the findings of the research, that the problem of random sample propensity is not a sporadic or insignificant one. In addition, the findings of the study indicate the presence of multiple types of prejudice. Because of the potential repercussions that could result from doing a distorted sentiment analysis, it is really necessary to keep your method focused.
Obstacle avoidance approaches of autonomous mobile robots in harsh environment

Obstacle avoidance has been a hotspot in the field of robotics. Good performance of obstacle avoidance is key to the success of robots, especially in harsh environment where the external environmental factors pose challenges to the robots. Currently, numerous obstacle avoidance methods are being developed in an attempt to optimize the decision of the autonomous mobile robots in harsh environment. To enhance the reliability of obstacle avoidance methods, it is essential to have a clear picture of the advances and breakthroughs in this field and see which research area needs to be improved. This paper focuses on the current obstacle avoidance methods or path-planning algorithms in the underwater environment and the space environment, with description of their works and discussion of their advantages and weakness. Current problems of these methods include lack of reliable field tests, inadequate assumption of obstacles when designing the algorithm and limited capability in handling special cases that no possible path is feasible. This paper intends to provide researchers with a generalized review of obstacle avoidance methods in harsh environment and a proposal of the potential research directions in this field in the future. It is recommended that researchers to design more complex scenarios, improve the performance of robots to avoid being trapped in special situations, and consider the adaptability of robots in different environment.
Generating superpositions of quantum states via a beam splitter with position measurement

Abstract We use the quadrature measurement to generate the novel nonclassical states via the beam splitter with two input states, i.e., a Fock state and a vacuum state. It is interesting to find that the desired target states are the Hermite polynomial excited vacuum states. Our results have shown that the zero-position detection for the position detector, the little photon number in the input state, and the high transmittance of the beam splitter (BS) are beneficial to improve the detection efficiency of finding the output states. The proposed states quantum statistical properties and squeezing effects are also studied in detail via different criteria. Our numerical analysis demonstrates that the output quantum states are new nonclassical states. Compared with the method of photon catalysis, position detection is easier to realize in experiments. Therefore, the results in this paper shall provide theoretical support for the experimental generation of several new nonclassical states.
Parts-of-Speech Tagging for Unknown Words in Assamese using Viterbi Algorithm

Objectives: This study aims to explore the application of the Viterbi algorithm for Part-of-Speech (POS) tagging in the Assamese language, focusing on tagging out-of-vocabulary words. The objective of this paper is to assess the algorithm\'s performance using various training and testing data ratios of 50:50, 70:30, and 90:10. Methods: The study utilizes the dynamic programming capabilities of the Viterbi algorithm to determine the most likely sequence of hidden states based on observable events. A corpus comprising approximately 50,000 words is employed to train the algorithm, with different ratios of this data utilized for training and testing purposes. Findings: The Viterbi algorithm achieves an accuracy of 86.34%, surpassing the state-of-the-art POS taggers for the Assamese language. The experimental evaluation demonstrates that the proposed approach outperforms previously existing research work by achieving 6.14% higher accuracy in tagging out-of-vocabulary words, highlighting its effectiveness in addressing the challenges associated with less-resourced languages like Assamese. Novelty: The results of this study contribute to the understanding and development of POS tagging techniques in less-resourced languages like Assamese. The proposed approach not only achieves superior performance in terms of accuracy but also showcases its potential for improving POS tagging in similar linguistic contexts, surpassing the achievements of previous research efforts. Keywords: Assamese, NLP, Out­of­vocabulary, POS Tagging, Viterbi Algorithm
Effective practices for increasing student's learning efficiency during the pandemic

The study aims to investigate methods and tools for studying students’ learning efficiency in online learning conditions in specialties like “nursing” and “physical therapy and ergotherapy” during the pandemic. Data was collected from 412 students of universities in Ukraine and the Republic of Kazakhstan. A survey was carried out to assess such methods, tools, and moral values that inspired students innately and motivated them to study in an online learning environment, where students rated elements of Technology Adoption and Use of Effort Expectancy lower than other elements such as moral values as important in both specialties. The elements of Conformity and Tradition were also rated highly. Students also highly appreciated the importance of developing and improving the technical, structural, and regulatory elements of online learning, as well as fostering the value of a healthcare professional survey on elements to improve the effectiveness of training future healthcare professionals in an online format. This suggests that moral and professional values of education and an inner desire to be a high-level professional and quality communication between students and teachers promote quality education in conditions of limited access to clinical practice and online learning.