Reviewerly/exHarmony huggingface dataset:
Recognition and Classification of Stone Inscription Character Using Artificial Neural Network

Recognizing character from natural images is a challenging problem that has received substantial attention in the field of character recognition. Inscriptions are the earliest form of writing, found in a typically conventional form, and are to be deciphered and translated into a contemporary language form in order for them to be read and their contents understood. The traditional method of estampaging contents from stone inscriptions is cumbersome and time-consuming. This work attempted a new approach on implementation of the artificial neural network (ANN) to recognize ancient Tamil character. The digitally acquired eleventh-century stone inscription image is preprocessed, and binarized character image is given as an input to the system. Firstly, the system extracts the qualitative and quantitative features. Secondly, classification is done by ANN. These features are then trained to get the recognized character. The classification accuracy rate achieved is 80%.
A Blockchain-Based Cooperative Perception in Internet of Vehicles

In the Internet of Vehicles (IoVs), cooperative perception allows vehicles to share the sensor data with each other, so as to increase the perception range of vehicles beyond their field of view. This enables vehicles to obtain more accurate sensing information while driving and improves the safety of vehicles on the road. However, malicious nodes can send false information and poison the cooperative perception process. Therefore, how to guarantee the security of cooperative perception is crucial. In this paper, we propose a blockchain-based scheme for the post hoc electronic forensics of cooperative perception. Applying blockchain in cooperative perception is however challenging. Due to the high mobility of vehicles, the connection of vehicles to the Internet is intermittent and unpredictable. In this scenario, the security and effectiveness of blockchain can not be guaranteed as vehicles are offline. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Offline refers to that the vehicles can not connect to the Internet, but they can use vehicle-to-vehicle communication to share data. and can not update the blockchain on time. On addressing the issue, We develop an offline blockchain scheme that is composed of offline and online phases. In the offline phase, vehicles transact by sending a commitment. In the online phases before and after the offline phase, the deposit and arbitration mechanisms are proposed to defeat potential attacks in the offline phase. Lastly, the consortium blockchain is deployed to store the records of the cooperative perception process so that the records are tamper-proof and can be traced. Using extensive evaluations, we show the effectiveness of the proposed scheme.
Differential Privacy Algorithm for Integrated Energy System Based on Improved K-means

This paper studies the privacy protection method of k-means clustering integrated energy system based on improved differential privacy protection. Firstly, it introduces the research status of integrated energy system and privacy protection, and the basic principles and methods of differential privacy protection. In order to solve the problem that the reliability and concealment of clustering results of k-means clustering method can not be taken into account, a new differential privacy k-means clustering method is proposed, and it is proved that it satisfies differential privacy protection. Finally, the experiment proves that the improved differential privacy protection k-means clustering integrated energy system privacy protection method can protect data privacy and improve the stability of clustering effect, which greatly improves the data privacy protection of integrated energy system.
Incorporating Proportional Sparse Penalty for Causal Structure Learning

Inferring causal relationships is key to data science. Learning causal structures in the form of directed acyclic graphs (DAGs) has been widely adopted for uncovering causal relationships, nonetheless, it is a challenging task owing to its exponential search space. A recent approach formulates the structure learning problem as a continuous constrained optimization task that aims to learn causal relation matrix. Following it are various variants, such as nonlinear variants which uncover nonlinear causal relationships. However, the nonlinear variant which considers an ordinary sparsity constraint as part of its optimization objective may not effectively eliminate spurious edges, i.e., wrongly predicted edges. In this paper, we investigate the defect that the ordinary sparsity constraint inherently contains, that is, when the model predicts spurious edges, the sparsity constraint may be unable to make the relation matrix sparse to help eliminate such spurious edges, and based on the theoretical and empirical analysis of the defect, we propose the proportional sparse penalty constraint PSP which replaces the ordinary sparsity constraint with a normalized first-order matrix norm. We then compare our proposed model GAE-PSP with three models to show considerable performance improvement. We further conduct three comparative experiments and show that the PSP can make the relation matrix sparse to eliminate spurious edges.
Ambient-oriented CCA Modeling in Agriculture

Due to its complexity, Cyber-Physical and Social Systems (CPSS) processes require prior modeling and verification. When developing a system for intelligent agriculture, it is necessary to consider the spatial aspects of intelligent IoTs. For this purpose, we use ambient-oriented CCA (Calculus of Context-aware Ambients) modeling, for the implementation of which we have created a special tool – a visual CCA editor, through which we model and verify the basic scenarios in the smart agriculture system.
A New Empirical Approach for Estimating Solar Insolation Using Air Temperature in Tropical and Mountainous Environments

Solar irradiance is an available resource that could support electrification in regions that are low on socio-economic indices. Therefore, it is increasingly important to understand the behavior of solar irradiance. and data on solar irradiance. Some locations, especially those with a low socio-economic population, do not have measured solar irradiance data, and if such information exists, it is not complete. There are different approaches for estimating solar irradiance, from learning models to empirical models. The latter has the advantage of low computational costs, allowing its wide use. Researchers estimate solar energy resources using information from other meteorological variables, such as temperature. However, there is no broad analysis of these techniques in tropical and mountainous environments. Therefore, in order to address this gap, our research analyzes the performance of three well-known empirical temperature-based models—Hargreaves and Samani, Bristol and Campbell, and Okundamiya and Nzeako—and proposes a new one for tropical and mountainous environments. The new empirical technique models daily solar irradiance in some areas better than the other three models. Statistical error comparison allows us to select the best model for each location and determines the data imputation model. Hargreaves and Samani’s model had better results in the Pacific zone with an average RMSE of 936,195 Wh/m2 day, SD of 36,01%, MAE of 748,435 Wh/m2 day, and U95 of 1.836,325 Wh/m2 day. The new proposed model showed better results in the Andean and Amazon zones with an average RMSE of 1.032,99 Wh/m2 day, SD of 34,455 Wh/m2 day, MAE of 825,46 Wh/m2 day, and U95 of 2.025,84 Wh/m2 day. Another result was the linear relationship between the new empirical model constants and the altitude of 2500 MASL (mean above sea level).
WST+iMPT: A High-performance Incremental Verification World State Model for Massive Accounts

MPT (Merkle Patricia Tree) is the core storage structure of blockchains as the integrity of the account state data can be verified by the MPT structure, which is the sum of the state information generated by the blockchain system based on the account model after the execution of a transaction. As the number of transactions increases, the amount of blockchain state data grows exponentially, which poses a huge challenge for efficient access to blockchain state data. In this paper, a new world state model is proposed to address this problem. Firstly, the organization mode of a full node and the total state is improved. A state tree is constructed for some states in blocks, and the organization mode and insertion mode of state construction are redefined. Then, the state storage mode is improved, which simplifies the state storage model and changes the full storage of tree nodes in the world state, and its state update and maintenance are introduced. Experiments show that the proposed model significantly reduces memory consumption and improves retrieval efficiency. When the number of transactions in a block is 1200, the query efficiency of our proposed model is three times of MPT. In terms of memory consumption, the memory consumption of our model is only 5% of MPT.
From Prose to Prototype: Synthesising Executable UML Models from Natural Language

This paper presents a vision for a development tool that provides automated support for synthesising UML models from requirements text expressed in natural language. This approach aims to simplify the process of analysis - i.e. moving from written (and spoken) descriptions of the functionality of a system and a domain to an executable specification of that system. The contribution focuses on the AI techniques used to transform natural language into structural and dynamic UML models. Moreover, we envision a ‘human-in-the-loop’ approach where an interactive conversational component is used based on machine learning of the system under construction and corpora of external natural language texts and UML models. To illustrate the approach, we present a tool prototype. As a scoping, this approach targets data-intensive systems rather than control-intensive (embedded) systems.
Design and Development of Information Sharing and Analysis Center (ISAC) as an Information Sharing Platform

Cyber attacks are the main focus highlighted in all countries globally, both private and public sectors. It is undeniable that every private and public sector has a significant role in the digital world and cybersecurity. It is a reason for every country to improve and develop everything in cyber technology behind defense or attack. One of the solutions offered is to build a platform that can be used to share information to improve a coordinated and structured cybersecurity defense strategy. Data information, a list of attacks and threats can help stakeholders in each sector identify threats, attacks, and incidents in the cyber world. Therefore, this research will develop a web-based Information Sharing and Analysis Center (ISAC) platform to collect information and view a list of attacks and threats in the cyber world. The list of attacks and threats will be obtained through the Malware Information Sharing Platform (MISP). A two-factor authentication method will be implemented on the login form in the development of the ISAC platform. Two-factor authentication is a method used to secure user data from attackers. The research method used in building this platform is Design Research Methodology (DRM) with a prototyping development method. The results of this study obtained an ISAC portal that can be used to share information and display a list of threats and attacks received from the MISP platform.
A robust quantum private query based on six-qubit with decoherence

In order to deal with the error of oblivious key distribution caused by quantum channel noise, a new quantum privacy query (QPQ) protocol with six-qubit with decoherence is proposed. When the error rate caused by quantum channel noise is less than 30%, the final key bit error rate of this protocol is better than that of other QPQ protocols using the same post-processing algorithm. Furthermore, the protocol is also a cheat-sensitive protocol, in which dishonesty of either party can be detected.
Haversine and Tabu Search in Determining the Nearest Bus Stop based on GIS

Public bus users have to wait at the bus stops that have been provided to take the bus. Failure to find the nearest bus stop according to the user's location may cause them to miss the bus. Therefore, a GIS-based system is developed to support the bus users in finding the nearest bus stop with an optimum route by utilizing Haversine and Tabu Search. The Haversine method is used to find the nearest bus stop from the user's location and the Tabu Search is used to find an optimum route from the user's location to the nearest bus stop. The system testing showed that the Haversine method gives the closest distance of bus stop to the user's location while the Tabu Search algorithm generates the optimum route with an average running time of 0.526 ms.
Practical Learning Model for Stimulate Innovative Idea of Technician to Reduce Loss in Manufacturing

This paper proposes a practical learning model for stimulate innovative idea of technician to reduce loss in manufacturing. The content is about the model of creating activities, learning process from work experience in problem-solving. The model incorporates Kolb's experience learning cycle theory into practice. Learning activities is arranged to promote the development of problem-solving skills and create an important incentive to realize the importance of correct and standardized solutions. The goal is to reduce the time to solve problems when occur problems and the waste of raw materials. As a result of this activity, technicians can analyze the cause of the problem, collaborate to find solutions and prevent problems directly by creating standard operating procedures. After applied standard operating procedures, there are fewer problems in the sealing process of canned beverage causing from adjusting the seamer machine, as well as reducing the waste of raw materials and the time for solving the problems following as the target.
Software Quality Practices in Growing Startups

Startups arise in environments of extreme uncertainty, with few resources and the need to scale quickly. In the growth phase, they still need agility, but they tend to concern more with software quality and the development process by having more human and financial resources. Studying the factors that lead startups in the growth phase to seek quality in their processes can help new startups anticipate implementing quality practices. In this paper, we aim to identify how startups adopt quality practices in the growth phase. We carried out a multiple case study with four growth phase startups. We applied qualitative analysis techniques to summarize the findings. The studied startups adopt limited quality practices and are influenced by the development team's maturity level, organizational culture, and experience. Furthermore, they tend to be reactive towards quality, solving the problems when they impact their product, business, customer, or when technical debt can no longer be managed. The findings are relevant to support stabilizing and growing startups to define actions to improve the quality of their processes and products. Besides, they suggest that growth phase startups begin a process of change in the organizational culture concerning quality, adopting the behavior of mature companies as soon as they get more human and financial resources.
Customer Knowledge Management (CKM) Towards Customer Satisfaction in Delivering Digital Services

To support current demanding customer need while adapting with new work norm after COVID-19 strike, digital services organizations need to redefine their strategy. They need to adopt CKM concept that can have an ability to flow information easily and smoothly between the organization and customer. On top of that, they can't afford any adoption failure, so they must ensure that their strategy is successfully implemented. However, there are lack of studies that come out with the CKM adoption and its enabling factors while delivering digital services. Hence, this research is aimed to identify factors to ensure organization’ strategy can be implemented successfully, and thus service quality can be enhanced and simultaneously will increase customer satisfaction. Extracted findings from literature review and further verified by an interview with the digital services expert shows that there are eleven enabling factors that have a beneficial effect on CKM adoption to enhance organization's service quality. The factors were categorized into three perspectives which are technological, organizational, and human factors.
Acceptance and Rejection Zones for a Classifier’s Predictions in Deep Learning

A big challenge in deep learning is to estimate the reliability of a classifier’s predictions. In this paper, we propose to approach this problem through the concept of acceptance, rejection and abstain zones. Given a new example, a reliability score is defined from the cosine similarities between the activation values of the model output layer and those of its correct and incorrect predictions on the test set. The corresponding zone to a prediction is determined by its reliability score. Then the prediction is accepted, for the acceptance zone. Otherwise it is rejected for the reject zone and not considered for the abstain zone. In order to empirically examine our proposition, we carried out a case study in text classification. The results obtained show the relevance of the proposed approach.
The Multi-target Tracking Algorithm Based on the MM-GLMB Filter with Doppler Information

The traditional multi-target tracking (MTT) algorithm only uses the position information of the target for target tracking, which is prone to more false alarms when dealing with the multi-target tracking problem in the dense clutter environment, and it will increase the complexity and computation of data processing, and even generate the false tracks or lose tracks. In addition, the threshold of tracking gate of the traditional MTT algorithm is fixed, and the probability of target miss detection or false alarm is greatly increased in dense clutter environment. Aiming at the above problems, a tracker is built in order to improve the tracking performance in this paper. Firstly, Doppler information (or radial velocity) is introduced based on the multiple models generalized labeled multi-Bernoulli (MM-GLMB) filter under the consideration of the correlation between range and radial velocity. Secondly, the multi-dimension tracking gate is constructed with the information of target position and velocity in polar coordinate system, and the threshold of the multi-dimension tracking gate is adjusted through a maximum likelihood adaptive method. Finally, the state of target is updated using position measurements followed by sequential updates using Doppler measurements for make full use of the Doppler information. The results of the experimental simulation show that introducing the Doppler information of the target based on MM-GLMB in the dense clutter environment and the processing of the measurements using the adaptive tracking gate can effectively suppress the clutter and significantly improve the tracking performance.
A Polynomial Multiplication Accelerator for Homomorphic Encryption using DGT

In modern information society, data security has become more and more critical. Homomorphic encryption is one of the best choice to solve the security problem of user data on the server because of its unique characteristics of allowing operations in the ciphertext. A homomorphic encryption scheme based on R-LWE is a hotspot in research and application. Polynomial multiplication in &#x2124;<inf>p</inf>[x]/(x<sup>n</sup>+1) has brought significant attention recently. The key to accelerating the FV homomorphic encryption scheme is to accelerate the polynomial multiplication [1]. Traditional accelerators use the NTT algorithm. However, the conventional NTT scheme will cause the expansion of the number of terms after the operation, requiring additional modular reduction calculations. This paper presents a hardware accelerator for polynomial multiplication based on Discrete Galois Transform. Compared with traditional NTT, DGT cuts the length of the polynomial by half [2]. At the same time, a negative loop convolution is added to avoid additional modular reduction calculations. We designs a DGT-based hardware accelerator for polynomial multiplication on the ring. At the same time, a particular improvement is made to the DGT algorithm to make it friendly to hardware design and save some cycles. Experimental results show that our accelerator via DGT can effectively improve the multiplication speed of polynomials.
A Performance Evaluation of Sentiment Classification Applying SVM, KNN, and Naive Bayes

The rising use of the internet and social networks has opened up new avenues for individuals to express themselves. It’s also a platform with a plethora of information where an individual can see other people’s thoughts, which are diverged into numerous sentiment categories and are slowly becoming a primary part of the decision. This study makes a significant contribution to sentiment classification, which is effective in determining data in a big amount of tweets with de-contextualized sentiments which are often positive or negative, or in the middle. To accomplish this, we initially pre-processed the raw data, and then draw out the meaningful words and phrases (characteristic vector), then picked the characteristic vector list, and then applied machine-learning classification methods including Naive Bayes, KNN, and SVM. And at last, we assessed the classifier’s performance using the terms recall, accuracy, and precision, as well as the F1-score. Support Vector Machine has the highest accuracy of 92 percent, followed by KNN and Naive Bayes with 88 and 85 percent accuracy, respectively.
Dual-View Conditional Variational Auto-Encoder for Emotional Dialogue Generation

Emotional dialogue generation aims to generate appropriate responses that are content relevant with the query and emotion consistent with the given emotion tag. Previous work mainly focuses on incorporating emotion information into the sequence to sequence or conditional variational auto-encoder (CVAE) models, and they usually utilize the given emotion tag as a conditional feature to influence the response generation process. However, emotion tag as a feature cannot well guarantee the emotion consistency between the response and the given emotion tag. In this article, we propose a novel Dual-View CVAE model to explicitly model the content relevance and emotion consistency jointly. These two views gather the emotional information and the content-relevant information from the latent distribution of responses, respectively. We jointly model the dual-view via VAE to get richer and complementary information. Extensive experiments on both English and Chinese emotion dialogue datasets demonstrate the effectiveness of our proposed Dual-View CVAE model, which significantly outperforms the strong baseline models in both aspects of content relevance and emotion consistency.
Distance Transformer for Image Captioning

The architecture based on Transformer represents the state of the art in sequence modeling tasks. However, their applicability to multi-modal contexts, such as image captioning, has not been fully studied. In this paper. We propose a new image captioning model based on Transformer. It reconstructs the self-attention in Transformer by introducing distance dependent operator in attention modeling between different words, thus solving the problem of wrong attention in the application of Transformer in image captioning field. Our method is evaluated on the MSCOCO dataset and achieves state-of-the-art performance, improving the best result in terms of BLEU-4 from 38.4 to 39.2.