Reviewerly/exHarmony huggingface dataset:
Design and Analysis of Area and Energy Efficient Reconfigurable Cryptographic Accelerator for Securing IoT Devices

Achieving low-cost and high-performance network security communication is necessary for Internet of Things (IoT) devices, including intelligent sensors and mobile robots. Designing hardware accelerators to accelerate multiple computationally intensive cryptographic primitives in various network security protocols is challenging. Different from existing unified reconfigurable cryptographic accelerators with relatively low efficiency and high latency, this paper presents design and analysis of a reconfigurable cryptographic accelerator consisting of a reconfigurable cipher unit and a reconfigurable hash unit to support widely used cryptographic algorithms for IoT Devices, which require block ciphers and hash functions simultaneously. Based on a detailed and comprehensive algorithmic analysis of both the block ciphers and hash functions in terms of basic algorithm structures and common cryptographic operators, the proposed reconfigurable cryptographic accelerator is designed by reusing key register files and operators to build unified data paths. Both the reconfigurable cipher unit and the reconfigurable hash unit contain a unified data path to implement Data Encryption Standard (DES)/Advanced Encryption Standard (AES)/ShangMi 4 (SM4) and Secure Hash Algorithm-1 (SHA-1)/SHA-256/SM3 algorithms, respectively. A reconfigurable S-Box for AES and SM4 is designed based on the composite field Galois field (GF) GF(((22)2)2), which significantly reduces hardware overhead and power consumption compared with the conventional implementation by look-up tables. The experimental results based on 65-nm application-specific integrated circuit (ASIC) implementation show that the achieved energy efficiency and area efficiency of the proposed design is 441 Gbps/W and 37.55 Gbps/mm2, respectively, which is suitable for IoT devices with limited battery and form factor. The result of delay analysis also shows that the number of delay cycles of our design can be reduced by 83% compared with the state-of-the-art design, which shows that the proposed design is more suitable for applications including 5G/Wi-Fi/ZigBee/Ethernet network standards to accelerate block ciphers and hash functions simultaneously.
New Teachers’ Perceptions of Their Impact on Student Learning While Developing Knowledge and Skills to Teach Online

Due to the health pandemic of 2020, teachers have been forced to initiate online teaching delivery models with little preparation just as students and parents have had their routines abruptly altered from in-person schooling to hybrid and fully online instruction (Mecham, et al., 2021). The pandemic brought to light new opportunities for novice teachers to learn how to implement pedagogical strategies using digital tools to support student learning and development in remote settings. As part of a larger case study of 23 novice teachers who graduated from our urban institution, these results focus on new teachers’ perceptions of their own development and their impact on student learning while teaching online, during the pandemic. Semi-structured interviews focused on educators’ approaches to instruction across 12-18 months of the pandemic. Data were analyzed using NVivo through open coding. Findings help us understand these new teachers’ perceptions of their impact on student learning while developing knowledge and skills to teach fully online. Four themes are highlighted in this study as a glimpse into new teachers’ perceptions of themselves and their impact during this time. New teachers describe their impact on student learning as their ability to build relationships and trust with learners and their families, to use students’ funds of knowledge to engage them in meaningful tasks, to teach with digital tools, modeling and collaborating with learners, and to incorporate various digital tools to monitor progress, assess, and provide feedback online. Novices describe student engagement and motivation as resulting student outcomes due to their abilities to build relationships, tap into students’ multiple knowledge bases, and use digital tools to teach and assess student learning. They describe their new knowledge and skills teaching online as unintended outcomes related to their own development and learning.
Mobile Application for Keeping Records of Class Attendance

Recording attendance in the class meeting session is often time-consuming, prone to human error(s) and frequent recording of incorrect data. Also, tracking attendance per student can be a tedious process since it involves some sort of manual tracking/counting by pen and paper. This method consumes additional time and data may be prone to error from time to time. To avoid these problems, this paper describes a mobile application for student attendance. This application is mainly designed for students and professors who maintain attendance. Using this application, students can mark their attendance with mobile devices, furthermore, application allows the professors to keep track of student’s attendance and store data for further retrieval. With advent of smartphones and tablets, which are very convenient to use, this process can be fully automated using the right technology. The tools used for application development are Kotlin and XML.
Fully automated condyle segmentation using 3D convolutional neural networks

The aim of this study was to develop an auto-segmentation algorithm for mandibular condyle using the 3D U-Net and perform a stress test to determine the optimal dataset size for achieving clinically acceptable accuracy. 234 cone-beam computed tomography images of mandibular condyles were acquired from 117 subjects from two institutions, which were manually segmented to generate the ground truth. Semantic segmentation was performed using basic 3D U-Net and a cascaded 3D U-Net. A stress test was performed using different sets of condylar images as the training, validation, and test datasets. Relative accuracy was evaluated using dice similarity coefficients (DSCs) and Hausdorff distance (HD). In the five stages, the DSC ranged 0.886-0.922 and 0.912-0.932 for basic 3D U-Net and cascaded 3D U-Net, respectively; the HD ranged 2.557-3.099 and 2.452-2.600 for basic 3D U-Net and cascaded 3D U-Net, respectively. Stage V (largest data from two institutions) exhibited the highest DSC of 0.922 ± 0.021 and 0.932 ± 0.023 for basic 3D U-Net and cascaded 3D U-Net, respectively. Stage IV (200 samples from two institutions) had a lower performance than stage III (162 samples from one institution). Our results show that fully automated segmentation of mandibular condyles is possible using 3D U-Net algorithms, and the segmentation accuracy increases as training data increases.
Know SOLID Principles

C# is a powerful language. It supports object-oriented programming and has lots of features. I believe that compared to the old days, coding is easier with the support of these powerful features. But the hard truth is that simply using these features in an application does not guarantee that you have used them in the right way. In any given requirement, it is vital to identify the purpose of classes, objects, and how they communicate with each other. In addition, your application must be flexible and extendable to fulfill future enhancements. Now the question is, where are these guidelines? To answer this, you need to follow experts’ footprints. Robert C. Martin is a famous name in the programming world. He is an American software engineer, is a best-selling author, and is also known as Uncle Bob. He has promoted many principles, and a subset of them are as follows:
Cryptanaylsis to Sowjanya et al.’s ABEs from ECC

Attribute-based encryption is a one-to-many, fine-grained access control mechanism, which is suitable for the modern data sharing model in clouds. To reduce the computation overhead, several attribute-based encryption schemes based elliptic curve cryptography have been proposed in the literatures. However, most of these schemes are not secure. In this manuscript, we review on the pairing-free ciphertext-policy attribute-based encryption proposed by Sowjanya et al. in 2021, and give the cryptanalysis on their scheme. More precisely, we demonstrate an attack method to their scheme, which allows a third party, called decryption server in their scheme, to generate private key of any chosen attribute set. Thus, by applying our attack, the decryption server is able to decrypt any ciphertext.
An Attention-Based Adversarial Disentangle Heterogeneous Embedding for Improving Node Classification

For many years, within the information network analysis and mining (INAM) area, heterogeneous information network (HIN) is considered as a hot topic which have attracted attentions from many researchers. In fact, HIN is considered as the sufficient reflection of real-world information networks which contains multiple types of nodes and links (e.g., knowledge graph, social networks, etc.). Within HIN-based analysis and mining domain, meta-path is considered as the key concept which is employed to extract rich contextual information from semantic relationships, in forms of meta-paths, between multi-typed nodes. However, the various semantic informative sources which are obtained from different meta-paths between nodes are frequently considered as ambiguous and entangled for primitive tasks in INAM area such as node classification, clustering or link prediction. Recently, there are several well-known studies which have dedicated on the disentangling the meta-path-based representations of nodes in HIN through different techniques such as variational embedding and adversarial learning paradigms. However, the disentangled meta-path-based node representation learning in HIN still have several unresolved problems. One of the most crucial limitations of this research direction is related to the capability of properly interpretation for meta-path-varied disentangled node embeddings in order to achieve better fine-tuned performance for different downstream tasks, such as node classification. In order to overcome this problem, in this paper we proposed an improvement of disentangled HIN representation learning for node classification task with the employment of attention-based mechanism, called as: HAttDE model. A self-attention-based mechanism is placed between the encoding and generating components in order to softly align and interpret multiple fused meta-path-based node representations into better classification-friendly embedding spaces. Extensive experiments in benchmark networked datasets demonstrated the effectiveness of our proposed model in comparing with recent state-of-the-art HIN embedding baselines.
Understanding user experience of an open design-clothing product.

Few studies related open design to the clothing sector, but none explored how users would experience it. The study reported here aimed to investigate how people with some or without prior sewing knowledge - advanced and amateur users – experience an open design-clothing product. Following four fashion design heuristics, a garment was created and distributed as DIY kits among advanced and amateur users. Data were collected in two stages: assembly and personalization. The results indicate that although skills play a significant role during assembly, other factors, like cross-generational differences and personal taste, influence how users experience an open design product. Furthermore, the study shows that given the necessary support, the open design can be used by a heterogeneous public, amplifying the participation of users with little or without prior sewing skills in clothing co-creation.
XAI: Using Smart Photobooth for Explaining History of Art

The rise of Artificial Intelligence has led to advancements in daily life, including applications in industries, telemedicine, farming, and smart cities. It is necessary to have human-AI synergies to guarantee user engagement and provide interactive expert knowledge, despite AI's success in "less technical" fields. In this article, the possible synergies between humans and AI to explain the development of art history and artistic style transfer are discussed. This study is part of the "Smart Photobooth" project that is able to automatically transform a user's picture into a well-known artistic style as an interactive approach to introduce the fundamentals of the history of art to the common people and provide them with a concise explanation of the various art painting styles. This study investigates human-AI synergies by combining the explanation produced by an explainable AI mechanism with a human expert's insights to provide reasons for school students and a larger audience.
Synthetic face generation from in-the-wild face components swapping

Facial identification has recently been a legal con-cern for protecting one's identity and personal confidentiality. Many face synthesis techniques were used to safeguard individual users' data. This work presents a technique for generating synthetic faces from in-the-wild face components. The face components, such as the eyes, eyebrows, nose, and mouth, were extracted from a facial landmark of in-the-wild images and ran-domly replaced with the original image. Generative Adversarial Networks (GANs) for face restoration were then used to denoise the swapped image while preserving the original colorization. The experiments on face swapping with ten thousand of wild images demonstrate an average of 0.723 difference from the source image. The result shows that our face component swapping technique could be an effective lawful way to use facial data in the future.
“Dignity Embodies Duty”: Islamic Perspective on Combating “Hate Speech”

Abstract Hate speech ( ‘al-jahr bi’-sūʾ min al-qawl in the Qur’anic description) continues to be the subject of contentious debate. Arguably, the notion of “unregulated speech” in the liberal discourse encourages hate speech on the pretext of “defending” the right to freedom of speech. Islam recognises human dignity as the underlying basis of all human rights and freedoms, including the right to freedom of speech. Here arise two core questions. First, is freedom of speech and expression an absolute right or has Islam imposed certain conditions on the human agency while this right is exercised? Second, what do these conditions concerning freedom of speech imply about the regulation of hate speech? I approach these questions by beginning with the introduction of karamah principle followed by an overview of theoretical conceptualisations of free speech in the Western context. Finally, the Islamic position on the subject is brought forth through the conceptual analysis of relevant Islamic texts. The article draws three conclusions. First, human dignity is the intrinsic condition that surpasses all rights and freedoms. Second, the idea of “unregulated speech” is controversial and has been a reason for harming human dignity and making the public discourse inimical to social order. Third, there should be a legal, moral and rational reconciliation between exercising ‘speech limitations’ and ‘free speech’ to ensure sustainable peace and social cohesion.
On the Performance of Digital Modulation Classification for Cooperative Multiple Relays Network System without Direct Channel

Automatic modulation classification (AMC) is defined as the automatic identification of modulation format of the sensed signal. We consider in this work a features-based digital modulation identification approach for wireless cooperative multi-relay networks using amplify-and-forward (AF) protocol and without direct channel between source and destination nodes. We compare the performances of three models among the most commonly used classifiers, namely decision tree (TREE), K-nearest neighbors (KNN), and support-vector machine (SVM). Comparison criteria are probability of identification, recall, precision and F-Scores. Our purpose is to discriminate between the modulation format and order of different M-ary shift keying modulations using higher order cumulants (HOCs) of the received signal. To select the most suitable classifier, we firstly consider a single AF relay cooperative network system with direct link (source to destination nodes). Then, the best classifier is applied to the cooperative multiple relays network alternative ignoring the direct link. Obtained results, within the considered scenario, show that the SVM classifier is more efficient and achieves high probability of correct identification in acceptable snr level. This performance improves with the increase of the number of relays.
Investigating the mediating effect of human resource strategies on the relationship between knowledge management and job performance of employees

The present study aimed to investigate the mediating role of human resource strategies in the relationship between knowledge management and the job performance of Najafabad Tax Affairs Department employees. For this purpose, out of 100 employees of the tax affairs 80 people were selected by proportionate stratified random sampling based on Cochran's formula. Questionnaires with appropriate validity and reliability were used to collect the data. To analyse the data, first the acceptability of the theoretical model was examined using LISREL software, and it was found that the model is acceptable for the statistical population. The analysis results showed that human resource strategies have an effective role in the relationship between knowledge management and job performance of Najafabad Tax Affairs Department employees. Accordingly, employee participation strategies, the reward system, and employee training play a role in the relationship between knowledge management and job performance of Najafabad Tax Affairs Department employees.
Domain specific Question &amp; Answer generation in Tamil

Automatic Question & Answer generation is a challenging task in natural language processing. The proposed system is capable of automatically generating questions and answers from a given history related text content in Tamil. It processes the input text using various NLP techniques. The system has four modules namely, Preprocessing module, Rulebased module, Named Entity Recognition (NER) module, Question Answer Generator (QAG) module. Regex patterns and gazetteers are used in rule-based module and machine learning approach is used for NER module. A NER module built using Conditional Random Field (CRF) classifier is used which is trained on a manually tagged dataset for history domain in Tamil. Questions are formed using grammatical and defined rules from the named entities identified from both rule-based and NER module. An affix stripping algorithm implemented to find the inflection suffix. A history text from Wikipedia is evaluated by 16 native Tamil speakers under categories like undergraduates, graduates and experts. According to the evaluation results, 62.22% of total generated questions are grammatically correct and meaningful questions despite the domain and language related challenges.
Concept drift detection in toxicology datasets using discriminative subgraph-based drift detector

Abstract Due to the increasing importance of graphs and graph streams in data representation in today’s era, concept drift detection in graph streaming scenarios is more important than ever. Contributions to concept drift detection in graph streams are minimal and practically non-existent in the field of toxicology. This paper applied the discriminative subgraph-based drift detector (DSDD) to graph streams generated from real-world toxicology datasets. We used four toxicology datasets, each of which yielded two graph streams – one with abrupt drift points and one with gradual drift points. We used DSDD both with the standard minimum description length (MDL) heuristic and after replacing MDL with a much simpler heuristic SIZE (number of vertices + number of edges), and applied it to all generated graph streams containing abrupt drift points and gradual drift points for varying window sizes. Following that, we compared and analyzed the results. Finally, we applied a long short-term memory based graph stream classification model to all the generated streams and compared the difference in the performances obtained with and without detecting drift using DSDD. We believe that the results and analysis presented in this paper will provide insight into the task of concept drift detection in the toxicology domain and aid in the application of DSDD in a variety of scenarios.
Improving the Performance of Cooperative Transmission Protocol Using Bidirectional Relays and Multi User Detection

Cooperative transmission is a new communication strategy that takes advantage of the broadcast aspect of wireless channels to increase transmission by allowing network nodes to cooperate. Its potential for further application in wireless networks is limited due to its low spectral efficiency and the requirement for orthogonal channels. Using MUD, a cooperative transmission protocol with good spectrum efficiency, diversity gain, and coding gain was developed in this study. The benefit of MUD cooperative transmission methods is that improving one user’s link can help other users. By completely exploiting the link between the relay and the destination, the coding and high diversity order can be attained. In this case, one of the nodes will operate as a relay, facilitating collaboration among the network’s various nodes. MUD will determine which user will send the data and send it to the appropriate destination. Each node in our project will operate as a relay for any other node. Data loss and packet retransmission will be reduced as a result of this. The network’s performance will improve as a result of this. We utilize a dynamic base station to ensure that nodes outside the network broadcast packets to the network’s destination.
PyTorch Model Interpretability and Interface to Sklearn

Model interpretability is an area that needs special attention because it is connected with model adoption in particular and AI adoption in general. Users will adopt a model and framework if they can explain the decisions or predictions generated by the deep learning model. In this chapter, you will explore a new framework called Captum, which consists of a set of algorithms that can explain or help us interpret the predictions, model results, and layers of a neural network model. In this chapter, you are also going to use another framework called skorch, which is a library compatible for Scikit-learn users. Machine learning users prefer the sklearn library to train models, perform grid searches, and identify the best hyper parameters of the models—the same kind of seamless experience the users can experience when developing deep neural network models using PyTorch.
Ambient and Artificial Intelligence in Therapeutics

A brand-new approach to a technology called Ambient Intelligence (AmI) aims to empower people by creating a digital environment that is emotional-responsive, adaptable, and sensitive to their feelings, gestures, habits, and needs. The surroundings and position of the user are known to this environment. The most ambitious representation of AmI is Intelligent Mixed Reality (IMR), an improvement above conventional virtual reality environments. With the help of IMR, computer interfaces may be seamlessly incorporated into the physical world, allowing users to communicate and interact with their surroundings in a way that feels intuitive and natural. What effects will the adoption of the AmI paradigm have on medical care? The study emphasizes the relational and technological aspects of AmI to demonstrate its potential applications in healthcare. Medical professionals must pay close attention to human elements, project management, ergonomics, technology, and the relevant health service's institutional demographic structure to fully exploit the potential of AmI.