Reviewerly/exHarmony huggingface dataset:
Towards Quantification of Explainability Algorithms

Despite the proliferation in the use cases and the escalation in accuracy of Deep Neural Networks, the fact that such networks are black box models hamper our reliability in these models. Explainable Artificial Intelligence (XAI) algorithms work on building the trust in such models by attempting to interpret the basis of the predictions. However, due to the lack of work focused on developing quantitative approaches for such explanations, these explanations remain subjective in nature. Hence, the current XAI models require endorsement from the domain expert to justify the explainability. In this paper, we propose an approach which aims at obtaining quantitative explanations of three CNN architectures (namely InceptionV3, InceptionResNetV2, NASNetLarge) implemented using LIME XAI algorithm by proposing four general desiderata - Consistency, Efficiency, Integrity and Preciseness. The obtained experimental results offer enough information to form a quantitative relationship between the explanations of the three CNN models.
The Use of Finite Element Analysis in Design Courses and its Effect in Product Development

Computer-aided analysis and design tools have become essential to the modern design environment, and some parts of engineering curricula must be included in this field. Regarding the selection of materials, some engineering careers (programs) study their functional behavior primarily by using software and finite element analysis (FEA), a numerical method for obtaining approximate solutions for boundary value problems. Its teaching is quite demanding and challenging. FEA, by its nature, requires an excellent knowledge of elasticity theory, matrix algebra, mechanics, and computer programming skills to various degrees. This investigation intends to fill the gap between engineering and design in FEA education and show the impact of implementing FEA on the designer's mind and the outcomes in product development as part of the course “Materials Selection for Designers.” At the end of the study, the design students learned to correctly perform and interpret a basic FEA simulation, taking the interpretation beyond the engineering objectives (i.e., knowing the material's behavior and limits) to consider applications of this knowledge that would positively impact the economy, ecology, and aesthetics.
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection

We propose a new method for testing antenna arrays that records the radiating electromagnetic (EM) field using an absorbing material and evaluating the resulting thermal image series through an AI using a conditional encoder-decoder model. Given the power and phase of the signals fed into each array element, we are able to reconstruct normal sequences through our trained model and compare it to the real sequences observed by a thermal camera. These thermograms only contain low-level patterns such as blobs of various shapes. A contour-based anomaly detector can then map the reconstruction error matrix to an anomaly score to identify faulty antenna arrays and increase the classification F-measure (F-M) by up to 46%. We show our approach on the time series thermograms collected by our antenna testing system. Conventionally, a variational autoencoder (VAE) learning observation noise may yield better results than a VAE with a constant noise assumption. However, we demonstrate that this is not the case for anomaly detection on such low-level patterns for two reasons. First, the baseline metric reconstruction probability, which incorporates the learned observation noise, fails to differentiate anomalous patterns. Second, the area under the receiver operating characteristic (ROC) curve of a VAE with a lower observation noise assumption achieves 11.83% higher than that of a VAE with learned noise.
Holistic Recommendation System Framework for Health Care Programs

Government health care programs for disease surveillance, control, and prevention adopt a strategized technique in collecting information from various sources, monitor the trend of disease outbreak, initiate actions, and detect the response for further course of action. The main components for implementing such programs are integration and decentralization of surveillance activities at the zonal, and subzonal levels, developing and deploying human resources involving field workers to experts, strengthening public health lab facilities, and most importantly embracing digital information communications technology (ICT), the key component in binding all other components in bringing the effective implementation of the program. The proposed work explains the adoption of ICT through a holistic framework depicting the strategy and techniques in implementing the program. It discusses the digitalized strategy in data collection as well as data compilation, data analysis through integrated decision support modules, and proper presentation and dissemination of information data. The key element of the framework involves adopting various recommendation systems techniques in the analysis of data for information filtering, decision support, and data dissemination at various stages of handling the data. It suggests the selection of different recommendation techniques to address various needs such as predicting the disease dynamics and provide decision support in predicting likely hood of the next outbreak of the disease, rate of speeding, demographic details of spread, etc. The framework proposes a strategy to use smartphone-based mobile intervention to support the program and also proposes an API-based data exchange architectural style for data gathering and dissemination connected to various sources and endpoints.
Document Enhancement and Binarization Using Deep Learning Approach

The majority of hardcopy documents are being converted to the digital representations in this era of digitalization. Electronic scanning is used to store and preserve vast amounts of documents during this transformation process. The images contained in these documents, however, must be rendered legible and searchable in order to make them genuinely valuable. One way to do this is through document image binarization. A variety of strategies for binarizing document images have been suggested by a number of scholars. This paper introduces a unique deep learning framework and shows how it can be used for improving and binarizing documents. Unlike traditional algorithms, which estimate the binary labels for each of the pixel in the input image, neural network is taught to detect degradations present in the document pictures for creating homogeneous versions of the damaged input pictures. In this paper, we have firstly improved the quality of degraded documents and then performed binarization. For this, we introduced the U-Net framework, a system of deep learning. Experiments on many publicly accessible benchmark datasets reveal that our suggested technique produces a clear form of the images that have been damaged, indicating that when binarization is performed on these better versions of images, hopeful outcomes are obtained.
A Novel Duplication-Based Countermeasure to Statistical Ineffective Fault Analysis

The Statistical Ineffective Fault Analysis, SIFA, is a recent addition to the family of fault-based cryptanalysis techniques. SIFA-based attack is shown to be formidable and is able to bypass virtually all the conventional fault attack countermeasures. Reported countermeasures to SIFA incur overheads of the order of at least thrice the unprotected cipher. We propose a novel countermeasure that reduces the overhead (compared to all existing countermeasures) as we rely on a simple duplication-based technique. In essence, our countermeasure eliminates the observation that enables the attacker to perform SIFA. The core idea we use here is to choose the encoding for the state bits randomly. In this way, each bit of the state is free from statistical bias, which renders SIFA unusable. Our approach protects against stuck-at faults and also does not rely on any side channel countermeasure. We show the effectiveness of the countermeasure through an open-source gate-level fault attack simulation tool. Our approach is probably the simplest and the most cost-effective.
Intelligent interactive test system for the human-machine interface function of electric vehicle charging piles based on computer vision

In recent years, the market share of new energy vehicles has been increasing, so the number of electric vehicles charging piles put into use has been rising. Therefore, the inspection work of the charging pile before leaving the factory is very important. The human-machine interface function is a necessary test item, the current testing method of which is still mainly manual, with low efficiency, high error rate, high cost, and the problem of easy electric shock. In order to solve these problems, this paper proposes an intelligent interactive test system for the human-machine interface function of electric vehicle charging piles based on computer vision to replace manual labor. The system consists of three parts: the interface perception module, the robotic arm control module and the cloud platform. This paper also puts forward the key technologies in the interface perception module, including the multi-task network that completes the interface positioning and text detection, and the parallel multi-model text recognition network. The system has been verified by twenty devices in the quality inspection workshop of the State Grid of China, which can well complete the quality inspection of charging piles. The paper also provides the comparison results with other quality inspection algorithms.
AI Education Based on Evaluating Concentration of Students in Class

Attention is an indispensable part of the learning process. Currently, teachers in most of the schools judge student’ concentration only by their eyes subjectively. But this approach not only distracts teachers from the lesson but also difficult to obtain feedback on the result after class. In this paper, we introduce a method based on machine vision to evaluate students’ concentration. We apply the YOLO algorithm on facial movement detection to predict whether students are engaged in class. It not only helps to differentiate instruction for students but also helps teachers to optimize their lessons. The experiments demonstrate that the method can achieve the assessment of students’ concentration within a small-size classroom.
Image encryption using modified perfect shuffle-based bit level permutation and learning with errors based diffusion for IoT devices

Image encryption is important for IoT devices which handle military, medical and other confidential images. In this paper, stream cipher based image diffusion is performed using tent chaotic map and block based encryption is done by taking four bytes every time to multiply the matrix formed using the secret key and error matrix. Pixel permutation is performed in two stages: block level pixel permutation and whole image permutation to resist the statistical attacks. Bit level permutation is performed with piecewise linear chaotic map using modified perfect shuffle technique to overcome differential attacks. Various statistical and differential analyses are performed on the encrypted image and the results obtained using these analyses convey its resistance to statistical and differential attacks.
Bangsamoro Data Challenge - Data-Driven Peace-Building through Collaborative Ideation

We describe the Bangsamoro Data Challenge, a platform for collaboration, ideation, and solution development to solve social issues in the Bangsamoro Autonomous Region of Muslim Mindanao (BARMM) the Philippines. The challenge invited data enthusiasts, students, and working professionals to create solutions by utilizing the publicly-available datasets such as government, satellite, climate, health, agriculture, and local data. We discuss the history, relevance, process and impact of this platform, as well as the current status and potential of the prototypes generated by the exercise. The success of the challenge has paved the path towards data-driven development of the BARMM.
Information System Security Risk Priority Number: A New Method for Evaluating and Prioritization Security Risk in Information System Applying FMEA

The emergence of the COVID-19 pandemic led several organizations around the world and in the most varied areas of activity, to move from the intention to implement a digital transformation in the medium/long-term, to an instant obligation to apply the digital transformation. The organizations’ ability to adapt immediately meant their survival and even in some cases a positive evolution of their business. The digital transformation applied in an abrupt way has uncovered some critical factors for its success. One of the most relevant factors will be information security. Many of the digital systems put into operation more intensively during the pandemic, have shown to be highly fragile on issues related to information security. One relevant problem of the organizations is the low effectiveness and efficiency of financial, human, and material resources, allocated to the reduction or mitigation of the risks identified in their information systems. This study aims to offer a new method for prioritizing security risks. The new proposed method directs the organizations resources to more effectively and efficiently actions to reduce or mitigate the identified vulnerabilities of the information system.
A Scheme of Relational Database Desensitization Based on Paillier and FPE

Data desensitization is a process that makes some kind of special transformations on sensitive data to keep the data secure. At present, relational database is widely used in various IT fields and the data stored in relational database can be queried efficiently by using indexes of database fields. With the rapid development of cloud computing, how to make the data in relational database secure and can be queried efficiently has become a problem to be solved. In order to resolve the problem of relational database desensitization, a scheme of desensitization is proposed. The improved FF1 algorithm which is called FF1-SM4 is proposed by changing AES to SM4. The general framework of scheme and the process of desensitization are designed. Paillier is utilized to encrypt numeral data and FF1-SM4 is utilized to encrypt character string. The scheme makes the data secure on the cloud, desensitized character string data can be queried using traditional SQL without decrypting and desensitized numeral data can be calculated in the form of ciphertext. The results show that the data security in relational database on the cloud can be maintained and can be used efficiently.
Using Garbled Circuit for Secure Brokering

Many domains such as military and agriculture collect data from sensors and other trusted applications, process them, and distribute them according to predetermined rules to interested entities. These systems need to trust the data broker, which could be hosted in a public environment. This paper assesses the use of garbled circuits technique to protect the collected time-sensitive data and filtering and distribution policy from suspicious brokers. It reports about the design of a secure brokering protocol and preliminary performance evaluation of the implementation.
R-Calculus for PL

There are four tableau proof systems in propositional logic. Let $$*\in \{{\texttt {t}},{\texttt {f}}\}.$$ A theory $$\mathbf{X}$$ is $$*$$ -valid if for any assignment v, there is a formula $$X\in \mathbf{X}$$ such that $$v(X)=*.$$ There is a monotonic,sound and complete tableau proof system $$\mathbf{T}^*$$ such that $$\mathbf{X}$$ is provable in $$\mathbf{T}^*$$ if and only if $$\mathbf{X}$$ is $$*$$ -valid.
The Mixed Latent Markov Chain Model

A discussion of dynamic latent class analysis in a framework of the latent Markov chain model is made, following Chap. 5 . First, basic dynamic latent class models are briefly reviewed, and the equivalence of the latent Markov chain model and the mixed latent Markov chain model is shown. Second, the ML estimation procedure for the models via the EM algorithm is discussed in relation to that for the latent Markov chain model. A numerical example is given to demonstrate the method for the mover-stayer model and the mixed latent Markov chain model. Finally, advanced studies for the mixed Markov modeling are briefly reviewed to link to further research.
Evaluation method of second classroom teaching quality based on multifaceted Rasch model

Aiming at the problems of poor teaching effect and large deviation of teaching quality evaluation results in the current second classroom, this paper puts forward the second classroom teaching quality evaluation method based on the multi-faceted Rasch model, uses the multi-faceted Rasch model to set the second classroom teaching ability evaluation index, standardize the evaluation scale and construct the evaluation model, so as to ensure the accuracy of teaching evaluation and improve the second classroom teaching quality, Finally, experiments show that the second classroom teaching quality evaluation method based on multi-faceted Rasch model has high accuracy, can better correct the evaluation deviation value and ensure the teaching quality.
Smartphone Spectatorship in Unenclosed Environments: The Physiological Impacts of Visual and Sonic Distraction During Movie Watching on Mobile Devices

Smartphones’ popularity is based on their pervasiveness, portability, and the wide range of functions they encompass: they can play high-definition moving-image content without spatial and temporal constraints. However, the lack of spatial and temporal frameworks can account for environmental distractions. Distractions can be stimuli of various modalities, intensities, and can hold different amounts of relevance for a viewer. In this paper, we argue that distraction effect in movie-watching is based on a distractor’s modality, neutrality, and ecological relevance to the movie. To test the effects of these properties, we recorded viewers’ gaze and electrodermal activity while they watched a narrative film sequence on smartphone and projector screens in the presence of sonic and visual distractors. We found that screen type can affect attention and arousal: in comparison to projector viewers, smartphone viewers experienced lower arousal and were more likely to shift their attention from the movie even when a distractor was closely related to the movie. It was also observed that distractors that require urgent attention and are unrelated to the movie redirect the viewer’s attention and increases electrodermal activity values. In contrast, distractors with ecological relevance to the movie are less likely to induce changes in attention and arousal.
Study of Secure Communication over the 5G Network

With the newly developed technologies in communication over mobile networks we are breaking into the swiftest 5G network era. There will be different applications available in 5G era to serve the purpose for different requirements. There would be various public key cryptosystems for the different 5G network slicing. Hence security it serves us with security issues over conjunction of heterogeneous systems and needed to be taken into consideration. To make our communication secure for transmission over 5G network in the domain of isolated public key cryptosystems, here are proposed two schemes based on heterogeneous signcryption provides mutual communication over Public Key Infrastructure and Certificate Less Cryptography based environment. Our setup includes working on two heterogeneous Raspberry Pi cryptosystems to simulate the method for communication between interprocesses among various available public key cryptosystems. Moreover, quantification and analysis of performance of each scheme is to be done. Over the efficiency and security our proposed scheme is high if compared to existing schemes.
An Outlier Detection Algorithm Based on Spectral Clustering

Outliers refer to data that is far away from conventional data objects, resulting from different mechanisms, and there is a clear gap from conventional data. Outlier detection algorithms include statistics-based, distance-based, density-based, and cluster-based algorithms. Spectral clustering has become a research hotspot in the field of clustering because it can cluster on arbitrary-shaped data samples.Based on Self-Tuning spectral clustering, optimize the scale parameters, improve the method of determining the initial centroid and the optimal clustering center in the K-means algorithm, calculate the outlier index for each data, and sort the outliers according to the size of the outlier index.Group points.Through the verification of artificial data and real data sets, the experiments show that the outlier detection algorithm based on spectral clustering is superior to the traditional outlier detection algorithm.
Intelligence Information Retrieval System Modeling for Afaan Oromo

Due to today’s information overload, finding and retrieving desired data or information has become difficult. Due to this new phenomenon, users will have a harder time locating and retrieving relevant information. It is now the norm rather than the exception to have data and information in multiple languages. It is critical in Ethiopia, where huge amounts of Afaan Oromo data are produced daily. With massive document collections come archival and search issues, putting these data and information characteristics to the test. Afaan Oromo users could easily search for and retrieve data that was relevant to their needs and interests using a hybrid information retrieval system developed for Afaan Oromo. Performance of information retrieval systems (IRS) has been under-researched in the Afaan Oromo linguistic domain. The efficiency of these systems has been found to be significantly lacking. For these issues, the AOIR system’s performance was improved by integrating various IR system approaches. The prototype includes the current indexing and searching subsystems. On-line news articles (Oromia Broadcasting Network, VOA Afaan Oromo), websites, books, and the Afaan Oromo Bible were used to gather 1000 Afaan Oromo text documents for the experiment. To identify terms and vocabulary that contained content, the text was subjected to text operations such as tokenization, normalization, stop-word removal and stemming and calculated using tf-idf term weighting scheme. After the experimental analysis in Python 3.7, precision, recall, and F-measure were all greater than 96.6 percent. The polysemy issue continued to affect the system’s overall performance. The paper also advocates for more work on system performance optimization to advance the AOIR.
Teachers’ Perceptions of Using Virtual Reality Technology in Classrooms: A Large-Scale Survey

High-immersion virtual reality (VR) technology is often associated with gaming. Yet, it is increasingly popular in educational contexts due to its potential to engage and motivate learners. Prior to VR technology integration in the classroom, the acceptance or resistance toward VR needs to be explored. This paper reports the results obtained from a large-scale (N = 20,876) survey on teachers’ attitudes toward the use of VR for education. The survey took place in 2020 and explored the relationships between the teachers’ VR integration level and their instructional approaches, as well as the frequency of VR use. Furthermore, the survey yielded answers on the relationship between the availability of information technology (IT) personnel and the frequency of VR use. Overall, teachers had moderately positive perceptions of the use of VR in education. There was no strong correlation between instructional approaches and the level of VR integration, but lower levels of VR integration were associated more traditional teaching approaches. The results revealed a positive correlation between the level of VR integration and the frequency of VR use. However, the VR frequency use had a weak correlation with the availability of IT personnel.