Reviewerly/exHarmony huggingface dataset:
A-unital Operations and Quantum Conditional Entropy

Negative quantum conditional entropy states are key ingredients for information theoretic tasks such as superdense coding, state merging and one-way entanglement distillation. In this work, we ask: how does one detect if a channel is useful in preparing negative conditional entropy states? We answer this question by introducing the class of A-unital channels, which we show are the largest class of conditional entropy non-decreasing channels. We also prove that A-unital channels are precisely the completely free operations for the class of states with non-negative conditional entropy. Furthermore, we study the relationship between A-unital channels and other classes of channels pertinent to the resource theory of entanglement. We then prove similar results for ACVENN: a previously defined, relevant class of states and also relate the maximum and minimum conditional entropy of a state with its von Neumann entropy. The definition of A-unital channels naturally lends itself to a procedure for determining membership of channels in this class. Thus, our work is valuable for the detection of resourceful channels in the context of conditional entropy.
Knowledge Discovery and Data Mining for Intelligent Business Solutions

Data Mining refers to the process of extracting previously unknown and useful patterns from large datasets. Traditional data mining approaches focus mainly on finding the most frequent patterns from databases. However, mining the frequent patterns alone is not sufficient in all scenarios. For example, a business manager might be more interested to find the most profitable items by taking into account frequency and profit both, rather than mining the most common items alone. Therefore, in recent years, the research focus has shifted to mining of high utility patterns from datasets, where utility is used to represent users’ preference and it can be cost, profit or any other aesthetic value depending upon the application. High-utility itemset mining (HUIM) deals with the problem of finding high utility itemsets (HUIs), where every item is associated with atleast two utility values-internal and external. HUIM has found numerous applications in web mining, cross-marketing, customer segmentation, medical treatments, etc. This paper explains the concept of HUIM, it’s relevance and applications, and also provides an in depth analysis of techniques and advancements in the field of HUIM.
Hate Speech Detection Model Using Bag of Words and Naïve Bayes

In this era of increasing hate and intolerance among the people, especially among those who interact with each other over the Web, there is a dire need of some technological innovation that would cater to this situation. The said hate and clash of opinions among the people often comes out in the form of hate speech in texts and in pictures. To counter this situation, we have come up with a hate speech detection model which would be able to detect and identify hateful and provocative content in a textual data, which is published on various social media websites, viz. Twitter, Facebook, and Instagram. The sole idea behind the making of this model is to be able to prevent every individual from spreading as well as witnessing hate-speech on different digital-platforms. We have developed a text classifier using basic principles of natural language processing. This has been achieved by the use of the bag of words model for feature extraction purposes, followed by various text filtering processes, and ultimately feeding this data to a naïve-Bayes classifier, and hence training the same to work autonomously to classify textual data depending upon the sentiments indicated by them, i.e. whether they imply negative aspects over a certain matter/topic or positive. As a result of this experiment, we were able to successfully classify all the data taken by us with a cumulative accuracy of 99.7% upon the test data set.
Remote Spoken Arabic Digits Recognition Using CNN

While Arabic is the fifth most spoken language in the world, it still lacks powerful speech recognition systems. Moreover, well-resourced languages have benefited greatly from recent developments in artificial intelligence and In deep learning, while the development of Arabic speech recognizers is still modest. In this paper, we contribute to bridging the gap between the resources of Arabic and those of the well-resourced languages, in particular in the context of remote communications. Since the unavailability of dedicated corpora is the most problematic that prevents the development of Arabic products, we built a corpus composed of the ten Arabic digits. The corpus was collected remotely, over-connected smartphone apps, and social media. A convolutional neural network algorithm is then trained to recognize the digits. Initial experiments faced an overfitting problem due to insufficient data. To overcome this problem, we artificially expanded the training dataset with audio data augmentation techniques. In further experiments, we explored cross-corpora speech recognition. The final model achieved a recognition rate of 95.94% on training subset and 77.30% on test subset. The results showed the potential of the proposed approach in remote speech recognition conditions.
A Comprehensive Analysis of Chaos-Based Secure Systems

Chaos is a deterministic phenomenon that emerges under certain conditions in a nonlinear dynamic system when the trajectories of the state variables become periodic and highly sensitive to the initial conditions. Chaotic systems are flexible, and it has been shown that communication is possible using parametric feedback control. Chaos synchronization is the basis of using chaos in communication. Chaos synchronization refers to the characteristic that the trajectories of two identical chaotic systems, each with its own unique initial conditions, converge over time. In this paper, data extraction is performed on different chaotic equations implemented as circuits. Lorenz is the base system implemented in this paper, followed by Modified Lorenz, Chua’s, L $$\ddot{u}$$ ’s, and R $$\ddot{o}$$ ssler systems. Additionally, more recent systems (e.g., SprottD Attractor) are included in the data extraction process. The robust system implementations provide an alternative to software chaos and architectures, and will further reduce the required power and area. These chaotic systems serve as alternatives for quantum era computing, which will cause synchronous and asynchronous techniques to fail. The data extracted organize different modes of chaos implementation based on the ease of their fabrication in integrated circuits. Performance metrics including power consumption, area, design load, noise, and robustness to process and temperature variant are extracted for each system to demonstrate a figure of merit. The figure of merit showcases chaos equations fitting to be implemented as a transmitter/receiver with a mode of chaotic ciphering in communication.
Robust Object Detection Based on Deep Neural Network and Saliency Features from Visible and Thermal Images

Robust and accurate object detection is crucial for autonomous automotive devices. Recently, object detection has made considerable progress with deep learning. However, object detection approaches fail in challenging illumination conditions such as fog, low light environment, and darkness. To address these challenges, in this paper, we propose a novel approach based on deep neural network and saliency features for fusing RGB visible images with Thermal images and use the complementarity of the two modalities for a robust and accurate multi-category object detection under challenging conditions. Specifically, we introduce a thermal saliency map to augment the RGB Image for improving detection in various conditions. We also propose a new fusing strategy that integrates salient features as guidance in the decision stage for improving detection accuracy. Extensive experiments are conducted on UTokyo multispectral object detection dataset [1]. Experimental results show the effectiveness of the proposed approach.
Node Security Metric: Proof of Conformity Blockchain Consensus Protocol

The arrival of the Proof of Stake (POS) consensus algorithm has solved the problem of energy waste caused by Proof of Work (POW) through replacing POW’s wasteful computing power with proof of possession of a certain amount of locked token called “Stake”. The weight of the latter will allow its holder to be more likely to be selected as “Leader” and earn a corresponding reward. However, the mechanism adopted by POW, the POS (and its derivatives) promotes the mining monopoly by the most powerful or the wealthiest members of the network without any other tangible criterion proving that these participants actively contribute to the security of the Blockchain network and respect the security recommendations (application of urgent updates for example) issued by the development team of said Blockchain. In this paper, we propose a new consensus algorithm called “Proof of Conformity -PoC-” favoring the node security aspect and offering additional reliability to the Blockchain. Proof of Conformity improvement involves the introduction of a new impact factor in the node weight calculation named “Node Security Metric” (NSM). NSM is primarily based on the Common Vulnerability Scoring System (CVSS). Furthermore, NSM computation mainly relies on the re-calculation of the weight of each network node based on the stability and security of their software and hardware environment. Our new approach will strengthen the node security index and encourage miners with up-to-date environments. Moreover, compliant, proactive and vigilant nodes will increase their chances of being selected as “Leader” and earning rewards. PoC is theoretically evaluated through simulation scenarios and the result obtained shows that our approach ensures that participating nodes deemed secure are more likely to be designated as “Leader” based on their conformity rates reflected by their NSM score.
CircWood: Laser Printed Circuit Boards and Sensors for Affordable DIY Woodworking

Due to its natural warmth, wood is frequently used to produce touchable objects such as furniture and signboards. Laser cutting machines are becoming common in personal wood processing to cut and engrave wood. In this paper, we propose a method and workflow for producing various sensors and electrical circuits for interactive devices by partially carbonizing the wood surface with a laser cutting machine. Similar to wiring on a conventional printed circuit board (PCB), the carbonized part functions as a conductive electrical path. A method for creating electronic circuits and sensors made of carbon graphene on botanical materials has been proposed. This technique makes use of a raster-scanning femtosecond (fs) laser, which is less common for personal fabrication than a constant-wave (CW) laser. Moreover, raster-scanning requires a substantial amount of time to create a circuit that is mainly made of conductive lines. This paper extends the method with a defocused vector-scanning CW laser beam and reduces the time and cost required for fabrication. The proposed method uses an affordable CW laser cutter to fabricate an electrical circuit, including touch sensors, damage sensors, and load sensors on wood boards. The circuit can be easily connected to traditional PCBs and electric parts such as one-board microcomputers using metal screws and nails typically used in DIY woodworking. We develop ease of use software design tool that supports the creation and fabrication of carbon paths. In addition, we report on a series of investigations, including optimizing wood materials and laser parameters to establish design guidelines.
Decentralised Autonomous Organisations (DAOs) as Data Trusts: A General-purpose Data Governance Framework for Decentralised Data Ownership, Storage, and Utilisation

This piece investigates if and how principles of data trusts might apply to “Decentralised Autonomous Organisations” (DAOs) as a data governance structure for digital data trusts. In data trusts, a legal entity is appointed as the trustee to act on behalf of the contributors and beneficiaries of data as an independent fiduciary steward of data. The intent of Decentralised Autonomous Organisations is to enable people to coordinate and govern themselves, mediated by self-executing rules encoded in software infrastructure on a public blockchain. Rather than appointing a legal entity to negotiate and enforce rules through legal mechanisms, rules in decentralised blockchain-based systems are inscribed in software code where they are transparent, verifiable, and enforceable, and digital tokens are used to align incentives among diverse stakeholders in the system. If DAOs were data trusts, then the DAO would have the fiduciary duty for data governance on behalf of the interests of participants. To explore this idea, I outline the fundamentals of trusts, the concept of data trusts as a data governance framework, and the idea of DAOs as data trusts to steward and utilise data on behalf of participants. I then explore possible use cases and early examples of this governance infrastructure in practice in blockchain communities. I find that DAOs are a promising digital governance infrastructure for people to operationalise the principles of data trusts where the promise of data stewardship is entrusted to software code and self-governance, rather than third-party legal representatives. This contribution lends itself to further scholarly research and industry practice to test the concept of DAOs as data trusts as a data governance model for greater individual autonomy, verifiability, and benefit over one’s data and how it is used.
Comparison Between K-Nearest Neighbor (KNN) and Decision Tree (DT) Classifier for Glandular Components

Prostate cancer is one of the most common cancers in men, and the cases of this disease is increasing. Histopathological examination of prostate cancer is one of the main procedures for prostate cancer detection. The structural changes of the cytoplasm, stroma, lumen and nucleus in the glandular tissue will indicate the presence of cancerous or non-cancerous areas in the histopathology of prostate cancer. Therefore, a framework was developed to automatically segment and classify glandular tissue into cytoplasm, stroma, lumen, and nucleus, which can reduce the complexity of prostate cancer detection. The images underwent image enhancement using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). Then, in segmentation phase, K-means clustering (KMC) and multi-level thresholding (MT) methods were implemented to segment the enhanced image into cytoplasm and stroma, lumen and nuclei regions. A total of 8 feature vectors are extracted from each segmented image. All these features were introduced into the classification system namely K nearest neighbor (KNN) and decision tree (DT). The overall results showed that the performance of KNN is better than DT with an accuracy of 86.67%, sensitivity and specificity are both 100% (the features of the KMC category). With the features of MT category, KNN achieved 84.44% in term of accuracy, 100% sensitivity and 96.67% specificity. Here, it can also be concluded that the features of the KMC category are more suitable for the classifiers. In addition, leave-one-out cross-validation has been implemented, which can improve the performance of the two classifiers.
Efficient IaC-Based Resource Allocation for Virtualized Cloud Platforms

Cloud computing has become an inevitable part of information technology (IT) and other non-IT businesses. Every computational facility is now provided as computing services by the cloud service providers (CSPs). While providing these services, CSPs try to maintain efficiency by keeping the performance index as high as possible. Although virtualization technology has made this possible by applying resource provisioning techniques, but this approach is still hectic and expertise-dependent. In this paper, we propose an efficient infrastructure as code (IaC) based novel framework for optimizing resource utilization percentage through an automatic provisioning approach. This framework maximizes the resource utilization and performance metrics of virtualized cloud platforms. In this context, we have presented some mathematical formulations and considering those, we addressed our designed programming model for the proposed IaC-based framework. Extensive simulations have been performed to establish the novelty of the proposed approach. We have also presented a comparative study by considering two data centers, one with IaC based proposed model and the other is a conventional contemporary model. Result analysis confirms the performance of our proposed IaC-based framework.
Publication Activities of Pedagogical Universities: Quantitative and Qualitative Indicators

The article aims to study the effectiveness of teachers’ research activities at pedagogical universities. This indicator is measured by scientific publications that promote scientific research results and the dissemination of scientific flows that affect productivity and effectiveness of scientific activities at universities. The main methods of research are the quantitative analysis of the characteristics of teachers’ publication activity at pedagogical universities, the analysis of bibliometric data and their classification, statistical and comparative analysis. Within the framework of the study, a literature review is presented. The article highlights the results of the of the teachers’ publication activity analysis over the past five years and trends in the development of publication activity. The authors have also provided an analysis of the quality and the level of journals in which authors affiliated with pedagogical universities are published. In addition, the shortage of peer-reviewed journals for educational research publication is revealed. The conclusions are made about the specifics of promoting research of pedagogical universities: orientation of university teachers to publish research results in peer-reviewed journals; formation of collaborations with the leading Russian and foreign scientists or research groups for joint research. The authors also propose measures to eliminate difficulties in the teachers’ publication activities: training in the basics of legal literacy for intellectual activity results protection and promotion, as well as the formation of a support system for scientific and pedagogical staff for publishing research results in peer-reviewed journals.
A feature attention dehazing network based on U-net and dense connection

The image acquisition equipment influenced by the dense fog, dust, and other weather factors produced low-contrast and low-quality images, which are unfriendly to smart city applications. Various algorithms, models, and networks have been proposed to achieve the dehazing work, all of which have behaved well to the uniform noise. However, in the real scene, due to the nonhomogeneous distribution of the fog, the performances of the dehazing method still need to be improved. In this paper, we proposed a Feature Attention Dehazing Network based on U-Net and Dense Connection, which is FAD-U-Net, to minimize the influence of the nonhomogeneous haze. The FAD-U-Net employed the U-Net as the base structure used the feature attention model to fit the contracting path of U-Net; in addition, the dense connection technique has been added between each block on both contracting path and expansive path, which aims to enhance the feature information. Finally, a group of 18 residual blocks has been added to the networks to improve the image quality. Experiment results show that the proposed dehazing network outperforms most of the existing dehazing methods under nonhomogeneous fogging environments. Regarding the four state-of-art methods we compared in this paper, the PSNR value is increased by 3.21 on average, and SSIM is increased by 0.14. The FAD-U-Net is suitable for most of the application scene in the smart city.
The Impact of Student-Paced Pragmatics Instruction through Nearpod on EFL Learners’ Request Performance

Although the use of technology for pragmatics instruction has attracted significant attention from scholars, the number of studies regarding the impact of self-access materials to this end is limited. Nearpod is a useful cloud-based application to support self-paced learning. This paper aims to probe the effect of learning through Nearpod on EFL learners’ request performance. Researchers designed seven self-access courses to teach requests. The data was collected from 11 EFL learners through Online Oral Discourse Completion Tasks before and after the treatment. The post-test results revealed that the participants significantly improved their request performance after the treatment. The learners’ responses in the pre-test and the post-test were also qualitatively analyzed for an in-depth investigation of the changes in their requesting behavior. The findings revealed that the learners stopped modality generalization and started using a variety of modal verbs according to the context of situation after the treatment. Additionally, it was indicated that self-paced pragmatics instruction helped learners overcome the pragmatic failures regarding the main verb use in request head acts. Furthermore, the differences regarding the learners’ use of internal and external modifiers were also documented. After the treatment, they performed various internal modifiers that were either absent or uncommon in the pre-test data. Alerters and grounders were found to be the most frequent external modifiers in both the pre-test and the post-test data set. In summary, the results indicated that self-paced learning helped learners improve strategies for performing appropriate requests.
Efficient Analysis of Transactional Data Using Graph Convolutional Networks

We show that by transforming financial records into graph structures, Graph-based Deep Learning methods can greatly outperform their conventional Neural Network counterparts by assimilating data latent in the graph structure itself: topology and neighbourhoods. Our resulting approach allows for competitive classification of fraudulent users, even when resampling the data to reinstate the authentic imbalance. When it comes to research and analysis, in the pursuit of safeguarding financial systems, the problem is that transactional datasets are noisy and unstructured, especially with the goal of discerning illicit activity. In this research we demonstrate the powerful capabilities of semi-supervised Graph Convolutional Networks in mining insights out of these large datasets, despite strong imbalances and only partial labelling. Fraud and financial malpractice has always been, and likely will remain ubiquitous. The problems are amplified in the new world of cryptoeconomic financial systems - which are young and unknown, lack regulation and can be confusing for new users. The result is a lucrative playground for scammers, phishers and hackers. However, in this research we have built and describe an effective countermeasure to take on these adversarial actors.