Reviewerly/exHarmony huggingface dataset:
$\text{Edge}^{n}$ AI: Distributed Inference with Local Edge Devices and Minimal Latency

We propose <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{Edge}^{n}$</tex> AI, a framework to decompose a complex deep neural networks (DNN) over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> available local edge devices with minimal communication overhead and overall latency. Our framework creates small DNNs (SNNs) from an original DNN by partitioning its classes across the edge devices, while taking into account their available resources. Class-aware pruning is applied to aggressively reduce the size of the SNN on each edge device. The SNNs perform inference in parallel, and are configured to generate a ‘Don't Know’ response when an unassigned class is identified. Our experiments show up to 17X inference speedup compared to a recent work, on devices of at most 150 MB memory when distributing a variant of VGG-16 over 20 parallel edge devices.
Perceived Effectiveness of Developing a Mobile System of Formative Test with Handwriting Revision to Devise an Instruction Design Based on Cognitive Apprenticeship Theory

Education helps increase socioeconomic mobility and is an important way of leaving poverty according to the United Nations, especially since COVID-19 hit the world hard in early 2020. A Mobile System of Formative Tests with Handwriting Revision is proposed in the paper, called the MSFT system. The MSFT is developed from the cognitive apprenticeship theory (CAT) in instructional design. The instruction model can be utilized for higher education mathematics teaching/studying for quiz-oriented instruction inside traditional classrooms as well as for distance-learning modes. The MSFT platform provides college undergraduates and graduates an app for a handheld device, which is used to upload their answer sheets with captured photos to the cloud database server. Moreover, instructors can use the platform to revise or assess answer sheets with instructors’ handwriting through web interfaces or apps. Important features of the integrated platform for teachers are (1) grading answer sheets by handwriting, (2) correcting mistakes in the answer sheets by handwriting, (3) writing down instructors’ comments on students’ answer sheets directly, and (4) choosing examples to demonstrate during class presentation, in a single window through web applications. To evaluate MSFT performance and service level for students, a questionnaire survey was conducted for 51 students and separated into an experimental group and a control group. Results from the experiment show that learning attitudes and learning satisfaction were significantly increased with the MSFT system in the experimental group compared to the control group.
A Multi-Group Structural Equation Modeling For Assessing Behavioral Intention of Using Mobile Cloud Computing-The Case of Jordanian Universities During The Covid19 Pandemic

The adoption of new technologies in Jordanian Universities related to cloud services, shows differences in practices between faculty and staff members. Resistance to adoption may accrue by faculty and staff members who are accustomed and favoring old practices. A questionnaire was developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model to identify factors that affect behavioral intentions that lead to the use of mobile cloud computing during the covid-19 pandemic, taking into consideration Work-type as the mediating factor. Five Jordanian Universities participated in this study, with a total response of 153 faculty and staff members. The conceptual proposed model was tested to ensure the fitness of the structural model for providing correct estimations. The collected sample was subjected to confirmatory factor analysis to ensure construct, convergent and discriminant validity. The results came positive in terms of composite reliability as they were above 0.70, for Average Variance Extracted (AVE) it came more than 0.05and Cronbach alpha exceeded 0.70. The results revealed the fitness of the proposed model to measure differences in behavioral intentions towards adopting mobile cloud services between faculty members and employees. Moreover, the results showed that work type had some interesting moderating impact on the tested relationships. Moreover, the results showed that there is a high Behavioral Intention (BI) between faculty and staff to use mobile cloud services and solutions within their workplace. In addition, the results showed some inequalities of the behavioral intention toward the adoption of mobile cloud services in Jordanian Universities between the two groups. These results call the university administration to clarify these factors for user groups to obtain a better judgment on investment and future practices for using new technologies.
Accommodating English for Specific Purposes to Computer Assisted Language Learning

Based on a descriptive approach regarding the content of the LanGuide project, but in perfect accordance with the literature review on CALL, the present article researches into the technicalities of developing a mobile application for foreign language learning and assessing, in four different specialised fields (academic, mobility, administrative and IT) offered for six different languages (English, Romanian, Slovene, Italian, Croatian and Spanish) at 3 different levels of knowledge (beginner, intermediate and advanced) and for 3 different learning personae (student, teacher, and administrative staff member). Placing the users in the appropriate contexts by the task environment created for each exercise and including cultural elements from each of the foreign languages targeted, LanGuide manages to distinguish itself among other language learning mobile applications, by not only harmonizing ESP to CALL but also by combining the pedagogy of the communicative approach to teaching a foreign language to that of the digital era.
Multi-Pose Facial Expression Recognition Using Hybrid Deep Learning Model with Improved Variant of Gravitational Search Algorithm

The recognition of human facial expressions with the variation of poses is one of the challenging tasks in real-time applications such as human physiological interaction detection, intention analysis, marketing interest evaluation, mental disease diagnosis, etc. This research work addresses the problem of expression recognition from different facial poses at the yaw angle. The major contribution of the paper is the proposal of an autonomous pose variant facial expression recognition framework using the amalgamation of a hybrid deep learning model with an improved quantum inspired gravitational search algorithm. The hybrid deep learning model is the integration of the convolutional neural network and recurrent neural network. The applicability of the hybrid deep learning model can be considered as significant if the feature set is efficiently optimized to have the discriminative features respective to each expression class. Here, the Improved Quantum Inspired Gravitational Search Algorithm (IQI-GSA) is utilized for the selection and optimization of features. The IQI-GSA method is significant for optimizing the features compared to quantum-behaved binary gravitation search algorithm for handing the local optima and stochastic characteristics. Comparing with state-of-art techniques, the proposed framework exhibits the outperformed recognition rate for experimentation on Karolinska Directed Emotional Faces (KDEF) and Japanese Female Facial Expression (JAFFE) datasets.
A Borehole Muon Telescope for Underground Muography

Radiographic imaging with muons by absorption, also called Muon Radiography or Muography, is a methodology based on the characteristic of the matter to be crossed by high energy muons.This physical property allows muons to pass through the material with a measurable degree of absorption depending on the density of the material.Muon Radiography applies to several different situations and is particularly suitable for investigating subsoil of civil or archaeological interest.This kind of applications needs the muon detector to be installed below the target region.A novel borehole cylindrical detector has been built and tested for use in harsh conditions and for limited space installations.It is based on the past expertise with scintillator detectors and is composed of two types of scintillating elements, bar-shaped and arc-shaped.Due to its size, it can be easily installed in drilled holes of 25 cm in diameter or more, typically economical to make.Here, we describe the idea, commissioning, and some preliminary results.
Single-Sample Face Recognition Based on Shared Generative Adversarial Network

Single-sample face recognition is a very challenging problem, where each person has only one labeled training sample. It is difficult to describe unknown facial variations. In this paper, we propose a shared generative adversarial network (SharedGAN) to expand the gallery dataset. Benefiting from the shared decoding network, SharedGAN requires only a small number of training samples. After obtaining the generated samples, we join them into a large public dataset. Then, a deep convolutional neural network is trained on the new dataset. We use the well-trained model for feature extraction. With the deep convolutional features, a simple softmax classifier is trained. Our method has been evaluated on AR, CMU-PIE, and FERET datasets. Experimental results demonstrate the effectiveness of SharedGAN and show its robustness for single sample face recognition.
A Two-Stage Decision-Making Method Based on WebGIS for Bulk Material Transportation of Hydropower Construction

Bulk materials are necessary for hydropower construction. The bulk materials transportation (BMT) scheme is a guiding document for material supply, and its selection has a significant influence on hydropower construction. Since the BMT problem includes transportation planning and scheme selection issues simultaneously, only a small number of studies have focused on it. This paper presents a theoretical two-stage decision-making method (TDM), which innovatively combines the path optimization method and the multi-criteria decision-making (MCDM) method to solve the BMT problem. In the first stage, a multi-source path optimization model is established to optimize the transportation network and obtain a set of alternatives from each supply point to the construction site. In the second stage, considering the factors of economy, risk and construction progress, the MCDM method is adopted to select the optimal scheme from the alternatives. In addition, web crawler technology is used to obtain the transportation network data from the public WebGIS automatically. Case results show that the TDM can effectively solve this problem, and its result keeps consistent with engineering practice; with the help of the web crawler, it can reduce the design task time from months to days. Therefore, the TDM based on WebGIS can benefit hydropower construction design efficiency.
Cognitively Complex Leaders: How Principals Influence Organizational Learning Through Climate for Innovation

Using an information-processing approach, the current innovative study examines a model for the prediction of organizational learning mechanisms (OLMs), linking school principals’ cognitive complexity and OLMs through the mediating effect of an innovative climate. The model was examined for three levels of principals’ cognitive complexity (high, intermediate, and low). Study participants were 131 principals and 1,279 teachers from 131 elementary schools in Israel. Results indicated that principals’ cognitive complexity promotes organizational learning mechanisms through a climate for innovation. The model with a high level of cognitive complexity showed a better fit to the data than those with intermediate (average) and low levels of cognitive complexity.
Expandable Spherical Projection and Feature Concatenation Methods for Real-Time Road Object Detection Using Fisheye Image

Fisheye lens cameras are widely used in such applications where a large field of view (FOV) is necessary. A large FOV can provide an enhanced understanding of the surrounding environment and can be an effective solution for detecting the objects in automotive applications. However, this comes with the cost of strong radial distortions and irregular size of objects depending on the location in an image. Therefore, we propose a new fisheye image warping method called Expandable Spherical Projection to expand the center and boundary regions in which smaller objects are mostly located. The proposed method produces undistorted objects especially in the image boundary and a less unwanted background in the bounding boxes. Additionally, we propose three multi-scale feature concatenation methods and provide the analysis of the influence from the three concatenation methods in a real-time object detector. Multiple fisheye image datasets are employed to demonstrate the effectiveness of the proposed projection and feature concatenation methods. From the experimental results, we find that the proposed Expandable Spherical projection and the LCat feature concatenation yield the best AP performance, which is up to 4.7% improvement compared to the original fisheye image datasets and the baseline model.
SynthNet: A skip connected depthwise separable neural network for Novel View Synthesis of solid objects

The process of synthesizing an image with objects in new pose angles other than input pose is known as Novel View Synthesis. Humans can visualize the objects in new poses by imagination. This paper proposes a novel deep learning based solution named SynthNet for the same task. This paper identifies the research gap that none of the existing deep learning models have employed depthwise separable convolution in their design for the application of Novel View Synthesis. SynthNet utilizes depthwise separable convolution in its design with a goal to reduce trainable parameter count and make the model computationally efficient. Identity gated skip connection, attention gated skip connection and flow attention gated skip connection are designed and employed to mitigate the vanishing gradient problem. A total of eight variations of SynthNet are designed, built, trained and tested. SynthNet is trained for car and chair class of objects using ShapeNet dataset. The trained model is successful in generating novel view of the object capturing the desired target view angle for a given input image with an unspecified view angle. Performance metric used for evaluating SynthNet are MAE and SSIM. The performance is on par or better than the baseline papers when compared with different variants of SynthNet. Notable achievement of the proposed design is reduction in trainable parameter count which indicates reduction in computational steps. SynthNet achieved 40% reduction in terms of trainable parameter count when compared with existing models due to application of depthwise separable convolution. Therefore, in simple words, SynthNet has achieved better performance than its peers simultaneously achieving computational efficiency.
Zero Trust Container Architecture (ZTCA)

Containerisation is quickly becoming an accepted industry standard for development environments and Gartner, in a recent market forecast, estimated that by 2022 more than 75% of organisations will be using containers in production deployments. With this explosion in growth comes an added focus on security and best practices for using containers. The use of containers, in particular Docker containers, has altered some of the more traditional deployment paradigms by giving control of deployments to the development teams. This has massively benefited the DevOps release cycle, but at the expense of many mature security and review processes that are integrated into traditional deployments. Like all systems, containers need frameworks to guide best practices for deployments and to ensure mistakes are not made that increase the risk level or attack surface of an application or service using containers, or the containers themselves. Indeed, according to a recent presentation during DevSecCon24 by Justin Cormack, Security Lead at Docker Inc., Cormack believes most security issues related to Docker are due to misconfiguration rather than direct exploit. While work has been previously conducted with regards to container security and separately applying Zero Trust Networking Architecture to containers, in this work we will investigate the security state of a default deployment of the Docker container engine on Linux and analyse how the principals of Zero Trust Architecture can be extended beyond the domain of networking, distilled into a ”Zero Trust Containers Architecture” and applied to secure Docker deployments. In order to determine this, research was conducted into the current state of Docker security and Zero Trust Architecture. Practical and theoretical attacks were reviewed against a default Docker deployment to identify common themes and areas of issue. Results were used to advise a generalised trust-based framework which was then used to analyse a Docker deployment and validate mitigation of a selection of the identified attacks, proving out the concept of the proposed “Zero Trust Container Architecture” framework.
“English First” APK for Facilitating Autonomous Screenagers

Digital native or screenagers cannot be separated from technology in their education and entertainment (edutainment). That phenomenon gives them an opportunity to be autonomous learners. Regarding the principle of autonomous learners, this study was aimed at developing an Android Package Kit (APK) smartphone application as learning media for facilitating screenagers. A research and development design comprising need analysis, design, and development was employed during the study. Freshmen year of English Language Education who attended English Intensive Course (IEC) were involved in the study. The data were collected through students’ interview guide, document analysis, expert judgment rubrics, and a questionnaire. The result showed that the freshmen year needed innovative, challenging, and fun English learning media in the course. In addition, the application was developed based on five topics found in the document analysis, namely “Meeting at the Campus”, “What You Are Wearing”, “Directions”, “Weather”, and “Necessity and Obligation”. From the result of the expert judgment, the learning media were considered excellent. Both learners and lecturers in this digital era should consider the needs of utilizing technology in teaching and learning process specifically for facilitating autonomous learners.
Exploring the vulnerability in the inference phase of advanced persistent threats

In recent years, the Internet of Things has been widely used in modern life. Advanced persistent threats are long-term network attacks on specific targets with attackers using advanced attack methods. The Internet of Things targets have also been threatened by advanced persistent threats with the widespread application of Internet of Things. The Internet of Things device such as sensors is weaker than host in security. In the field of advanced persistent threat detection, most works used machine learning methods whether host-based detection or network-based detection. However, models using machine learning methods lack robustness because it can be attacked easily by adversarial examples. In this article, we summarize the characteristics of advanced persistent threats traffic and propose the algorithm to make adversarial examples for the advanced persistent threat detection model. We first train advanced persistent threat detection models using different machine learning methods, among which the highest F1-score is 0.9791. Then, we use the algorithm proposed to grey-box attack one of models and the detection success rate of the model drop from 98.52% to 1.47%. We prove that advanced persistent threats adversarial examples are transitive and we successfully black-box attack other models according to this. The detection success rate of the attacked model with the best attacked effect dropped from 98.66% to 0.13%.
Low-Light Image Enhancement Network Based on Recursive Network

In low-light environments, image acquisition devices do not obtain sufficient light sources, resulting in low brightness and contrast of images, which poses a great obstacle for other computer vision tasks to be performed. To enable other vision tasks to be performed smoothly, it is essential to enhance the research on low-light image enhancement algorithms. In this article, a multi-scale feature fusion image enhancement network based on recursive structure is proposed. The network uses a dual attention module-Convolutional Block Attention Module. It was abbreviated as CBAM, which includes two attention mechanisms: channel attention and spatial attention. To extract and fuse multi-scale features, we extend the U-Net model using the inception model to form the Multi-scale inception U-Net Module or MIU module for short. The learning of the whole network is divided into T recursive stages, and the input of each stage is the original low-light image and the inter-mediate estimation result of the output of the previous recursion. In the t-th recursion, CBAM is first used to extract channel feature information and spatial feature information to make the network focus more on the low-light region of the image. Next, the MIU module fuses features from three different scales to obtain inter-mediate enhanced image results. Finally, the inter-mediate enhanced image is stitched with the original input image and fed into the t + 1th recursive iteration. The inter-mediate enhancement result provides higher-order feature information, and the original input image provides lower-order feature information. The entire network outputs the enhanced image after several recursive cycles. We conduct experiments on several public datasets and analyze the experimental results subjectively and objectively. The experimental results show that although the structure of the network in this article is simple, the method in this article can recover the details and increase the brightness of the image better and reduce the image degradation compared with other methods.
Early Detection of Breast Cancer Tumors using Linear Discriminant Analysis Feature Selection with Different Machine Learning Classification Methods

Globally, the frequency of breast cancer and its morality speak to a critical and developing risk for the developing countries. In Asia, Pakistan has the biggest rate of breast cancer. It is evaluated that every year 83,000 cases were reported in Pakistan and over 40,000 deaths are caused by breast cancer. Patients suffering from this malignancy have a better chance of surviving if they are diagnosed early. Many Early identification of breast cancer can be achieved using data mining techniques, allowing preventative treatments to be done. In this research Wisconsin Breast Cancer Dataset (WBCD) and Duke Breast cancer dataset (DBDS) are used with Linear Discriminant Analysis (LDA) feature selection with Support Vector Machine (SVM), Decision Tree (DT), Neural Network and Random Forest (RF) machine learning classifiers to predict breast cancer tumors. The finding of the proposed model is that feature selections through LDA improve the accuracy of detecting tumors and also reduce time duration of executing model. The best machine learning model with LDA feature selection is Neural Network Model with highest accuracy 1.00 among all classification models and also consume less time.