Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based IoT Cross-Domain Delegation Access Control Method

The collaborative demand in the Internet of Things (IoT) is becoming stronger. One of the collaborative challenges is the security of interoperability between different management domains. Although cross-domain access control mechanisms exist in IoT, the majority of them are based on a trusted third party. In addition, the heterogeneity of multidomain policies makes it difficult for authority delegation to satisfy the principle of least authority. In this paper, we propose a blockchain-based IoT cross-domain delegation access control method (CDDAC). The delegation-trajectory-on-blockchain strategy proposed enhances the scalability of the cross-domain delegation system. The presented multidomain delegation trajectory aggregation scheme supports the forensic analysis of the cross-domain delegation system. The performance of CDDAC is evaluated in the Ropsten, which is the Ethereum’s official public blockchain test network. The experimental results show that CDDAC has faster delegation verification speed and higher decision-making efficiency than existing work, demonstrating the lightweight and scalability of the method.
Public Participation with Fuzzy Cognitive Maps to Assess Safety Perception in Urban Regeneration

In urban regeneration it is essential to promote civic engagement through public participatory processes because stakeholder involvement ensures democracy, legitimacy and effectiveness of decisions. Nevertheless, this entails dealing with a mass of fragmented qualitative information which, in the case of the public’s perception of safety, is intangible, uncertain and ambiguous. Experts need supportive methods to process this in order to make it more understandable and manageable. This paper discusses the work conducted in a case study of urban regeneration of a degraded, historical central square in the city of Bari (Italy). It presents a process designed with Scenario Workshop consultations and conducted by leveraging the Fuzzy Cognitive Map approach to model shared knowledge. This aimed to achieve a collaborative problem-solving decision-making environment to address differing intentions in situations where means and ends are confused. The development of what-if intervention scenarios enabled the evaluation of counterintuitive cause-effect links, the recognition of critical causal loops and unintended consequences and the identification of feasible choices. The results demonstrate the suitability of the proposed process as a decision support system in the field of urban policy making to facilitate learning and negotiation, allowing for innovative solutions to emerge and for the empowerment of local communities.
Artificial Intelligence for Disease Identification and Diagnosis

Application of Artificial Intelligence (AI) has revolutionized many sectors like healthcare, agriculture, finance, computer vision, ecommerce, social media, data security, and education. AI plays a vital role in the health sector, like detecting, diagnosing, predicting diseases in advance to reduce the suffering and mortality rate. Besides, application of AI techniques improves hospital management and detection of health insurance fraud. With increased automation in the medical sector, advancements in image acquisition devices and availability of personal wearable devices at affordable cost, voluminous data are being generated. Deep learning techniques can leverage this big data with powerful Graphical Processing Unit (GPU) based systems to analyze and detect hidden patterns in the data and gain insights. Deep learning techniques can learn features from big data sets to get insights that will assist doctors in early diagnosis and treatment. Medical data analysis faces many challenges like limited data availability due to privacy issues, unbalanced data sets for diseases like cancer and rare disease, unavailability of specialists for labeling the data, variation in the experts’ opinion in decision making, variability in genes, environment, and lifestyle of individuals. This chapter discusses the techniques for dealing with the challenges of medical data processing. It also presents the AI techniques for identifying and predicting different types of cancer, diabetes, cardiac, and rare diseases at an early stage using data sets of different formats like clinical data, gene expression data, and medical images. The chapter includes the sections that discuss the methods to deal with unbalanced, small, and high-dimensional data sets, data and label denoising methods, and feature representation learning using neural networks.
Optimal Policy Synthesis from A Sequence of Goal Sets with An Application to Electric Distribution System Restoration

Motivated by the post-disaster distribution system restoration problem, in this paper, we study the problem of synthesizing the optimal policy for a Markov Decision Process (MDP) from a sequence of goal sets. For each goal set, our aim is to both maximize the probability to reach and minimize the expected time to reach the goal set. The order of the goal sets represents their priority. In particular, our aim is to generate a policy that is optimal with respect to the first goal set, and it is optimal with respect to the second goal set among the policies that are optimal with respect to the first goal set and so on. To synthesize such a policy, we iteratively filter the applicable actions according to the goal sets. We illustrate the developed method over a sample distribution system.
Data Based Message Validation as a Security Cornerstone in Loose Coupling Software Architecture

Far from being a hype, cybersecurity is one of the most important aspects of our daily lives. Gaining trust in a cyber system is not an easy task, as problems and security hazards can be willingly or unwillingly introduced in it. This paper describes a security-centric architecture of the communication bus in loosely coupled heterogeneous systems. The concept is modular and designed having in mind the fulfillment of the principles of Service Oriented Architecture as well as the common functional and non-functional requirements regarding such communications.
Programming with Natural Languages: A Survey

Thomas, Julien Joseph Suresh, Vishnu Anas, Muhammed Sajeev, Sayu Sunil, K. S.Programming with natural language is a research area that has a wide range of applications including basic programming, robotics, etc. Factors like preserving the meanings, handling the ambiguity, etc. have to be considered while converting a natural language text to programming language statements. Many developments have been taken place in this area over the past few years. Different types of CFG parsers were used initially for converting the natural language texts to programming language statements. The developments in technologies based on AI have a huge impact in this area and efficient models like GPT-3 are created. These models are capable of converting natural language to target programming language more precisely. In this paper, we do a detailed and systematic study of the developments that happened in this area and list some of the relevant research works among them.
Joint Extraction of Events in Chinese Electronic Medical Records

The widely deployed of hospital information systems causes an explosive growth of the electronic medical records (EMRs). It makes the medical structured processing technologies become critical to find researchable data in the large medical dataset. However, the high quality structured processing is a challenging task, in particular due to the inherent complexity and polysemy of medical terminology. In this paper, we propose a novel approach to achieve the joint extraction of events in Chinese electronic medical records, which solves the problem of cascading error transmission in traditional models and the ambiguity of Chinese characters. We first use the Bi-directional Encoder Representation from Transformers(BERT) model to mine features from the preprocessed medical data; then based on the characteristics of Chinese, we use the Bi-directional Long Short-Term Memory(BILSTM) model to capture the semantic information of the context. The experiments were conducted on a real dataset. The F1 score of our model in the identification and classification tasks of event triggers and arguments is the highest, reaching 71.6, 68.1, 55.4 and 46.9, respectively, which proves the effectiveness of the proposed method.
Quantifying the Abundances of Minerals of Granitic Composition Using the Hapke Model of Bidirectional Reflectance

Quantitatively assessing the abundances of the composite minerals in terrestrial granite is crucial to understanding the evolutionary history of the earth’s crust and to mineral exploration as well. Prevalent methods of estimating mineral abundances based on the Hapke model by setting the optical constants of the endmembers ahead of time are no longer applicable to terrestrial granite because of the complexity of natural granite, which leads to remarkable uncertainties in these estimations. In this study, we retrieved specific photometric parameters from the bidirectional reflectance spectra measured at a range of incidence, emergence, and phase angles before they were input into the Hapke model and used to estimate the mineral abundances. Four types of granite samples containing the main granitic minerals (quartz, alkali feldspar, and plagioclase) were used to test the effectiveness of our proposed method. The effects of the particle size and dark minerals on the inversion results using the visible near and shortwave infrared (VNIR-SWIR) wavelengths were also examined. The results show that using the photometric parameters retrieved from multiangle measurements as inputs to the Hapke model can produce accurate estimations of the abundances of quartz, alkali feldspar, and plagioclase in both natural and synthetic granite samples. Furthermore, the results demonstrate that the retrieved particle sizes of the particulate samples are close to the ground measurements. Thus, the proposed approach provides a more accurate and efficient estimation of the compositions of terrestrial granites, making it feasible to quickly assess the abundances of the minerals contained in granite.
How Do Avatar Appearances Affect Communication from Others?

Many systems and services for VR spaces use avatars to represent the appearances of each user. In those systems and services, the users communicate with each other via their avatars. Currently, various techniques and researches have been studied for avatar appearance. However, the existing techniques and studies mainly regard avatars as the self-expression of each user and do not mention the influences on communication from others. In communication content, avatars are one of self-expression of each user and one of the communication tools. Therefore, this paper describes an investigation of how avatar appearances affect communication from others. The result from the research showed that the degree of deformation of avatars affects communication from others in various situations such as informal and formal situations.
An Adversarial Neural Cryptography Approach to Integrity Checking: Learning to Secure Data Communications

Securing communications is an increasingly challenging problem. While communication channels can be secured using strong ciphers, attackers who gain access to the channel can still perform certain types of attacks. One way to mitigate such attacks is to verify the integrity of exchanging messages between two parties or more. While there are robust integrity check mechanisms currently, these lack variety and very few are based on machine learning. This paper presents a methodology for performing an integrity check inspired by recent advances in neural cryptography. We provide formal, mathematical functions and an optimization problem for training an adversarial neural cryptography architecture. The proposed neural architectures can adequately solve the problem. In our experiments, a receiver can verify if incoming messages are authentic or altered with an accuracy greater than 99%. This work expands the repertoire of integrity checking methodologies, provides a unique perspective based on neural networks, and facilitates data security and privacy.
Cross modification attention-based deliberation model for image captioning

The two-pass decoding framework has been proved to considerably improve the performance of image captioning models. However, most of the existing two-pass models involve the coarse captions in assisting the refining process by simply using a conventional attention module. Such an insufficient interaction cannot provide satisfactory support for reproducing higher-quality image descriptions. In this paper, we propose a novel Cross Modification Attention (CMA) module to exploit the complementarity of images and the corresponding coarse captions to supply more reliable features for refinement. Specifically, our CMA extends the conventional attention mechanisms with a hierarchical gating network, which mutually modifies the attended vectors of both visual and linguistic modalities. Thus, it can make the visual semantic representation more unambiguous and filter out misleading information from the coarse captions. To cooperate with CMA in feature interaction, we further explore a general two-pass decoding framework, where the drafting and the deliberation model share only the image encoders rather than the whole drafting network as previous methods. Our framework provides visual features tightly coupling both decoding processes, and ensures the efficient joint optimization of the two-pass models. Moreover, we consider the coarse captions as a baseline when optimizing the deliberation model and employ a potential-oriented reward shaping strategy for reinforcement learning to pertinently improve the quality of refinement. Experiments on Flickr30K and MS COCO datasets demonstrate that our Cross Modification Attention-based Deliberation Model (CMA-DM) obtains significant improvements over single-pass decoding baselines and achieves competitive performance on MS COCO online test server.
An Effect of Binarization on Handwritten Digits Recognition by Hierarchical Neural Networks

The influence of binarization on the recognition of handwritten digits has been investigated. The main methods of converting images from grayscale to black and white images are analyzed. The binarization process is applied to images of handwritten digits. Both the Convolutional neural network (CNN) and the multilayer perceptron (MLP) architectures are proposed for an automated recognizing the handwritten digits. The CNN and MLP are trained on the raw data of images. Essential characteristics are obtained by the method of feature extraction from binarized images and then are used for the CNN and MLP training. The influence of the binarization threshold on network recognition errors is analyzed. A comparison of the recognition accuracy by the neural networks for different algorithms is carried out. It was estimated that the minimum error of combining the proposed networks does not exceed 99.86%. A new approach to the design of a hierarchical network composed of a network trained on basic images of digits and images of digits obtained by binarization is proposed. The hierarchical neural networks with an accuracy of 99.61% on the MNIST dataset are proposed.
Proximal Policy Optimization with Future rewards

Abstract Among the current reinforcement learning algorithms, the Policy Gradient algorithm (PG)[7] is one of the traditional and most widely used algorithms, but it has the disadvantage of unstable gradient estimation, and the newly Proximal Policy Optimization algorithm (PPO) [8]solves the problem. It solves the stability problem, but the update policy is slow, and it is easy to produce over-fitting when the training times are too many. In this article, a new method is proposed, referring to Asynchronous Advantage Actor-Critic (A3C)[9], the basic PPO algorithm is trained in parallel, and a method that considers future rewards is introduced, and the future rewards are also calculated to the current. In the reward, through the OPEN GYM platform and the experimental results of capturing at any position of the robotic arm, our actions can ensure a faster training speed, while also avoiding overfitting during long-term training.
Comparative Analysis of Bengali Stop Word Detection Using Different Approaches

Stop words are employed for sentence formation without contributing semantic content in any language. In the field of text mining, information retrieval, text summarizer, indexing, search, and retrieval systems, and so on, stop words must be excluded. Until now, very few works have been done for Bengali stop word detection and handle the unique problem of stop words acting as content words contextwise and vice-versa. So, in this paper different approaches are the Dictionary-based approach, POS Tagging based approach, Word2Vec based approach, and FastText based approach, is used and analyzed with 20,000 Bengali sentences. The dictionary-based approach outperformed with 92 percent accuracy, indicating that adopting the dictionary-based approach for Bengali stop word detection and solving the specific case problem can result in an automatic, reliable, and functioning system. Also applying Word2Vec and FastText based approach is the first attempt for Bengali stop word detection in Bengali language processing.
Design and Re-engineering of an Automated Solar Lawn Mower

The study focuses on introducing the design of an efficient automated solar lawn mower. This autonomous vehicle is equipped with sensory unit to avoid obstacles, houses a powerful solar panel along with solar tracking system and carries an electromagnetic trimmer which cuts grass. The model of the mower passed through several inspection simulations to refine and perfect its design. The concern of the end users were taken into consideration and several technical analysis including Kano model assessment, Quality Function Deployment, Function Structure, Bill of Materials, Material Selection and Manufacturing Process Selection were used as the baseline for making any decision regarding the lawn mower. The product features were selected based on the Kano Model of customer need assessment. The Quality Function Deployment converted the customer needs into technical requirements which helped in developing the function structure. Later the function structure aided in designing the product and accelerated the selection of materials and manufacturing processes. The purpose of this project is to assemble the necessary building blocks to develop a less costly, durable, portable, driverless, safer and reliable lawn mowing vehicle.
Unsupervised Learning Implementation for SAR Images Clustering

Unsupervised learning algorithms play a major role and participate in different applications. These algorithms work mainly on defining the hidden patterns within the dataset and clustering the data points into different groups. Unlike supervised learning, unsupervised learning works without any supervision from human, therefore, it is mainly used with unknown data to discover the underlying structure of it. In this research, two of the main unsupervised learning algorithms are implemented and evaluated in clustering of the synthetic aperture radar (SAR) images.
Invocation of Multi-Cloud Infrastructure Services in Web-Based Semantic Discovery System

In the present multi-cloud climate, the administrations offered by different cloud specialist co-ops appear to be altogether indistinguishable as for functionalities even still they vary as far as value, the computational force, stockpiling strategy, and so on. Indeed, even the generally utilized web search tools like Google and MSN neglect to distinguish proper cloud benefits that would fulfill client necessities. A cloud service essentially incorporates set of resources that needs to be composed dynamically between service provider and service broker and must be virtualized with respect to the requirements on an on-demand basis. With the development in the amount of organizations, there is an augmentation pursued and execution of cloud organizations, and making multi-cloud organization ID and disclosure emerged as a troublesome task. It is a result of changed organization depictions, non-standardized naming shows, heterogeneity in sort, and features of cloud organizations. The semantic question processor is the crucial area in this proposed structure, which receives customer, shows commonsense requirements from a GUI for the duration of the disclosure degree, and attempts to make the RDF request to locate semantically associated data in cloud organizations, and later the semantic rating of all of the recuperated cloud groups is resolved with the assistance of semantic likeness work. Finally, tests were coordinated to evaluate the request for profitability in assurance of cloud organizations subject to precision appraisal gauges, for instance, accuracy, review, and F-measure.
Distributed Emergent Agreements with Deep Reinforcement Learning

Building autonomous agents that are capable to cooperate with other machines is an essential step towards large scale application of AI systems. Especially systems comprised of multiple self-interested agents with general sum returns can profit from cooperative behavior as cooperation can help to increase the return from all agents simultaneously. A critical aspect that might undermine cooperation is given if agents cannot make credible threats or promises (called commitment problems). Inspired by this idea in this work we augment deep reinforcement learning agents with the capability to build agreements with one another, thereby enabling agents to autonomously learn at which time to cooperate with other agents. This approach, called distributed emergent agreement learning (DEAL), enables agents to commit to specific policies defined by the agreement. We evaluate DEAL with up to 16 agents, represented as Deep Q-Networks or instances of Proximal Policy Optimization in a factory domain and empirically show that agreements increase cooperation by improving both overall and agent individual returns.
The Application Analysis of the Construction Method of Minimum Entropy Unsupervised thesaurus in Ancient Chinese Word Segmentation

Ancient Chinese text segmentation is the basic work of the Intelligentization of ancient books. In this paper, the unsupervised thesaurus construction algorithm based on the minimum entropy model is applied to a large-scale ancient text corpus, and the lexicon composed of high-frequency cooccurring neighbor characters in the ancient text is extracted; and the lexicon is combined with existing word segmentation tools to perform ancient text segmentation experiment. The experimental results show that this method has different enhancement effects on the word segmentation effect of ancient texts in different periods, which shows that the vocabulary has a certain range of effectiveness. In addition, this article is one of the few works that apply monolingual word segmentation methods to ancient Chinese word segmentation. The work of this article has enriched the research in related fields.
The Alumni’s Career Prediction Based on Academic Performances Utilizing Neural Network Algorithm

This current study implemented a neural network (ANN) algorithm for managing alumni’s career prediction data based on their academic performance. Tracer Study (TS) is a tracking for alumni regarding information related to the competence of higher education at the work environment. Such system was found beneficial for universities and higher education services as their reference to enchance the achievement of study program in order to produce alumni whose comptencies are needed by industries. The neural network algorithm is commonly used to classify data based on samples from training data. According to the results of the application of the neural network algorithm, it was found that the class precision accuracy was about 88.51% with a predicted waiting period of alumni to get a job (n) <= 6 months as many as 393 data, 6 months < n <= 1 year as 38 data and 1 year < n <= 2 years as 13 data