Reviewerly/exHarmony huggingface dataset:
A Conceptual Information Security Culture Framework for Higher Learning Institutions

Education institutions within and outside Ghana continue to experience mass information leakages at an alarming rate even with the huge investment made in information technology infrastructure to secure their information assets. The lack of organisational commitment to enhance the non-technical aspects of information security − thus, information security culture (ISC) − largely accounts for the consistent rise of security breaches in institutions like the educational institutions. Securing information assets goes beyond technical controls and encompasses people, technology, policy, and operations. The aim of this paper is to identify a comprehensive list of the factors of ISC and construct a conceptual ISC framework (InfoSeCulF) that can be used to provide guidance for the cultivation of a strong ISC in higher learning institutions to secure information assets. A scoping literature review was conducted to determine what constitutes a comprehensive list of factors for cultivating ISC in higher learning institutions. The study proposes a comprehensive list of factors and provides a conceptual framework (InfoSeCulF) which serves as guide for cultivating a strong ISC in institutions.
Stable Parallel Algorithms for Interdisciplinary Computer-Based Online Education with Real Problem Scenarios for STEM Education

In this paper, we analyse and study the interdisciplinary style of stable parallel algorithms for online computer education and real problem scenarios for STEM education. Under the guidance of the STEM concept, the project theme is designed based on the principles of project design, and the interdisciplinary knowledge points related to the theme are determined. Based on the project theme, the teaching design model of the STEM-based robotics project was constructed. The analysis of the results showed that the STEM-based middle-school robotics project not only increased students’ interest in robotics but also promoted their creativity, problem-solving, teamwork, and communication skills, as well as their learning of other subjects. In this paper, an adaptive overflow-aware loss amplification strategy is proposed, which effectively alleviates the training nonconvergence problem caused by gradient overflow in mixed accuracy training. Also, this paper demonstrates that data-parallel training under multiple machines and multiple cards should use local batch normalization, which is particularly effective in accelerating neural networks containing more batch normalization layers.
Controllable and Editable Neural Story Plot Generation via Control-and-Edit Transformer

Language-modeling-based methods for story plot generation aim to generate a plot with a language model (LM). LM methods have limitations of user-assist plot generation of goal control, refinement for editing, causing the generated plots not clear sense for specific goal, lack coherence, and edit flexible. We present a control-and-edit transformer technique which uses controlled imitation learning of editing distance from dynamic programming to support deleting policy, inserting policy, a weighting-reward with prepossess of corpus statistic, and measures continues reward for the controlled goal. Automated evaluation and Haman judgement show our method is promising in comparison with the baselines.
Standard generators of finite fields and their cyclic subgroups

We define standard constructions of finite fields, and standard generators of (multiplicative) cyclic subgroups in these fields. The motivation is to provide a substitute for Conway polynomials which can be used by various software packages and in collections of mathematical data which involve finite fields.
A Model-Based Approach to Facilitate Design of Homogeneous Redundant E/E Architectures

Designing the new generation of electronic and electrical architectures, guaranteeing reliable communication is a prerequisite. The fulfillment of this requirement is a challenging undertaking that requires advanced expertise and is time-consuming. This paper presents a novel model-based approach for automating the generation of optimized network architectures in the design phase, supporting homogeneous redundant routings. Our results indicate a linear growth of architecture synthesis time if the number of applications is growing while the number of nodes remains constant. Conversely, increasing the number of nodes results in exponential growth of the architecture synthesis time. We believe that the proposed approach contributes to facilitating the design of safe E/E architectures.
Multimedia in Virtual Reality and Augmented Reality

Multimedia is one of the key drivers improving virtual reality and augmented reality (VR/AR), which are promising to reform human-computer interaction in the future with lower-cost and all-in-one headsets containing powerful hardware. Advances in multimedia research on video compression and human-computer interfaces have further enhanced the immersion and efficiency of experiences on the platform. However, many VR/AR experiences are still very difficult to build using traditional engineering methods and many available behavioral and biometric data have not been well explored. Further research in the multimedia community is needed to enhance the usefulness of these systems, with potential in affective learning, resource generation, and developer tools.
A Technique for Securing Multiple Digital Images Based on 2D Linear Congruential Generator, Silver Ratio, and Galois Field

Security of multiple digital images over an unsecured channel is a current area of research in the field of image cryptography.Several techniques have been proposed for this purpose.However, the main drawbacks of the existing techniques are as follows: the existing techniques can be applied merely to the grayscale or the truecolor images; the size of the cipher (output) image is bigger than that of the plain (input) images; the authors have used a large number of parameters in the encryption/decryption process, besides the secret key; and the cipher/decipher rounds use the same key for encryption/decryption process.The proposed technique, which is based on a two-dimensional (2D) Linear Congruential Generator (LCG), silver ratio, and Galois field, is designed not merely to secure multiple digital images, namely, three truecolor, nine grayscale, and seventy-two binary images of different sizes, simultaneously, but also to address the above drawbacks.The designed technique achieves both confusion and diffusion properties essential for a secure cryptosystem.Also, the technique is empirically assessed via several statistical and security evaluation metrics, such as key sensitivity, histogram, chi-square, entropy, number of pixel change rate, structural similarity index measure, processing time, etc.The results of these evaluation metrics prove the efficiency and robustness of the designed technique.
A Deep Hybrid Architecture for Human Activity Recognition

In this paper we present early results of a novel approach for human activity recognition, focusing on activities of daily living. Our approach is multimodal, i.e., it combines RGB, depth and skeletal data measurements. Moreover, it is based on a novel hybrid deep neural network architecture that combines a convolutional neural network, a long short term memory network and it is evaluated using a publicly available dataset.
Earthquake detection and location for Earthquake Early Warning Using Deep Learning

Abstract Earthquake Early Warning System (EEWS) is a warning system that provides information about the estimated S wave arrival time, which can cause significant and destructive seismic energy using the information carried by the P wave. Technological advances in analyzing data supported by big data, the interconnection between networks, and high-performance computing systems in the era of the 4.0 industrial revolution have posed challenges to process and analyze earthquake early warning using modern seismological techniques. Early identification of earthquake events is the key to time efficiency to accelerate the dissemination of information. Here, we implement deep learning for early detection and classification of the earthquake P wave and noise signals using raw historical data from 3 component BMKG single station (2014 -2020) in the subduction zone of West Sumatra. The feature selection of the waveform is only selected for earthquakes distance in the cluster close to the station centroid. Statistically, the results of training and testing show good and convergent performance. This result is a preliminary study of deep learning, which is targeted at the classification of earthquakes p wave and noise signals and its association to estimate early earthquake location using 3 component record channels.
The Vulnerability of the Neural Networks Against Adversarial Examples in Deep Learning Algorithms

With the further development in the fields of computer vision, network security, natural language processing and so on so forth, deep learning technology gradually exposed certain security risks. The existing deep learning algorithms cannot effectively describe the essential characteristics of data, making the algorithm unable to give the correct result in the face of malicious input. Based on current security threats faced by deep learning, this paper introduces the problem of adversarial examples in deep learning, sorts out the existing attack and defense methods of black box and white box, and classifies them. It briefly describes the application of some adversarial examples in different scenarios in recent years, compares several defense technologies of adversarial examples, and finally summarizes the problems in this research field and prospects its future development. This paper introduces the common white box attack methods in detail, and further compares the similarities and differences between the attack of black and white boxes. Correspondingly, the author also introduces the defense methods, and analyzes the performance of these methods against the black and white box attack.
Research on the Sharing of Equipment Data Based on Blockchain

In today's big data era, the effective sharing of data is the basic link to make full use of data. However, there are a lot of security risks in this link. In particular, equipment data involves many secrets and cannot be completely open. Based on the almost immutable characteristics of blockchain, encrypted transmission, full chain consensus, and full process traceability, a set of security, privacy, and mutual trust is proposed. Equipped with data sharing solutions. Blockchain technology can effectively improve the credibility and security of data sharing links. In the overall framework, the blockchain, as the core of the data sharing system, guarantees reliable communication between different data centers and terminals, and realizes the entire process of encrypted transmission, and the original data “never exits the data holder's hard drive”. It not only meets the demand side's use of data, but also avoids the risk of data leakage by the data holder, thereby eliminating the worries and worries of the data holder's information security.
Lane Detection Algorithm Using LRF for Autonomous Navigation of Mobile Robot

This paper proposes a lane detection algorithm using a laser range finder (LRF) for the autonomous navigation of a mobile robot. There are many technologies for ensuring the safety of vehicles, such as airbags, ABS, and EPS. Further, lane detection is a fundamental requirement for an automobile system that utilizes the external environment information of automobiles. Representative methods of lane recognition are vision-based and LRF-based systems. In the case of a vision-based system, the recognition of the environment of a three-dimensional space becomes excellent only in good conditions for capturing images. However, there are so many unexpected barriers, such as bad illumination, occlusions, vibrations, and thick fog, that the vision-based method cannot be used for satisfying the abovementioned fundamental requirement. In this paper, a three-dimensional lane detection algorithm using LRF that is very robust against illumination is proposed. For the three-dimensional lane detection, the laser reflection difference between the asphalt and the lane according to color and distance has been utilized with the extraction of feature points. Further, a stable tracking algorithm is introduced empirically in this research. The performance of the proposed algorithm of lane detection and tracking has been experimentally verified.
Improvement in Multi-Person 2D Pose Estimation: Applying Polar Representation in OpenPose

Recent pose machines provide a relative accurate estimation in 2D real-time multi-person situations. In this work, we demonstrate an advanced open-pose design with a sequential stages of prediction and use of polar coordinate system. The main contribution of this paper is to denote a pose machine frame work based on the available open-pose model, which performs improvement in both efficiency and accuracy in image-dependent spatial models learning. We achieve this by considering additional information of image features with both a sequential structure of convolutional networks and the support of part affinity fields, as well as the advantages of using polar coordinate system, which efficiently predicting accurate estimates in multi-person cases. Our approach characterizes how the concept of part affinity fields can be used in key points connection. We perform competing methods on standard data sets including COCO data set, compare our result with several bottom-up approach and illustrate the result in straightforward ways.
Aion Attacks: Manipulating Software Timers in Trusted Execution Environment

Side-channel attacks are a threat to secure software running in a Trusted Execution Environment (TEE). To protect Intel SGX applications from these attacks, researchers have proposed mechanisms to detect cache-probing and repeated interrupts that these attacks rely on. These defenses often rely on high-resolution timers. However, since there is no trusted high-resolution timer hardware module, developers have resorted to software timers, which unfortunately underestimate the scope of possible attacks. In this paper, we propose Aion attacks that manipulate the speed of a reference software timer to subvert defensive mechanisms against SGX side-channel attacks. Specifically, we introduce a CPU thermal attack that leverages the thermal management mechanism to change the execution speed of the timer thread, and a cache eviction attack that evicts the target timer counters and forces the system to load them from memory instead of cache. We evaluated the above Aion attacks and introduced an analytical model and show that software timers cannot be improved to fit the defenders under our attacks.
Towards Linking CNN Decisions with Cancer Signs for Breast Lesion Classification from Ultrasound Images

Convolutional neural networks have shown outstanding object recognition performance, especially for visual recognition tasks such as tumor classification in 2D ultrasound (US) images. In Computer-Aided Diagnosis (CAD) systems, interpreting CNN's decision is crucial for accepting the system in the clinical use. This paper is concerned with 'visual explanations' for decisions from CNN models trained on ultrasound images. In particular, we investigate the link between the CNN decision and the calcification cancer sign in breast lesion classification task. To this end, we study the output visualization of two different breast lesion recognition CNN models in two folds: Firstly, we explore two existing visualization approaches, Grad-CAM and CRM, to gain insight into the function of feature layers. Secondly, we introduce an adaptive Grad-CAM, called EGrad-CAM, which uses information entropy to freeze feature maps with no or minimal information. Extensive analysis and experiments using 1624 US images and two breast classification models show that calcification feature contributes to the CNN classification decision for both malignant and benign lesions. Furthermore, we show many feature maps in the final convolution layer are not contributing to the CNN decision, and our EGrad-CAM produces similar visualization output to Grad-CAM using 24%–87% of the feature maps. Our study demonstrates that the CNN decision visualization is a promising direction for bridging the gap between CNN classification decision of US images of breast lesions and cancer signs.
Customer Satisfaction Assessment Coffee Roaster Restaurant using SERVQUAL: Utilization of Customer Relationship Management (CRM) Application

Several cities have been recognized to be tourism destination due to the popularity in the culinary, which provides diverse choice in every corner such as food stalls, cafes, food festival restaurant and much more. The market potential is very huge, which the competition run in the tight way in term of provides variability, exclusivity, durability and other offered to increase customer satisfaction. The strategy emphasizes the importance of customer service to generate responsible customer experience leading to the increase customer retention. Thus, this study wants to explore the customer satisfaction to verify the influence of customer expectation, loyalty program, shared stories and service marketing through the questionnaire survey. The assessment will generate the important factors that can influence the degree of satisfaction in the culinary industry through the utilization of customer relationship management (CRM) as the front desk application.
Human-Computer Interface Design of Intelligent Spinning Factory Monitoring System Based on Eye Tracking Technology

This paper studies and analyses theoretically the layout, text and color of the human-computer interface design of intelligent spinning factory Monitoring system. At the same time, eye movement physiological experiments are conducted to evaluate the size, shape and spacing of the card interactive elements in the interface. The research results will provide a new human-machine interface design scheme for the large intelligent spinning factory, so that the users can improve their work efficiency and adapt to the development of spinning industry in the new era.
Deep Learning Based Facial Feature Detection for Ethnicity Recognition

In the study of ethnical group recognition, facial feature discovery is one of the essential tasks. Specially, with the recent development of deep learning methods, significant evolution has been made in computer-based facial recognition. In this paper, we proposed a deep learning model for ethnicity recognition, considering the facial features. At first, we gathered the dataset for ethnical group which includes Southeast Asian, East Asian, Black, White, Latin, Indian and Middle Eastern. Also, we have collected real-time face images to recognize the ethnicity group. Every ethnicity group has its own characteristics and facial features. Therefore, we considered the particular ethnical groups and intended to find the similar categorization. Since human facial information will vary based on the geographical location, we have collected data from various regions to achieve better results for our experiment. In this analysis, deep leaning-based ethnicity identification has been exploited for various details in human faces. This study is proposed to understand and analyze the relation between the ethnicity and ethnical groups, by considering the images with different intensity and registered range. We trained 85,000 images and managed to give a precise result. The research work on ethnicity recognition with facial features helps in the study of facial features evolution in anthropology (the study of human societies and cultures and their development).
Behavioural separation with parallel usages

Mungo is an object-oriented language that uses typestates with a behavioural type system to ensure the absence of null-dereferencing. Typestates are usages that specify the admissible sequences of method calls on objects. Previous type systems for Mungo have all had a linearity constraint on objects. We present an extension of these systems, where usage specifications can now include a parallel construct that lets us describe separate local behaviour. A parallel usage describes a separation of the heap, and this allows us to reason about aliasing and to express arbitrary interleaving of local protocols. This also solves the state-space explosion problem for usages. Our extension retains the safety properties of previous type systems for Mungo.
HemodynamicVR - Adapting the User’s Field Of View during Virtual Reality Locomotion Tasks to Reduce Cybersickness using Wearable Functional Near-Infrared Spectroscopy

We present HemodynamicVR, a virtual reality headset combined with functional near-infrared spectroscopy (fNIRS). We believe that sensing brain activity will enable novel interactions in virtual reality. In this paper, we assess a user's cybersickness based on the change of their total hemoglobin concentration measured via an fNIRS device, and we try to mitigate that by changing the field of view (FOV) in real-time. In this experiment, participants experienced VR locomotion with an added variable FOV controlled by velocity changes and by fNIRS. The results suggest that fNIRS can detect cybersickness as registered by the qualitative SSQ test.
Using Population Migration and Mutation to Improve Loser-Out Tournament-Based Fireworks Algorithm

The fireworks algorithm (FWA) is a newly proposed swarm intelligence algorithm inspired by the phenomena of fireworks explosion and has solved many real-world optimization problems successfully. A loser-out tournament-based fireworks algorithm (LoTFWA) is a new baseline in the development of FWA due to its outstanding independent framework and competition mechanism for multimodal optimization. Under this framework, each firework calculates its expected fitness improvement compared with the best fitness to determine whether to be reinitialized. Although LoTFWA achieves the best performance among the variants of FWA, it lacks of comprehensive consideration of the fireworks' cooperation and hence weakens the algorithm's power. This paper improves the cooperation of fireworks in LoTFWA based on the idea of population migration and mutation in biogeography-based optimization (BBO). The proposed mechanism not only promotes fireworks' exploration ability but also enhances their exploitation ability greatly. Experimental results show that the proposed algorithm attains superior performance than the state-of-the-art fireworks algorithm in both unimodal and multimodal functions.
Investigation of Coronavirus impact on Blockchain and cryptocurrencies markets

Coronavirus had a tremendous global economic impact due to mistrust and reduced consumption. Especially in China, the country from which the infection started and which presents more than 90,000 infections on March 4th, the global economy has been experiencing the first sharp economic contraction since 1970. On February 24 th 2020, the Dow Jones Industrial Average lost 1800 points. Blockchain development communities designed and launch a new cryptocurrency called CoronaCoin. This research aims to study and identify whether users' mood and sentiment may affect CoronaCoin price. For this reason, two official CoronaCoin communities of two popular social media platforms, i.e. Telegram and Reddit, were analysed. Through the novel usage of the Hawkes models, we found that users of these social media platforms may affect Coronacoin's behaviour based on their sentiment, with significant price changes.