Reviewerly/exHarmony huggingface dataset:
Knowledge Management and E-Learning Effectiveness: Empirical Evidence from Jordanian Higher Education Institutions

In order to maximize the true advantage of e-learning, many higher education organizations have invested strongly in knowledge management. Yet the effect on the efficiency of e-learning of these investments remains ambiguous. The purpose of the research was therefore to determine the influence of knowledge management processes on the efficiency of e-learning in northern Jordanian private universities. The findings showed that knowledge management procedures have considerably impaired the capacity to meet the goals, excellence and quality of schooling using 101 answers by faculty members in four private colleges in the north area of Jordan. This suggests that procedures in knowledge management affect private universities ' ability to attain e-learning efficiency. In view of this research, universities should deliver the technologies they need for efficient management of their knowledge management processes and e-learning system. Universities are also to generate and share knowledge with others in a stimulating, promoting, and supportive culture.
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions. In this paper, we develop a novel post-hoc visual explanation method called Score-CAM based on class activation mapping. Unlike previous class activation mapping based approaches, Score-CAM gets rid of the dependence on gradients by obtaining the weight of each activation map through its forward passing score on target class, the final result is obtained by a linear combination of weights and activation maps. We demonstrate that Score-CAM achieves better visual performance and fairness for interpreting the decision making process. Our approach outperforms previous methods on both recognition and localization tasks, it also passes the sanity check. We also indicate its application as debugging tools. Official code has been released.
Supervised meta-heuristic extreme learning machine for multiple sclerosis detection based on multiple feature descriptors in MR images

In this study, we propose a hybrid approach involving feature extraction, feature selection, and optimized learning for the diagnosis of multiple sclerosis (MS), which can detect the lesion caused by MS plaques in the brain using magnetic resonance imaging analysis. A major challenge associated with lesion diagnosis by neurologists is that it is a time-consuming process and demands high expertise; therefore, researchers have been stimulated to find an auto-diagnose method of the disease. Given the high resemblance of MS plaque-induced lesions and other lesions such as Alzheimer's or dementia, scant research has explored the diagnosis of MS-induced lesions, most of which suffering from the lack of an efficient and accurate method. Informed by the need for a precise hybrid model for the classification of MS plaques and other comparable lesions, a solution is proposed that utilizes an efficient model. In this method, after image preprocessing, the feature vector is created by applying fractal and Pseudo-Zernike Moments descriptors. Feature selection using the Difference Evolution) algorithm to select the minimum subset of features will reduce the number of Extreme Learning Machine (ELM) inputs for classification. To improve the classification effect, the ELM wavelet kernel parameters are also regulated by the Shuffled Frog-Leaping Algorithm. By applying the proposed model to a set of brain MR images obtained from healthy subjects and MS patients during different experimental iterations, an average accuracy of 97% was obtained. The results of the method were estimated under specific conditions, and finally the proposed model yielded desirable outputs compared to similar methods.
Intra-day solar irradiation forecast using RLS filters and satellite images

Satellite-based solar irradiation forecasting is useful for short-term intra-day time horizons, outperforming numerical weather predictions up to 3–4 h ahead. The main techniques for solar satellite forecast are based on sophisticated cloud motion estimates from geostationary satellite images. This work explores the use of satellite information in a simpler way, namely spatial averages that require almost no preprocessing. Adaptive auto-regressive models are used to assess the impact of this information on the forecasting performance. A complete analysis regarding model selection, the satellite averaging window size and the inclusion of satellite past measurements is provided. It is shown that: (i) satellite spatial averages are useful inputs and the averaging window size is an important parameter, (ii) satellite lags are of limited utility and spatial averages are more useful than weighted time averages, and (iii) there is no value in fine-tuning the orders of auto-regressive models for each time horizon, as the same performance can be obtained by using a fixed well-selected order. These ideas are tested for a region that has intermediate solar variability, and the models succeed to outperform a proposed optimal smart persistence, used here as an exigent performance benchmark.
Secure Data Transmission in Video Format Based on LSB and Huffman Coding

The growth of needing to transmit bit amount of data through the internet in secure format encourage the research for steganography technique, especially in video file.Stenographic technics in video format gives many advantages to transportation of important data because video files are a part of people's daily life and the attackers can't notice easily.The high embedding capacity of video file improves the popularity of video steganography among the various media types.Therefore, the simplest form but with many advantage of (Least significant bit) LSB, that is enforced with the high compression method of Huffman chunk coding method is proposed in this paper to embed data in video file in multi-step cryptography embedding schemes.The intension is to get more secure nature of the system and to get more embedding capacity system.The experiments are carried out with various sizes of video files and text file sizes are used to show the effectiveness of the proposed methods.The results manifest superior performance for proposed algorithm with the performance parameters like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Bit Error Rate (BER) are calculated to test the quality of stego video.
Trapped Ion Architecture for Multi‐Dimensional Quantum Simulations

Abstract A rich and powerful toolbox for individually trapped atomic ions is available for quantum information processing, including quantum metrology and quantum simulation, demonstrating control with highest fidelities. Building on this success, a novel architecture for analog quantum simulations aims at setting up fully controlled and reconfigurable quantum lattices by individually trapped ions in multi‐dimensional arrangements. In this article, an overview of recent developments and demonstrations of prototype operations is given. The features and limitations of the architecture are discussed and crucial steps toward mid and long‐term simulation applications are laid out.
Natural Language Generation Model for Mammography Reports Simulation

Extending the size of labeled corpora of medical reports is a major step towards a successful training of machine learning algorithms. Simulating new text reports is a key solution for reports augmentation, which extends the cohort size. However, text generation in the medical domain is challenging because it needs to preserve both content and style that are typical for real reports, without risking the patients' privacy. In this paper, we present a conditioned LSTM-RNN architecture for simulating realistic mammography reports. We evaluated the performance by analyzing the characteristics of the simulated reports and classifying them into benign and malignant classes. An average classification AUC was calculated over two distinct test sets. A qualitative analysis was also performed in which a masked radiologist classified 0.75 of the simulated reports as real reports, showing that both the style and content of the simulated reports were similar to real reports. Finally, we compared our RNN-LSTM generative model with Markov Random Fields. The RNN-LSTM provided significantly better and more stable performance than MRFs (p <; 0.01, Wilcoxon).
Potential Map for the Installation of Concentrated Solar Power Towers in Chile

This study aims to build a potential map for the installation of a central receiver concentrated solar power plant in Chile under the terms of the average net present cost of electricity generation during its lifetime. This is also called the levelized cost of electricity, which is a function of electricity production, capital costs, operational costs and financial parameters. The electricity production, capital and operational costs were defined as a function of the location through the Chilean territory. Solar resources and atmospheric conditions for each site were determined. A 130 MWe concentrated solar power plant was modeled to estimate annual electricity production for each site. The capital and operational costs were identified as a function of location. The electricity supplied by the power plant was tested, quantifying the potential of the solar resources, as well as technical and economic variables. The results reveal areas with great potential for the development of large-scale central receiver concentrated solar power plants, therefore accomplishing a low levelized cost of energy. The best zone is located among the Arica and Parinacota region and the northern part of the Coquimbo region, which shows an average cost of 89 USD/MWh, with a minimum of 76 USD/MWh near Copiapó.
Extending intelligent content delivery in technical communication by semantics: microdocuments and content services

We address and develop a new concept for the dynamic delivery of topic-based content created within the domain of technical communication. Corresponding content management environments introduced within the last decades, focused so far on semantically structured and mostly XML-based information models and, more recently, on semantic metadata using taxonomies leading together to concepts of so-called intelligent content. Latest developments attempt to extend these concepts with additional explicit semantic approaches modelled and implemented, for example, by using ontologies and related technologies. In this article, we propose how content users might benefit from these semantic concepts by the delivery of sets of logically connected topics, which can be described as microdocuments (“microDocs”). This generic approach of topic assemblies might also play a role in the provisioning of content by web-services being integrated into different types of content processing and content delivery applications.
Enhanced fuzzy finite state machine for human activity modelling and recognition

Abstract A challenging key aspect of modelling and recognising human activity is to design a model that can deal with the uncertainty in human behaviour. Several machine learning and deep learning techniques are employed to model the Activity of Daily Living (ADL) representing the human activity. This paper proposes an enhanced Fuzzy Finite State Machine (FFSM) model by combining the classical FFSM with Long Short-Term Memory (LSTM) neural network and Convolutional Neural Network (CNN). The learning capability in the LSTM and CNN allows the system to learn the relationship in the temporal human activity data and to identify the parameters of the rule-based system as building blocks of the FFSM through time steps in the learning mode. The learned parameters are then used for generating the fuzzy rules that govern the transitions between the system’s states representing activities. The proposed enhanced FFSMs were tested and evaluated using two different datasets; a real dataset collected by our research group and a public dataset collected from CASAS smart home project. Using LSTM-FFSM, the experimental results achieved $$95.7\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>95.7</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and $$97.6\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>97.6</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> for the first dataset and the second dataset, respectively. Once CNN-FFSM was applied to both datasets, the obtained results were $$94.2\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>94.2</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and $$99.3\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>99.3</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , respectively.
Carbon Trading with Blockchain

Blockchain has the potential to accelerate the deployment of emissions trading systems (ETS) worldwide and improve upon the efficiency of existing systems. In this paper, we present a model for a permissioned blockchain implementation based on the successful European Union (EU) ETS and discuss its potential advantages over existing technology. We propose an ETS model that is both backwards compatible and future-proof, characterised by interconnectedness, transparency, tamper-resistance and high liquidity. Further, we identify key challenges to implementation of a blockchain ETS, as well as areas of future work required to enable a fully-decentralised blockchain ETS.
MPTEE

Intel Software Guard extensions (SGX), a hardware-based Trusted Execution Environment (TEE), has become a promising solution to stopping critical threats such as insider attacks and remote exploits. SGX has recently drawn extensive research in two directions---using it to protect the confidentiality and integrity of sensitive data, and protecting itself from attacks. Both the applications and defense mechanisms of SGX have a fundamental need---flexible memory protection that updates memory-page permissions dynamically and enforces the least-privilege principle. Unfortunately, SGX does not provide such a memory-protection mechanism due to the lack of hardware support and the untrustedness of operating systems.
Emotional Wellbeing Assessment for Elderly Using Multi-Language Robot Interface

There is always necessity prevailing for the exposed platforms under the vigilance of experts, incorporated with the capability of developing and gathering enquiries followed up by the analysis and remedial measures based on the long duration laborious medical follow-up. So the work carried out in the paper initiates to cultivate a robotic interface with the multi-language capability to assist in monitoring and assessing the emotional health of the elderly people by extending conversations. For this purpose the proposed method utilizes the voice interface to put forward, enquires from the experts and the robotic interface developed converses automatically to the clients with the supervisions of the experts to course the replies. The proposed system would be compatible in any room as it is carried out on an embedded platform designed for the edge computing. The advantage of the system was understood by subjecting it to the answer files in the audio form and validating it in the terms of the recognition scores, and the percentage of estimation accuracy.
A SYMMETRIC FULLY CONVOLUTIONAL RESIDUAL NETWORK WITH DCRF FOR ACCURATE TOOTH SEGMENTATION

Accurate tooth segmentation from CBCT images is a crucial step for specialist to perform quantitative analysis, clinical diagnosis and operation. In this paper, we present a symmetric full convolutional network with residual block and Dense Conditional Random Field (DCRF), which can achieve accurate segmentation automatically for tooth images. The proposed method can not only strengthen feature propagation, but also boost feature reuse, which can be credited to the contracting path and the expanding path that extract and recover pixel cues sufficiently. To this end, we apply special deep bottleneck architectures (DBAs) and summation-based skip connection into our network to ensure accurate segmentation for much deeper neural network. Compared with previous methods which are based on conditional random field for original image intensity, our approach applies DCRF to the posterior probability generated by the proposed network. To avoid the interferences of noises around the tooth, we combine the pixel-level prediction capability of DCRF, which further enhance the segmentation performance. In the experiments, we verify the capabilities of our methods based on four evaluation indicators, which demonstrates the superiority of our method.
Cyber Security Awareness Among University Students: A Case Study

This Case Study reports the preliminary results of a quantitative survey aimed to identify students' awareness and enthusiasm to learn cybersecurity in Nigerian Universities. The objective of the survey was to see how students in this developing country are aware of cyber-attacks and how they can mitigate the attacks and to find out if cybersecurity awareness program is part of the University program. The preliminary results indicated that the students claimed to have basic cybersecurity knowledge, but are not aware of how to protect their data. It also appears that most Universities do not have an active cybersecurity awareness program to improve students' knowledge on how to protect themselves from any threats. The surveyed students also show interest in learning more about cybersecurity.
Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles

For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mostly focused on communication and network layers of V2X (Vehicle-to-Everything) data sharing. However, there is a gap when it comes to the data layer. Limited attention has been paid to the ontology development in the automotive domain. More specifically, the way to integrate sensor data and geospatial data efficiently is missing. Therefore, we proposed to develop a new Connected Traffic Data Ontology (CTDO) on the foundations of Sensor, Observation, Sample, and Actuator (SOSA) ontology, to provide a more suitable ontology for large volumes of time-sensitive data coming from multi-sensory platforms, like connected vehicles, as the first step in closing the existing research gap. Additionally, as this research aims to further extend the CTDO in the future, a possible way to map to the CTDO with ontologies that represent road infrastructure has been presented. Finally, new CTDO ontology was benchmarked against SOSA, and better memory performance and query execution speeds have been confirmed.
Gripmarks: Using Hand Grips to Transform In-Hand Objects into Mixed Reality Input

We introduce Gripmarks, a system that enables users to opportunistically use objects they are already holding as input surfaces for mixed reality head-mounted displays (HMD). Leveraging handheld objects reduces the need for users to free up their hands or acquire a controller to interact with their HMD. Gripmarks associate a particular hand grip with the shape primitive of the physical object without the need of object recognition or instrumenting the object. From the grip pose and shape primitive we can infer the surface of the object. With an activation gesture, we can enable the object for use as input to the HMD. With five gripmarks we demonstrate a recognition rate of 94.2%; we show that our grip detection benefits from the physical constraints of holding an object. We explore two categories of input objects 1) tangible surfaces and 2) tangible tools and present two representative applications. We discuss the design and technical challenges for expanding the concept.
CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus

Spoken language translation has recently witnessed a resurgence in popularity, thanks to the development of end-to-end models and the creation of new corpora, such as Augmented LibriSpeech and MuST-C. Existing datasets involve language pairs with English as a source language, involve very specific domains or are low resource. We introduce CoVoST, a multilingual speech-to-text translation corpus from 11 languages into English, diversified with over 11,000 speakers and over 60 accents. We describe the dataset creation methodology and provide empirical evidence of the quality of the data. We also provide initial benchmarks, including, to our knowledge, the first end-to-end many-to-one multilingual models for spoken language translation. CoVoST is released under CC0 license and free to use. We also provide additional evaluation data derived from Tatoeba under CC licenses.
PhishSKaPe: A Content based Approach to Escape Phishing Attacks

A type of cyberattack, viz. phishing attack exposes a user’s credentials to malicious entities leading to breach of privacy. The proposed approach involves employing a search engine to match the domain name of the site under scrutiny with the sites that come up as result of our search query. Here, our search query is comprised of features viz. domain name and keywords consisting of title, meta description data and body text. We initially see how conventional TF-IDF (Term Frequency-Inverse Document Frequency) performs in classifying websites as phishing or legitimate. We then apply a weighted heuristic proposed in detail in this report by assigning different weights to different tag data and manipulate the TF-IDF score accordingly to improve the performance of our phishing detector. Experiments results illustrate that proposed search based method with weighted TF-IDF performs better than conventional TF-IDF.
Revisiting Transactional Statistics of High-scalability Blockchains

Scalability has been a bottleneck for major blockchains such as Bitcoin and Ethereum. Despite the significantly improved scalability claimed by several high-profile blockchain projects, there has been little effort to understand how their transactional throughput is being used. In this paper, we examine recent network traffic of three major high-scalability blockchains---EOSIO, Tezos and XRP Ledger (XRPL)---over a period of seven months. Our analysis reveals that only a small fraction of the transactions are used for value transfer purposes. In particular, 96% of the transactions on EOSIO were triggered by the airdrop of a currently valueless token; on Tezos, 76% of throughput was used for maintaining consensus; and over 94% of transactions on XRPL carried no economic value. We also identify a persisting airdrop on EOSIO as a DoS attack and detect a two-month-long spam attack on XRPL. The paper explores the different designs of the three blockchains and sheds light on how they could shape user behavior.