Reviewerly/exHarmony huggingface dataset:
Blockchain implementation for IoT devices: Blockchain of Things

IoT devices are faced with issues such as security flaws, privacy concerns, inadequate interoperability, and a lack of engineering specifications. Most Internet of Things devices need constant connection to the Internet, which brings many challenges in their protection and the security of the data. This is where blockchain comes into play by providing decentralization and authentication that makes it impossible for third parties to gain access to the network. It provides much-needed privacy and flexibility, which is currently missing from the IoT infrastructure. The approach we propose improves the security of smart devices by restricting them from making direct requests to the Internet. All requests are authenticated by the blockchain interface and approved if they are correct. Another layer of security from the Internet is added by implementing a simple interface as a security gateway. This interface protects the devices from third parties that are not allowed by network rules to access local networks. Our chapter provides a solution with an overview of what can be done to implement blockchain in existing IoT infrastructure and improve the security and privacy of the system.
A Brief Discussion on Design Education and Practice in the Hypermedia Era

The hypermedia design is becoming a mainstream of digital design works because of its contagious and communicative power. However, traditional design education is not enough to teach students the comprehensive ability to develop hypermedia works independently. Based on the design teaching practice of H5, this paper discusses the key points in designing and teaching method for hypermedia works. First, the fundamental concepts of the hypermedia and H5 are discussed from technical and application levels. Next, methodologies about the design thinking are concluded and proposed. Finally, a teaching practice case for hypermedia design is analyzed to demonstrate our points. In a word, beyond the design capability of graphic elements, the hypermedia designer needs to pay more attention to the integration of various types of multimedia including video, audio, animated and other interactable elements. Therefore, it is necessary to introduce engineering thought into the design teaching procedure.
Construction Plant Security Detection And Risk Assessment System

Construction plant needs to mobilize personnel for safety inspection and supervision. However, due to the large number of Construction plant, conducting one-by-one supervision investigations consumes a lot of human resources. Therefore, this article collects construction site inform action through safety helmet recognition, face recognition, and external sensors. While monitoring construction site irregularities, the risk index of each construction site can be obtained through the risk assessment system to provide judgment information for local managers. It can supervise and manage the construction site with higher risk index.
An Efficient Image Watermarking Through BEMD and Discrete Cosine Domain Based on PSO

This article presents a robust image watermarking founded on discrete cosine transform (DCT), bi-dimensional empirical mode decomposition (BEMD) and particle swarm optimization (PSO). In encoding process, DCT coefficient is implemented on original image, and also, the BEMD decomposition is applied to disintegrate the watermark image. For the optimization, PSO is used for complex and multidimensional search. The embedding and scaling factor is embedded through the help of security key. Such a procedure is accompanied by the reverse of IDCT as well as IBEMD. Recovery algorithm is employed to extract the watermark image. The outcome of this technique shows that the suggested technique is robust compared to various different attacks. Therefore, this proposed technique is feasible for the visible quality of watermark image and improves the imperceptibility compared to other techniques.
An Evaluation on Entity Extraction and Semantic Similarity Metrics to Facilitate Medical Text Analysis Based on WordNet

This paper aims to evaluate the previous entity extraction methods and semantic similarity metrics in order to improve the entity extraction and semantic analysis process for narrative text in electronic medical records. With the dataset collected from Medical Information Mart for Intensive Care III (MIMIC-III), our results showed that WordNet-based lemmatization and Part of Speech tagging have more of a significant influence in medical text analysis. For the sematic similarity metrics, the experimental results show that the measure proposed by Lin, which is based on IC, has the highest correlation coefficient with the artificial dataset of r = 0.727. The metric combining Resnik and Tversky’s measures, which is based on features as well as IC, also has relatively stable performance, which indicates that IC and feature are the factors should be particularly considered for the future research.
An experimental approach for discovering frequent patterns

Frequent pattern mining is one of the dynamic exploration subjects in information mining.Association Rule Mining is a space of information mining that spotlights on pruning up-and-comer keys.An Apriori calculation is the most generally utilized Association Rule Mining.It assumes a significant part in all information mining assignments like bunching, grouping and affiliation examination.Recognizing all incessant examples is the most tedious cycle because of a monstrous number of examples produced.In this paper, we present a procedure for mining affiliation rules utilizing Apriori calculation in huge information bases of deals exchanges.We carry out the Apriori calculation for discovering solid affiliation rules utilizing Supermarket information, which was taken from UCI Machine Repository information.Exploratory outcomes show that this calculation can find successive itemsets and adequately mine solid affiliation rules.
Cybersecurity risks : A behavioural approach through the influence of media and information literacy

The growing use of digital media has been accompanied by an increase of the risks associated with the use of information systems, notably cybersecurity risks. In turn, the increasing use of information systems has an impact on users' media and information literacy. This research aims to address the relationship between media and information literacy, and the adoption of risky cybersecurity behaviours. This approach will be carried out through the definition of a conceptual framework supported by a literature review, and a quantitative research of the relationships mentioned earlier considering a sample composed by students of a Higher Education Institution.
The relationship between organizational commitment and organizational performance with respect to knowledge sharing (Case study: Selected Project-based organizations)

Organizational culture and organizational commitment are of significant impact on the preparation and adoption of new technology systems including knowledge management systems. Appropriate initiatives in knowledge management can considerably affect the improvement of the organizations’ economic capacity and performance. Organizational culture has dramatically impacted on organizational commitment and knowledge management. As a result, organizational cultures have been under consideration in the present study. The research method was of a descriptive type. According to the type of industry and the size of organization, organizational culture was estimated as bureaucratic in three organizations. Then, Quinn organizational culture questionnaire was distributed. According to the results of the questionnaires and short interviews conducted with staffs, the dominant culture in three organizations was realized as bureaucratic. The sample size included 183 employees and a questionnaire containing 29 questions was used. The questionnaire reliability was obtained by Cronbach's alpha coefficients and the questionnaire validity was confirmed with the aid of several experts. The present research studies the relationship between organizational commitment, knowledge sharing, and organizational performance in three organizations with bureaucratic culture. Obviously, different organizations in different countries have different organizational culture. Hence, future studies can extend it into the organizations with different cultures throughout the world. It was figured out that if the cultural grounds are established in the studied organization, the organization will become a learning organization. In such circumstances, employees would have high level of commitment in their organization, feel more conveniently, and can improve organizational performance. The results show that in a bureaucratic culture, employees have little interest in sharing knowledge and little commitment to the organization. In addition, the relationship between organizational commitment, knowledge sharing and organizational performance was confirmed. In other words, the organizational commitment can lead to knowledge sharing and ultimately, it can improve the organizational performance.
Quantification of Streaking Effect Using Percentage Streak Area

Ahmed, Sajjad Islam, SaifulThe Streaking effect is an artifact or characteristic fingerprint left by the application of median filter. Many studies have used the application of median filters on digital images. Recently, percentage streaking area (PSA) has been used as a metric to measure streaking in images. The paper performs an analysis of the percentage streak area (PSA). The work investigates streaking effect using PSA in natural images and when commonly applied filters such as Gaussian filter, Average filter, and Unsharp masking are used for image filtering. Standard image datasets UCID, BOSS, and Dresden have been used in the study, and results are presented. The investigation shows that the streaking effect can be quantified by the percentage streak area.
Predictive Modelling for Future Technology Development

The prediction models in machine learning and the application of different algorithms help in developing appropriate knowledge of future behaviour based on past data. With a view to help developing insights, this chapter explains the basics of predictive analytics with its common traits. The main objective of this chapter and the topics covered in the predictive models show how different methods of tools and algorithms are crucial for predictive analytics and steps to be used to make a good model. It also describes the basic tools and techniques to be used in predictive modelling. The discussions are based on the data of live projects in predictive modelling. It attempts to handle the predictive analytics like linear regression, multivariate linear regression, nonlinear regression, and multivariate non-linear regression. These statistical tools are crucial and critical in future technology developments.
Digital Image forgery Detection by Utilize combined Feature extraction techniques

Due to the increased revolution of digital technology, the process of information sharing, accessing becomes easier. But securing this information is the major critical task. The major threat is occurred in digital images by making forgeries. Several existing techniques are utilized for detection the forgeries in digital images. But still, it lacks inaccurate detection. Hence a novel technique is designed for detecting the forged images accurately. The main motive of this research is focused on detect image forgery and localize the forged region accurately. Initially, the input images obtained from digital image acquisition and the selected images are isolated as an overlapping patch. Polar Cosine Transform (PCT) with orthogonal kernel and Local Binary Pattern (LBP) approaches are used to extract features from these patches. From the features extracted from the PCT approach, the patches are detected using Multidimensional Spectral Hashing techniques (MSH) and the forged patches are filtered out. Alternatively, geometry-based image forgery detection is carried out using the LBP extracted features. Finally, the forged regions are located and detected in the digital image. The proposed approach's efficiency is measured and compared to current techniques
Self in Virtual Reality

Virtual reality is a tool for extending the sense of self andAwareness corporeal self- awareness corporealSelf self-awareness self-awarenessAwareness self-awareness beyond the boundaries of the physical body. To explore the main assumptions on which this idea is based, we introduce the concepts of self, body representationBody body representation (body imageBody body image, body schemaBody body schema) and bodily self-consciousnessSelf self-consciousness. Focusing on the main components of bodily self-consciousness, we review spatial perspective taking in extracorporeal experiences in neurological patients and out-of-body experienceOut-of-body experience in virtual environments as examples of faulty multisensory integrationMultisensory integration. Finally, we present an argument that virtual reality requires multiple types of explanation, involving different levels of analysis: without such a multilayered approach, explanations of virtual reality experiences are likely to be partial or distorted.
Software Service with a Plug-in Architecture for Text Readability Assessment

The problem of developing a software service with a plug-in architecture for assessing the readability of text has been considered. The problem of text readability assessment has been analyzed. Approaches to the development of a software service for text readability assessment have been considered. The structure of the service for text readability assessment has been proposed. The structure of the service has been implemented using the Python programming language and the library Natural Language Toolkit (NLTK). The results of testing the service for text readability assessment have been presented.
Studies on Performance of Image Splicing Techniques Using Learned Self-Consistency

Priya, Bhukya Krishna Das, Anup Begum, Shameedha Ramasubramanian, N.In recent years, improper image manipulation has become a significant issue across many sectors, such as in the education field, politics, entertainment sector, and social media platforms. In various society sectors, images play a crucial role in interpreting the facts and number of fraud incidents. The fraud and fact can be detected by image manipulation, which is gaining more and more attention in today’s world. Detecting such manipulations images is a major challenging problem concerning many fragmented images’ unavailability as training data. A learning algorithm has been proposed for recognizing many visually spliced images and manipulations in the image. The proposed algorithm also utilizes the existing recorded image Exchangeable Image File Format (EXIF) metadata as a supervisory image for training the model to identify the image is self-consistent, i.e., whether the content of the photo has been generated using a single imaging pipeline. The proposed algorithm is implemented to the errand of identifying and localizing the image splices. An excellent performance improvement has been observed on several benchmarks, despite not using any spliced image data while training. This model will stand out as a vital source to detect morphed images in various industries.
Feedback Network With Curriculum Learning for Earthquake Event Classification

In this letter, we propose an earthquake event classification model utilizing a feedback network and curriculum learning (CL). In particular, we propose the CL method with a feature concatenation using gated convolution so that CL can be effectively performed in consideration of the feedback structure. We show that the proposed model is effective through comparison experiments with the existing model using the earthquake dataset for Korean Peninsula and the Stanford earthquake dataset.
A Study on Edge Preservation based Noise Suppression in CT images

Enhancing image quality is one of the important factors to access the more accurate information from the medical images. Hence this paper is focused on edge preservation using noise reduction on Computed Tomography (CT) images using thresholding in wavelet domain. The existing schemes are compared with similar and recent schemes and results are also compared with some performance metrics such as PSNR and SSIM.
Forms of professional training activation among cadets in the departmental higher educational institution of the Ministry of Internal Affairs of the Republic of Kazakhstan

The article is devoted to the issues of relevance of the educational process organization for the definition, use and application of various forms of education, their constant improvement and updating. The considered forms of training are aimed primarily at the formation of cadets in departmental universities of positive motivation and interest in the final result of mastering knowledge.
The New Method of Sensor Data Privacy Protection for IoT

This article introduces the new method of sensor data privacy protection method for IoT. Asymmetric encryption is used to verify the identity of the gateway by the sensor. The IoT gateway node verifies the integrity and source of the data, then creates a block, and submits the block chain transaction. In order to avoid tracking the source of the data, a ring signature is used to anonymize the gateway transaction. The proxy re-encryption method realizes the sharing of encrypted data. On the basis of smart contracts, attribute-based data access control allows decentralized applications to finely control data access. Through experiments, the effects of sensor/gateway verification, transaction signatures, and sensor data encryption on performance are discussed. The results show that transaction delays are all controlled within a reasonable range. The system performance achieved by this method is also relatively stable.
UNAME Tool: Automatic Generation of Computer Resources Monitoring Scripts

Decision-making regarding performance issues requires computer systems monitoring at any level of service provisioning. However, most of the available tools do not always allow flexibility, or when it does, it impacts directly the resource consumption through an intrusive methodology. This paper presents the UNAME tool, which proposes the automatic generation of Shell scripts for monitoring computational resources. Through a GUI, the user can drag in or out the components to be evaluated. The Linux monitoring commands were mapped to high-level representations of the computational resources, and now the user, through little interaction or knowledge about scripting, can generate their own scripts to monitor the required system resources. To demonstrate the UNAME tool feasibility we provide a case study and an evaluation scenario. The results demonstrated the effectiveness of the proposed tool.
Accurate Aspect Ratio Estimation in Scale Adaptive Tracking

Abstract In visual object tracking, robust scale estimation is always a challenging problem. Because of the fast movement of the target, the background, the relative distance and relative azimuth angle between the target and the background usually change greatly. However, traditional correlation filtering tracking method cannot track the target accurately or even lose the target when the target scale and aspect ratio change. To address these problems, this paper adds two independent filters for the length and width scale estimation to complete the adaptive tracking on the basis of the translation filter. The experimental results in the public data set show that: compared with the original trackers, our method improves the tracking accuracy significantly, and meets the needs of real-time target tracking.
The Concept of Information Graphs as a Tool to Identify Vulnerabilities in the Information Map of an Organisation

This paper defines the information graph concept and proposes its use to describe information links, with particular emphasis on information security. The information graph is a certain extension of the concept of a graph known from graph theory. The introduction of the information graph concept makes it possible to describe and analyse processes taking place in any organisation as information streams. In terms of information security in information systems described by means of information graphs, graph structures such as information graphs with vertices with a large number of classifiers, Hamiltonian cycles, and cliques are of particular importance. The analysis of these types of graph structures make it possible to identify vulnerability in the topology of information links of an organisation, and consequently the possibility of exploiting this vulnerability by a relevant threat. The proposals presented in the paper concerning the description of information links in an organisation may serve as guidelines for the designers of information systems, including the definition of roles and relations between individual users of these systems, especially in terms of security. The paper also reviews the literature related to the discussed issue that has inspired the authors of this paper to define the concept of information graph and to work on the applications of such graphs.
Computer-aided detection of abnormality in mammography using deep object detectors

Computer-aided detection of medical abnormalities is of great importance for both doctors and patients. In this chapter, we use mammogram screening as an example and introduce two state-of-the-art deep neural networks for the detection of mass tissues. More specifically, we compare two-stage and one-stage object detectors using deep convolutional neural networks. With the limited number of training data, we use transfer learning to fine-tune the general object detectors on a publicly available mammogram dataset. Experimental results indicate that the deep learning-based approaches have great potential in mammogram screening.
Survey of NLP Resources in Low-Resource Languages Nepali, Sindhi and Konkani

Research results in different subdomains of NLP are enumerated and compared for Nepali, Sindhi, and Konkani languages. These languages are spoken by relatively small populations, they have low NLP resources, and the numbers of their native speakers are falling. Hence, the list of results points to areas where more research is required. As far as we are aware, this is the first comprehensive survey that brings together NLP resources in Nepali, Sindhi, and Konkani.
Protecting the Privacy of Voters: New Definitions of Ballot Secrecy for E-Voting

Protecting the privacy of voters is a basic requirement of any electronic voting scheme, and formal definitions can be used to prove that a scheme satisfies privacy. In this work, we provide new game-based definitions of ballot secrecy for electronic voting schemes. First, we propose an intuitive definition in the honest model, i.e., a model in which all election officials are honest. Then, we show that this definition can be easily extended to the malicious ballot box setting and a setting that allows for a distributed tallier. In fact, to the best of our knowledge, we provide the first game-based definition of ballot secrecy that models both a malicious ballot box and a malicious subset of talliers. We demonstrate that our definitions of ballot secrecy are satisfiable, defining electronic voting scheme constructions which we prove satisfy our definitions. Finally, we revisit existing definitions, exploring their limitations and contextualising our contributions to the field.
A Cloud-Native Serverless Approach for Implementation of Batch Extract-Load Processes in Data Lakes

The paper presents an approach to deal with batch extract-load processes for cloud data lakes. The approach combines multiple data ingestion techniques, provides advanced failover strategies and adopts cloud-native implementation. The suggested approach. The prototype implementation utilizes Amazon Web Services platform and is based on its serverless features. The approach can be implemented also using other cloud platforms like Google Cloud Platform or Microsoft Azure.