Reviewerly/exHarmony huggingface dataset:
Vision–Language–Knowledge Co-Embedding for Visual Commonsense Reasoning

Visual commonsense reasoning is an intelligent task performed to decide the most appropriate answer to a question while providing the rationale or reason for the answer when an image, a natural language question, and candidate responses are given. For effective visual commonsense reasoning, both the knowledge acquisition problem and the multimodal alignment problem need to be solved. Therefore, we propose a novel Vision–Language–Knowledge Co-embedding (ViLaKC) model that extracts knowledge graphs relevant to the question from an external knowledge base, ConceptNet, and uses them together with the input image to answer the question. The proposed model uses a pretrained vision–language–knowledge embedding module, which co-embeds multimodal data including images, natural language texts, and knowledge graphs into a single feature vector. To reflect the structural information of the knowledge graph, the proposed model uses the graph convolutional neural network layer to embed the knowledge graph first and then uses multi-head self-attention layers to co-embed it with the image and natural language question. The effectiveness and performance of the proposed model are experimentally validated using the VCR v1.0 benchmark dataset.
Classification of Paddy Growth Phase Based on Landsat-8 Image with Convolutional Neural Network Algorithm

Abstract Food security is one of the major issues in the world. One of the related component of the issue is the provision of agricultural data. In Indonesia, there is a difference of rice production data between the Ministry of Agriculture and Statistics Indonesia (locally known as BPS). This is due to difference in data acquisition methods used. In order to predict rice production, we must know about paddy growing phase so that the prediction of rice production in a certain period can be accurately calculated. This research aims to propose a convolutional neural network method to develop paddy growth phase classification model using remote sensing data of landsat-8 images. The convolutional neural network algorithm is used to perform self-learning processes such as reading image, extracting and classifying by changing the structure of landsat-8 image into matrix or pixel. This research also conducted experiment against hyper parameter epoch with 10, 30 and 50 epochs to get good accuracy. The case research is the area of PT Sang Hyang Seri rice field in Subang Regency, Indonesia. The experiment results provided an accuracy of 80.37%. It means the convolutional neural network model is matched with the data. It is able to classify landsat-8 images.
Convergence of Technologies in Education: New Determinant of the Society Development

Purpose: The authors of the proposed research aimed at reviewing the use of electronic educational resources as modern convergent technologies, analysis and evaluation of the results of their use in the process of training highly qualified specialists who can effectively function in the conditions of integration and globalization taking place at the political, economic and socio-cultural levels.The processes of convergence of knowledge, technologies and methods in the modern system of scientific knowledge are being considered by representatives of various scientific fields: Political Science, Sociology, Biology, Psychology, Economics, Pedagogy, Information Technology, methodology and design of educational systems, etc. Convergence is regarded as a key and priority direction for the development of scientific thought. At the same time, convergence may be looked upon as a combination of various theories and practices, a complex phenomenon of the formation of an interdisciplinary environment.Large-scale changes in modern society dictate the need to train highly qualified personnel capable of solving innovative tasks. The researchers suppose that improvement of human capabilities and the methodology of self-development are impossible without the use of integrative convergence, where information and cognitive technologies play a key role.Methodology: In the framework of the proposed study, the authors consider a descriptive method to be fundamental, including elements of review, interpretation, comparison and description.Findings: The authors provide the rationale of the research issue; list the key principles of converging technologies; describe the internal structure of the cognitive theory of multimedia teaching; the researchers determine that the converged approach helps to make the learning process a practice-oriented, promotes the formation of new information culture and leads to improvement of quality of life.
Using Sub-character Level Information for Neural Machine Translation of Logographic Languages

Logographic and alphabetic languages (e.g., Chinese vs. English) have different writing systems linguistically. Languages belonging to the same writing system usually exhibit more sharing information, which can be used to facilitate natural language processing tasks such as neural machine translation (NMT). This article takes advantage of the logographic characters in Chinese and Japanese by decomposing them into smaller units, thus more optimally utilizing the information these characters share in the training of NMT systems in both encoding and decoding processes. Experiments show that the proposed method can robustly improve the NMT performance of both “logographic” language pairs (JA–ZH) and “logographic + alphabetic” (JA–EN and ZH–EN) language pairs in both supervised and unsupervised NMT scenarios. Moreover, as the decomposed sequences are usually very long, extra position features for the transformer encoder can help with the modeling of these long sequences. The results also indicate that, theoretically, linguistic features can be manipulated to obtain higher share token rates and further improve the performance of natural language processing systems.
CBIR algorithm development using RGB histogram-based block contour method to improve the retrieval performance

In this research paper, CBIR algorithm development using RGB histogram-based block contour method to improve the retrieval performance is presented along with the simulation results. CBIR to extract the visual content of an image automatically like color, texture, size, direction, distance, nearness or its shape using HSV colour RGB histogram & block contour methods is proposed here in this paper. The work presented in this paper is a new methodology of the picture retrievals by the combination of different parameters of the query image with the help of RGB histograms & the block contours. Simulations are carried out in the Matlab environment and the simulation results are observed. The CBIR based system is going to be tested using different types of test images for varieties & finally the nearest test outcomes will be got & be tabulated neatly. Here, in this proposed work using different sizes of the blocks & the no. of characteristics & the time obtained for extracting those realistic parameters could be found out & also other parameters such as the recalling, precision, characteristics were calculated and compared with existing systems. The precision and recall are used to calculate the performance of the designed system. The results presented gives precision values & the values of the recall parameter of the developed & existing CBIR systems, which shows the efficacy of the proposed methodology that is superior. Here, the observation is – the developed methodology will be highly unique, its performance will be more superior to the existing systems. Finally, to conclude, it could be iterated that this contributory work was developed to show about the RGB histogram & block contour concept involvement to extract the different features of the images and carry out the performance of system (retrieval). Finally, the results of the simulation gives the improved performance of the system (retrieval) over the others, thus showing the profoundness or the effectiveness of the research work done.
Analyzing Customer Reviews on Social Media via Applying Association Rule

The rapid growth of the use of social media opens up new challenges and opportunities to analyze various aspects and patterns in communication. In-text mining, several techniques are available such as information clustering, ... | Find, read and cite all the research you need on Tech Science Press
Hardware-Software co-implementation of a high performance and light-weight scalable Systolic-Montgomery based modified RSA for portable IoT devices

Internet-of-things (IoT) is the latest revolution in electronic industry after internet. Smart appliances, portable computing devices, mobile phones and handheld system dominates in IoT, because a large portion of world population use it. Major applications, mainly financial, e-commerce, information security and sensitive data-communication need special attention in terms of security. This creates a strong requirement for providing security solutions into these devices. Incorporating security module inside an Integrated-Circuit(IC) is a major challenge as most of the portable devices are smaller in size and highly power efficient. Hence, an optimized cryptographic implementation techniques, which differ from high-throughput desktop computers and servers need to be developed. RSA algorithm is extensively used public key cryptosystem but it suffers from long execution time. In this paper, we have designed and implemented a high performance, smaller area and low power consumption scalable Systolic-Montgomery-Multiplication based RSA (modulus sizes-1K, 2K and 4K) algorithm targeted for various IoT applications. Huffman compression is used to enhance the execution time. The design is evaluated on Xilinx Artix-7 FPGA. A practical RSA application was run on the design and the performance is superior than X86, ARM, MIPS, NEC and ST architectures. Further the design is integrated with Microblaze processor and we achieved data rate of approximately 56.76 Mbps at 44 MHz operating clock frequency for modular exponentiation operation using dedicated Direct-Memory-Access (DMA).
Estimating Age in Short Utterances Based on Multi-Class Classification Approach

Age estimation in short speech utterances finds many applications in daily life like human-robot interaction, custom call routing, targeted marketing, user-profiling, etc. Despite the comprehensive studies carried out to extr... | Find, read and cite all the research you need on Tech Science Press
Research on business continuity rating model in cloud environment

This paper proposes a data center business continuity risk grading model in a cloud environment, which determines the disaster recovery capability level of the target business system based on system attribute information, comprehensive basic level, system life cycle and return on investment, and determines the target business based on the disaster recovery capability level The business continuity protection mode of the system is to protect the target business system continuously according to the business continuity protection mode.
Security Challenges of Digital Currency System

Physical payment collection is creating a lot of safety and security challenges to handle payment collection in real-life scenarios. Digital currency has become a preferable choice in the 21st century for business transactions for payment collection. People are not aware about the utilization of digital currencies working principles & types and other digital currency integration with blockchain. We have discussed some of the digital currency system based applications that exist in today’s world, challenge faces within these applications and their security weakness within this conference paper. Proposed solution will help to boost customer confidence toward the digital currency system, enabling some methods of data transmission control and offer guidelines toward the selection process. Case study on other countries uses on the system as well as discussion between the non blockchain based and blockchain based digital currency system in order to better understand the advancement of digital currency system.
Traffic Normalization for Covert Channel Protecting

Nowadays a huge amount of sensitive information is sending via packet data networks and its security doesn't provided properly. Very often information leakage causes huge damage to organizations. One of the mechanisms to cause information leakage when it transmits through a communication channel is to construct a covert channel. Everywhere used packet networks provide huge opportunities for covert channels creating, which often leads to leakage of critical data. Moreover, covert channels based on packet length modifying can function in a system even if traffic encryption is applied and there are some data transfer schemes that are difficult to detect. The purpose of the paper is to construct and examine a normalization protection tool against covert channels. We analyze full and partial normalization, propose estimation of the residual covert channel capacity in a case of counteracting and determine the best parameters of counteraction tool.
Detecting Scenes in Fiction: A new Segmentation Task

This paper introduces the novel task of scene segmentation on narrative texts and provides an annotated corpus, a discussion of the linguistic and narrative properties of the task and baseline experiments towards automatic solutions.A scene here is a segment of the text where time and discourse time are more or less equal, the narration focuses on one action and location and character constellations stay the same.The corpus we describe consists of German-language dime novels (550 k tokens) that have been annotated in parallel, achieving an inter-annotator agreement of γ = 0.7.Baseline experiments using BERT achieve an F1 score of 24 %, showing that the task is very challenging.An automatic scene segmentation paves the way towards processing longer narrative texts like tales or novels by breaking them down into smaller, coherent and meaningful parts, which is an important stepping stone towards the reconstruction of plot in Computational Literary Studies but also can serve to improve tasks like coreference resolution.
IoT Device Authentication and Access Control Through Hyperledger Fabric

Internet of Things (IoT) is one of the sizzling technology that connects everything to everyone, everywhere. Security and privacy with confidentiality, integrity, and availability to data are among the most pressing challenge faced by IoT as well as the Internet. Networks are getting more expanded and are becoming more open, and security practices has to be uplifted to ensure protection of this rapidly growing Internet, its users, and data. In this paper, we propose a new authentication and access control mechanism for the IoT devices through a blazing blockchain technology, Hyperledger Fabric, an open-source distributed ledger platform for developing enterprise-grade permissioned blockchains. Blockchain is typically a hash-chain of blocks consisting of a number of (ordered) transactions. Fabric provides a secure and scalable permissioned platform with plug-in components that support data privacy and smartcontracts, rather than a permission-less system where anybody can access and transact data. The authentication and access control of the IoT devices is achieved by making use of newly introduced features in managing channel, chaincodes, policies, Certificate Authority (CA), and others in Hyperledger Fabric version 2.0. Our architecture has the potential to act upon different layers of the IoT in authentication and access control safeguarding the confidentiality, integrity, and availability of data.
Chaining Optimization Methodology: A New SHA-3 Implementation on Low-End Microcontrollers

Since the Keccak algorithm was selected by the US National Institute of Standards and Technology (NIST) as the standard SHA-3 hash algorithm for replacing the currently used SHA-2 algorithm in 2015, various optimization methods have been studied in parallel and hardware environments. However, in a software environment, the SHA-3 algorithm is much slower than the existing SHA-2 family; therefore, the use of the SHA-3 algorithm is low in a limited environment using embedded devices such as a Wireless Sensor Networks (WSN) enviornment. In this article, we propose a software optimization method that can be used generally to break through the speed limit of SHA-3. We combine the θ, π, and ρ processes into one, reducing memory access to the internal state more efficiently than conventional software methods. In addition, we present a new SHA-3 implementation for the proposed method in the most constrained environment, the 8-bit AVR microcontroller. This new implementation method, which we call the chaining optimization methodology, implicitly performs the π process of the f-function while minimizing memory access to the internal state of SHA-3. Through this, it achieves up to 26.1% performance improvement compared to the previous implementation in an AVR microcontroller and reduces the performance gap with the SHA-2 family to the maximum. Finally, we apply our SHA-3 implementation in Hash_Deterministic Random Bit Generator (Hash_DRBG), one of the upper algorithms of a hash function, to prove the applicability of our chaining optimization methodology on 8-bit AVR MCUs.
Consumer product emotion research using emoji

The universal use of computer-mediated communication has allowed emoji to rise to prominence for emotional expression. This motivates their inclusion in emotion-focused product research, and in less than a decade applications and methodological research have emerged. This chapter summarizes that body of research. Topics covered include the meaning of emoji and the uses of emoji questionnaires in consumer product emotion research. For the latter, practical considerations, emoji selection, and questionnaire variants are considered. Individual differences in emoji use are covered and the chapter also includes a section on other uses of emoji in consumer research. Finally, some suggestions for future research are presented.
Human Intention Estimation using Fusion of Pupil and Hand Motion

This paper addresses the problem of human intention inference in the context of human-robot collaboration by fusing information from both hand motion obtained using skeletal tracking and eye gaze obtained using pupil tracking to predict a human’s current intention. Intention is modeled as a motion profile that converges to a goal location. A Kalman filter is used on eye gaze data to obtain gaze point estimates. The gaze estimates are transformed into a reference frame common to the hand data. An IMM filter that tracks hand motion is designed which takes advantage of the gaze filter’s model probabilities by fusing them with its own. The fusion is performed with user chosen parameters that determine the degree to which each filter’s predictions are weighed over time. An experiment is designed to show the utility of the proposed algorithm in a setting in which multiple reaching tasks are completed in an unknown order. The results show that the proposed algorithm can accurately predict the human’s intention before the tasks are completed.
A Survey on Blockchain &amp; Cloud Integration

Blockchain is one of the emerging technologies with the potential to disrupt many application domains.Cloud is an on-demand service paradigm facilitating the availability of shared resources for data storage and computation.In recent years, the integration of blockchain and cloud has received significant attention for ensuring efficiency, transparency, security and even for offering better cloud services in the form of novel service models.In order to exploit the full potential of blockchain-cloud integration, it is essential to have a clear understanding on the existing works within this domain.To facilitate this, there have been several survey papers, however, none of them covers the aspect of blockchain-cloud integration from a service-oriented perspective.This paper aims to fulfil this gap by providing a service oriented review of blockchain-cloud integration.Indeed, in this survey, we explore different service models into which blockchain has been integrated.For each service model, we review the existing works and present a comparative analysis so as to offer a clear and concise view in each category.
A Receding Horizon Scheduling Approach for Search &amp; Rescue Scenarios

Many applications involving complex multi-task problems such as disaster relief, logistics and manufacturing necessitate the deployment and coordination of heterogeneous multi-agent systems due to the sheer number of tasks that must be executed simultaneously. A fundamental requirement for the successful coordination of such systems is leveraging the specialization of each agent within the team. This work presents a Receding Horizon Planning (RHP) framework aimed at scheduling tasks for heterogeneous multi-agent teams in a robust manner. In order to allow for the modular addition and removal of different types of agents to the team, the proposed framework accounts for the capabilities that each agent exhibits (e.g. quadrotors are agile and agnostic to rough terrain but are not suited to transport heavy payloads). An instantiation of the proposed RHP is developed and tested for a search and rescue scenario. Moreover, we present an abstracted search and rescue simulation environment, where a heterogeneous team of agents is deployed to simultaneously explore the environment, find and rescue trapped victims, and extinguish spreading fires as quickly as possible. We validate the effectiveness of our approach through extensive simulations comparing the presented framework with various planning horizons to a greedy task allocation scheme.
Multiple Kernel Based Transfer Learning for the Few-Shot Recognition Task in Smart Home Scene

With the development of artificial intelligence, smart home plays an increasingly important role in daily life. Since new objects may constantly appear at home and the collecting of enough training samples sometimes can be hard, the few-shot recognition task is essential and practical in smart home scene. MULticlass Transfer Incremental LEarning (MULTIpLE) is an effective algorithm that can perform transfer learning for class increment with few new samples based on Support Vector Machine (SVM). But the features of images are generally high-dimensional and the selection for kernel function affects the performance of MULTIpLE. In this paper, a new transfer learning algorithm based on multiple kernel learning, termed MULticlass Transfer Incremental LEarning based on Multiple Kernel Learning (MULTIpLE-MKL), is proposed for the few-shot recognition task in smart home scene. There are two main steps for the MULTIpLE-MKL, including the first multiple kernel learning stage and the second transfer learning stage. Specifically, multiple kernel learning is first applied in the construction of SVM models to optimize the selection of kernel function. When different kernels are calculated based on different features, the sparse kernel coefficients achieve the key feature selection. Second, the SVM models learn a new class from few samples by the virtue of the transfer learning algorithm, MULTIpLE. Compared with conventional methods, experiments based on the benchmark Caltech-256 dataset demonstrate that the proposed MULTIpLE-MKL not only maintains the good performance in original classes but also shows improving recognition ability for new classes only with few training samples.