Reviewerly/exHarmony huggingface dataset:
Effective use of BERT in graph embeddings for sparse knowledge graph completion

Graph embedding methods have emerged as effective solutions for knowledge graph completion. However, such methods are typically tested on benchmark datasets such as Freebase, but show limited performance when applied on sparse knowledge graphs with orders of magnitude lower density. To compensate for the lack of structure in a sparse graph, low dimensional representations of textual information such as word2vec or BERT embeddings have been used. This paper proposes a BERT-based method (BERT-ConvE), to exploit transfer learning of BERT in combination with a convolutional network model ConvE. Comparing to existing text-aware approaches, we effectively make use of the context dependency of BERT embeddings through optimizing the features extraction strategies. Experiments on ConceptNet show that the proposed method outperforms strong baselines by 50% on knowledge graph completion tasks. The proposed method is suitable for sparse graphs as also demonstrated by empirical studies on ATOMIC and sparsified-FB15k-237 datasets. Its effectiveness and simplicity make it appealing for industrial applications.
Building a Privacy-Preserving Smart Camera System

Abstract Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require users to relinquish control of their data to untrusted third parties, such as service providers (e.g., the cloud). Third parties therefore can (and in some instances have) access the video footage without the users’ knowledge or consent—violating the core tenet of user privacy. In this paper, we present CaCTUs , a privacy-preserving smart Camera system Controlled Totally by Users. CaCTUs returns control to the user ; the root of trust begins with the user and is maintained through a series of cryptographic protocols, designed to support popular features, such as sharing, deleting, and viewing videos live. We show that the system can support live streaming with a latency of 2 s at a frame rate of 10 fps and a resolution of 480 p. In so doing, we demonstrate that it is feasible to implement a performant smart-camera system that leverages the convenience of a cloud-based model while retaining the ability to control access to (private) data.
Multi-Purpose Forensics of Image Manipulations Using Residual-Based Feature

The multi-purpose forensics is an important tool for forge image detection. In this paper, we propose a universal feature set for the multi-purpose forensics which is capable of simultaneously identifying several typical image manipulations, including spatial low-pass Gaussian blurring, median filtering, re-sampling, and JPEG compression. To eliminate the influences caused by diverse image contents on the effectiveness and robustness of the feature, a residual group which contains several highpass filtered residuals is introduced. The partial correlation coefficient is exploited from the residual group to purely measure neighborhood correlations in a linear way. Besides that, we also combine autoregressive coefficient and transition probability to form the proposed composite feature which is used to measure how manipulations change the neighborhood relationships in both linear and non-linear way. After a series of dimension reductions, the proposed feature set can accelerate the training and testing for the multipurpose forensics. The proposed feature set is then fed into a multi-classifier to train a multi-purpose detector. Experimental results show that the proposed detector can identify several typical image manipulations, and is superior to the complicated deep CNN-based methods in terms of detection accuracy and time efficiency for JPEG compressed image with low resolution.
Cloud Computing Security Issues and Possibilities

for organizations green cloud computing is a new computational architecture which provides a business model; organization can acquire it without paying upfront investments. Cloud computing provide the potential gain but providing security in cloud computing is very necessary. Under the cloud model, security issue is become more complicated as new dimensions entered into the security scenario.
A Survey on Deep Learning Architectures and Frameworks for Cancer Detection in Medical Images Analysis

The various hurdles in machine learning are beaten by deep learning techniques and then the deep learning has gradually become preeminent in artificial intelligence. Deep learning uses neural networks to kindle decisions like humans. Deep learning flourished as an energetic approach and clarity marked its success in various domains. The study includes some dominant deep learning algorithms such as convolution neural network, fully convolutional network, autoencoder, and deep belief network to analyze the medical image and to detect and diagnose of cancer at an early stage. As early as the detection of cancer than to treat the disease is uncomplicated. Early diagnosis was particularly relevant for some cancers such as breast, skin, colon, and rectum, which prohibit the chance to grow and spread. Deep learning contributes to enhanced performance and better prediction in detection of cancer with medical images. The paper presents the study of a few deep learning software frameworks such as tensor flow, theano, caffe, torch, and keras. Tensor Flow provides excellent functionality for deep learning. Keras is a high-level neural network API that operates above on tensor flow or theano. The survey winds up by presenting several future avenues and open challenges that should be addressed by the researcher in the future.
Digital Education in the Context of the Development of the Digital Economy: Technological Opportunities and Prospects

Digital education is education that primarily functions based on digital technologies, that is, electronic transactions that are implemented using the Internet. Digital education aims to professionalize and increase the mobility of those who study and at the present stage of ICT development, it can be considered as a technological basis for the fundamentalization of higher education. The purpose of the article is to analyze the technological capabilities of digital education on the example of the use of innovative classroom in the educational process. The article analyzes the advantages of the educational environment equipped with digital tools. Based on an expert survey, the main characteristics of the innovative classroom, as well as technological requirements for the innovative classroom, are determined and various models for the organization of the innovative classroom are presented.
Machine Learning Based Behavioral Modification

To build up a particular profile about a person, the study of examining the comportment is known as Behavior analysis. Initially the Behavior analysis is used in psychology and for suggesting and developing different types the application content for user then it developed in information technology. To make the applications for user's personal needs it becoming a new trends with the use of artificial intelligence (AI). in many applications like innovation to do everything from anticipating buy practices to altering a home's indoor regulator to the inhabitant's optimal temperature for a specific time of day use machine learning and artificial intelligence technology. The technique that is use to advance the rule proficiency that rely upon the past experience is known as machine learning. By utilizing the insights hypothesis it makes the numerical model, and its real work is to infer from the models gave. To take the information clearly from the data the methodology utilizes computational techniques.
Characterising local feature descriptors for face sketch to photo matching

Sketch and photo are from a different modality. Inter-modality matching approach requires right feature representation to represent both images so that the modality gap can be neglected. Improper feature selection may result in low recognition rate. There are many local descriptors have been proposed in the literature, but it is unclear which descriptors are more appropriate for inter-modality matching. In this paper, we attempt to characterise local feature descriptors for face sketch to photo matching. Our evaluation for the characterisation uses cumulative match curve (CMC), and we compare seven different descriptors that are LBP, MLBP, HOG, PHOG, SIFT, SURF and DAISY. The evaluation focuses only on a viewed sketch. Based on the experiments, we observed that gradient-based descriptors gave higher accuracy as compared to the others. Out of five popular distance metrics evaluated, L1 gives a better result as compared to the other similarity distance measures.
3D Trajectory Planning of Positioning Error Correction Based on PSO-A* Algorithm

Aiming at the yaw problem caused by inertial navigation system errors accumulation during the navigation of an intelligent aircraft, a three-dimensional trajectory planning method based on the particle swarm optimization-... | Find, read and cite all the research you need on Tech Science Press
The Correlation between Customer Satisfaction and Service Quality in Jordanian Uber &amp; Careem

Customer satisfaction is a great concern for all companies in all sectors, to measure customer satisfaction it is necessary to measure the service quality to examine the relationship among them. In this paper, SERVQUAL model is used to measure service quality and the effect of these dimensions (tangibles, responsiveness, empathy, assurance, reliability) in customer satisfaction on intelligent transport applications (Uber &amp; Careem) in Jordan by conducting an online questionnaire. The archived results show that Reliability has a high degree of importance on the impact of customer satisfaction and 89.2 % of the sample prefer to use Uber &amp; Careem smart app rather than traditional taxis.
The Effects of Social Media on Reading Habits

The study aimed to conduct a quantitative inquiry to understand the effects of social media on the reading habits of the students. The study explored the social media usage patterns and statistically analyzed the effects of social media on reading habits by applying descriptive and inferential statistics. A structured questionnaire was developed by analyzing the related literature and reliability test (Cronbach alpha) testifies that data collection instrument was stable enough to measure the phenomenon. Data collected from a sample of 430 students. The findings of the study revealed the positive effects of social media on reading habits. However, distraction during the time of the study noted as the adverse effect. There was a significant difference of opinion among males and females opinion in terms of certain effects. The study mentioned that Facebook, Whatsapp, Google+ and YouTube were used daily by students. Most of the students were using social media through cell phone. The study has theoretical implications for researchers and practical implications for academia, teachers, policy institutions, and higher education institutions.
Use of Cloud Computing In Higher Education of Pakistan

Cloud computing is an internet-based service of delivering technology to users and an important technological facility where mutual resources are delivered on demand. Usage of cloud computing in educational Institutions provides students as well as administrative staff an opportunity to access various applications and knowledge swiftly. Its simplicity, upfront-cost, reduced downtime and less management effort make this service acceptable for all fragments of society particularly students and teachers. Despite the fact that the cloud computing technology is very useful, such as ease of access, cost effective. It has various issues like security violations, data confidentiality, data integrity, regional and geographical restrictions over the data. Safe cloud services are required to protect its users from different external threats such as disclosure or loss of data and compromising of users’ accounts. Users’ understanding about the cloud computing’s advantages, disadvantages and security issues can protect them from potential threats while ensuring its optimal usage in education. In this paper, a survey has been conducted to identify students understanding about the advantages, disadvantages and security issues regarding cloud computing in the educational sector. A sample of 212 respondents from graduate and undergraduate programs was taken to obtain required information. The findings reveal a lack of awareness about the use of cloud computing, its benefits, and security concerns and the implications of this deficiency are critical as multiple issues like outflow of personal data and its loss is faced by its users which may have adverse social, emotional and professional effects on student’s life. Considering the usefulness as well as the sensitivity of cloud computing in educational institutions, Authors propose maximum utilization of cloud computing in educational sector while ensuring the security of its users.
A Novel M-Commerce Data Security Mechanism using Elliptic Curve Cryptography

Cryptography is a mathematical science which permits only the authorized users to access the data. Today, it is necessary for our routine life to safeguard online data like credit card numbers, bank transactions, etc. Many cryptographic algorithm-based data security mechanisms have been introduced by various researchers for protecting the online / M-Commerce data. Even though, no data security algorithms achieved the required security level with less time. For overcoming these issues, we propose a new data security algorithm called Elliptic-Curve Cryptography and Diffie-Hellman based data security algorithm for securing the M-Commerce data. Here, the key size of the Elliptic Curve Cryptography is less than RSA that will be helpful to improve the efficiency and reduce the storage space. This algorithm is able to establish a secure session key through a server over an insecure channel and also handle various illegitimate users. In addition, this new algorithm is more secure, efficient and suitable for mobile commerce environments than existing data security algorithms. For evaluating the data security mechanism, many experiments conducted with M-Commerce data that are collected from the Internet which are freely available and also proved the efficiency of the data security algorithm.
Blockchain Technology: A Step Towards Sustainable Development

The goal of this research paper is to summarize the collected works on blockchain concepts, blockchain application area, blockchain problems, and draw appropriate conclusions. Since Blockchain is relatively an innovative technology, a representative sample of research is presented, spanning the last ten years, starting with the early work in this field. Different types of Blockchain use and other digital ledger techniques have been investigated, including their challenges, security and privacy issues. The key motivation of the review study is to detect the most favorable direction for future use of blockchain and research challenges in blockchain.
Revisiting quick response code technology: corporate perspectives

Quick response code (QRC) technology is facing various impediments to its adoption and usage. It has an uncertain future amidst an evolving landscape of technology and social tools available to companies. This study examines QRC's utilising marketing executives to gain current insights on its present use as well as future direction. A mixed methods survey was used to capture qualitative and quantitative responses from corporate marketing executive perspectives about QRC. Executives view QR codes primarily as a technology to increase both strategic advantage and communication advantage with customers. The findings suggest QRC will evolve as an important technology to support future corporate marketing and sales efforts.
DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark

DBSCAN is a density-based data clustering algorithm; in image processing, data mining, machine learning and other fields are widely used. With the increasing of the size of clusters, the parallel DBSCAN algorithm is widely used. However, we consider current partitioning method of DBSCAN is too simple and steps of GETNEIGHBORS query repeatedly access the dataset on Spark. So we proposed DBSCAN-PSM which applies new data partitioning and merging method. In the first stage of our method, we import the KD-tree, combine the partitioning and GETNEIGHBORS query, reduce the number of access to the dataset and decrease the influence of I/O in the algorithm. In the second stage of our method, we use the feature of points in merging so as to avoid the time costing of the global label. Experimental results showed that our new method can improve the parallel efficiency and the clustering algorithm performance.
Effects of market orientation and learning orientation on organisational performance

The current literature on market and learning orientation has been characterised by comparing the positive impact that these variables have on organisational performance. However, it has been observed that these relationships could be nonlinear and, they might be affected by another type of condition, mainly related to changes in the environment. The present research examines the impact of market and learning orientations on organisational performance by analysing a series of quadratic effects. Additionally, it analyses the possible moderating effects of environmental complexity on these relationships. A database of SME Colombian is used, such that the nonlinear relationships between these orientations and performance are proved, as well as the conditional effects of the environmental complexity on these nonlinear relationships. According to the results, market and learning orientation have decreasingly positive effects on organisational performance, and when environmental complexity increase, the positive effect of market orientation and learning on organisational performance decreases.
Early adopters versus the majority: Characteristics and implications for academic development and institutional change

The diffusion of innovation curve has become common parlance when considering the adoption of technology-enhanced learning in higher education. Innovators, early adopters, early majority, late majority and laggards indicate the different rates of technology adoption. This study explored the views and experiences of staff at one research-led institution within one of four colleges, to determine whether there were differences between the characteristics and digital academic practices of teachers, based on their self-identification as one of the five types in a questionnaire. Subsequently, two focus groups allowed comparisons between early adopters and early majority.&#x0D; The questionnaire found differences in terms of using a VLE to foster deep thinking through discussion, teachers’ digital practices and attributes, previous engagement with developmental TEL opportunities, perceived level of support from management, and perceived usefulness of CPD events. Focus groups revealed qualitative differences in terms of the amount of time participants invested in learning new technologies, sources of TEL support, preference for different types of academic development, and how they engaged with exemplars in their own or other disciplines.&#x0D; In addition to recommendations for different types of academic development for different groups, the study highlighted the importance of early adopters in leading digital practice. Institutions need to recognise and support the unique contribution made by early adopters. They contribute to the resilience, agility and digital capabilities of an institution in responding to rapid changes, such as the Covid19 pandemic, in terms of supporting and leading other staff, and prompting the institution to expand its digital education infrastructure.&#x0D;
On digitising the Greek music tradition: simulation of the Cretan lyre for mobile devices

In this paper, an application, which simulates the Greek traditional musical instrument 'Cretan lyre' in mobile devices and is the first of its kind, is presented. This instrument was selected on the grounds that it is quite interesting in terms of its playing technique and there has never been a similar attempt to do so to date. Moreover, music in Cretan tradition has been in the spotlight and intrigued a lot of people, not only those originating from Crete. The cretanlyra.app constitutes a recreational and educational application that features a presentation of the Cretan lyre and offers the user the capability of simulated playing, using the same gestures as an actual musician would. The user is instructed by the application so that they will familiarise themselves with playing some well-known melodies of the Cretan tradition. In addition, they can combine the lute sound with playing the lyre.
Implementing E-Learning Ontology to Scale for Provenance

This paper explores the aspects of providing education through E-learning model evaluating its relevance to distance education and for ICT systems. A subset of E-learning is a Web based learning that makes the learning -easier, impressive, structured and properly managed. The paper defines an university ontology describing how e-learning provides resources which are available online and designated cloud that can be delivered anywhere any time among the users. In the proposed model data is stored in designated cloud and users are able to share efficiently the same as it provides services to learner. Provenance or trust with respect to the academic resource is a major concern in these types of models, users accessing data must be trustable which help learners, researchers, developers, and users in future work also. This paper proposes an e-learning model which is well organized and structured, such that the machine responds with the accurate, trustable, desired information and results. The paper defines an ontology for semantic structuring, semantic rendering and applies provenance on suggested ontology to achieve authentic results. It is also desired to establish trust of the source contents of the Semantic Web, with the result that a user receiving data will need to verify whether the received data from source is in fact trustable or not. The defined ontogoly is suitable for consumption of both man and machine in the context of the e-learning and Semantic data rendering Web Keywords
A lightweight fully homomorphic encryption scheme for cloud security

A decade ago, fully homomorphic encryption mechanism came as a great breakthrough in security. In homomorphic encryption, ciphertext (data in encrypted format) should be sent to the cloud, the computations are made on the ciphertext, and the result of this computation is a ciphertext form itself. If the result of the computation is decrypted, then the correct plaintext result must be obtained. Maintaining the secrecy and privacy of data, generally in cloud scenario, has become an intense challenge for present day's practical applications. However, transferring private data to any third party consists of large amount risks of disclosure of private data while computation. This problem can be addressed by performing computations on encrypted data without decrypting it. In this paper, we propose a fully homomorphic encryption framework which is lightweight in nature and utilising symmetric key. Analysis of the scheme confirms that our proposed system is efficient and practical to adopt it in various cloud computation applications. Further, to prove the novelty, we present the implementation results and given the comparative analysis of our scheme with significant state-of-the-art.