Reviewerly/exHarmony huggingface dataset:
Reasoning with Rules and Rights: Term-Modal Deontic Logic

General obligations such as ‘every driver has to give way to a driver coming from the right’ are central in legal reasoning, but have been mostly overlooked in deontic logic. We claim that a simple extension of Standard Deontic Logic to the predicative level is insufficient to capture general obligations. Instead, we argue for an explicit representation of bearers (and counterparties) of obligations as terms in a quantified deontic logic. To achieve this we develop a term-modal counterpart of Standard Deontic Logic and give a sound and strongly complete axiomatization for it. We go on to show that this logic is not only suitable for capturing reasoning with general obligations, but also with (multital and paucital) Hohfeldian rights relations and rules of rights.
Performance Analysis of Virtual Communication Between Thermal Cameras and Contact-tracing Applications for COVID-19

Late 2019 and a significant part of early 2020 have witnessed the outbreak of Coronavirus disease-19 (COVID-19) across the world. As a desperate attempt to control the virus spread, many countries are enforcing measures to restrict large concentration of people at one place and are implementing some form of contact tracing mobile application to quickly track close interactions between people. Furthermore, as businesses come out of lockdowns, they are required to record the temperature of all visitors and staffs that move in and out of their premises. Some businesses are employing medical grade contactless thermal imaging cameras for scanning temperature. Communication and sharing of information between the contact tracing application and thermal cameras would make an effective tool against COVID-19. However, there is a disconnect between the contact tracing applications and contactless thermal imaging solutions because they employ different communication stacks, platforms, data formats, and protocols. Furthermore, any kind of middleware to mediate between the cameras and the mobile applications would render the solution useless because of the induced latencies. In this paper, we are proposing to virtualize the communication between the cameras and mobile applications so that they could communicate and interoperate over a common protocol stack. We further model and simulate the proposed virtualized communication algorithm, under various topologies and configurations to comprehensively evaluate the performance, scalability, and deployment feasibility. The simulation aptly and efficiently evaluates the results for latency, energy, and bandwidth consumption parameters.
Movie Recommendation and Mood Prediction System—Leisures

A recommendation system is a machine learning model to help the users to find items of their choice and prevent themselves from data overloading. It predicts the choices of a particular user and makes the recommendation as per the user’s choice and requirements. The recommendation systems might adopt different techniques based on personal rating and interest and even sometimes based on the wish list of some other user having the same interest. So in order to make one’s leisure good, we present our movie recommendation system and mood predictor, Leisures. Leisures is a platform where one can find everything they want to do in their free time with a built-in suggestion module. So, it analyzes what mood an individual is in and makes suggestions on what he/she should do in order to make their free time/leisure good. For example, if someone is gloomy and he opens Leisures, it will first analyze the person’s temper, and after figuring out his distress, it would suggest to either listen to sad songs or watch movies and will fetch respective data for it as well. The user can then watch movies as per his mood, and when he returns again, he can log in to his account, and he can either choose from recommended movies as per his last choice or may use the mood prediction module again. The introduction of the chatbot was the latest and advanced idea to make recommendations for the user. Moreover, apart from this newest mood predictor chatbot, the recommendation system works efficiently as well, a content-based recommendation model is being used in the project (Raval and Khedkar in Int J Sci Tech Res 8(12) 2019 [1])
ADJSCC-l: SNR-Adaptive JSCC Networks for Multi-Layer Wireless Image Transmission

Motivated by deep learning in wireless image transmissions, joint source-channel coding (JSCC) techniques based on convolutional neural networks (CNNs) can successfully improve the image quality in noisy channels compared with conventional digital techniques. In urgent or limited scenarios like the surveillance communication, image information with a low compression rate is sent at the first transmission stage to save the channel rescources. If the quality of the reconstruction is of low-quality, the supplementary information can be transmitted at the second stage to improve the quality of the reconstruction. In this paper, we propose an attention mechanism based multi-layer JSCC architecture for the progressive image transmission, called ADJSCC-l. In the proposed architecture, the first layer can recover the base information with the highest compression rate in the first transmission, while the refinement layers can utilize the refinement information in successive transmissions to obtain better image quality.The provided numerical results show that the ADJSCC-l offers a bi-mode transmission which can significantly enhance the validity and reliability of the data in wireless image transmission compared with other neural network (NN) based JSCC schemes.
Rainbow-RND: a Value-based Algorithm Augmented with Intrinsic Curiosity

Deep Reinforcement Learning (DRL) is, without a doubt, one of the most promising and exciting research area in Artificial Intelligence (AI). Several approaches have been proposed and improved in a short time to solve different problems. To tackle exploration&#x2019;s issues, we present in our work a new approach based on curiosity to generate intrinsic rewards. These latter are related to complex environments with sparse rewards, such as the notorious Montezuma&#x2019;s Revenge and an even more recent and complicated environment named Obstacle Tower. This type of environments requires the agent to generalize its knowledge, learn high-level planning and low-level control. The results of our experimentations showed that a value-based algorithm (such as Rainbow) can be successfully used with a curiosity-based exploration approach (such as random network distillation). This combination effectively performs better in the Obstacle Tower environment.
Out of Non-linearity: Search Impossible Differentials by the Bitwise Characteristic Matrix

In this paper, we propose the \(\mathcal {M}\)-method which uses the bitwise characteristic matrix to search impossible differentials. \(\mathcal {M}\)-method exploits not only the linear components but also partial information of non-linear components. According to the principle of miss-in-the-middle, we construct two different types of contradiction to search the impossible differentials with limited time and memory complexity by calculating \( \mathcal {M}_{en}^{r_1} \) and \( \mathcal {M}_{de}^{r_2} \) which represent \( r_1 \) rounds encryption and \( r_2 \) rounds decryption, respectively. Compared with the previous methods, our technique is comprehensible and fast especially for large block size.As a result, we find the 7-round impossible differentials of GIFT-128, the 5-round impossible differentials of PRIDE, and the 4-round impossible differentials of Pyjamask-96. For GIFT-64, PRESENT, RECTANGLE which are well-analyzed by MILP-method or SAT-method, we construct new impossible differentials. Moreover, the efficiency of our method will not be influenced by the block size, which makes us find the new 5-round impossible differentials of the 320-bit permutation of ASCON.
Application Portfolio Profiling and Appraisal as Part of Enterprise Adoption of Cloud Computing

Computation resources have never been more affordable, powerful, or readily available than they are now, thanks to the Internet and the rapid advancement of processing and storage technologies. In response to this new trend in technological development, a brand-new computer paradigm is known as "cloud computing" has emerged. Resources are made available to end-users as general utilities that can be rented and released on-demand over the Internet. More and more corporate processes are being moved to the cloud as firms experience using the technology. Cloud adoption is substantially more complicated than we first thought when it comes down to data management, system integration, and managing various cloud providers. As cloud migration has grown more common, a number of have been plagued by failed application modernization initiatives or migration. However, many firms worldwide are finding that cloud computing is more expensive and more difficult to implement than they expected. Is cloud computing still up to the task in this scenario? Failures are largely due to a lack of planning in the early stages of identifying which legacy applications are best suited for private or public cloud deployment, resulting in cost and time overruns. Application portfolios need deliberate decision-making to reduce the risk of overruns and delays in migration. Consequently, a rough estimate of cloud operating costs (ROM) is required for inclusion in the planning phase of IT governance. ROM cloud run costs must first be gathered through an automated discovery process, followed by quantitative analysis. Accordingly, to assist in making an informed decision about whether or not to migrate legacy applications, we propose two methods: (1) gathering initial data through Application Portfolio Profiling (APP), and (2) performing Application Portfolio Assessment (APA). An Enterprise Cloud Computing Considerations Framework (CCCC) framework is used to help a corporation decide which legacy applications should be moved as part of the adoption process for Cloud Computing. In light of the shift in business models, infrastructure requirements, and other issues that arise when cloud computing is implemented, this decision is crucial.
A New Pupil Detection Algorithm Based on Multiple Angular Integral Projection Functions

In this paper a new pupil detection algorithm based on multiple angular integral projection functions is presented. The algorithm is based on binarizing the eye pupil image captured with an infrared video camera, using the dark pupil technique. To better detect the features points that belong to the pupil, the proposed algorithm uses the angular integral projection function, applied along six directions. The paper analyzes the cases when the six directions are evenly distributed around the center of the pupil in fixed positions, as well as when they are rotated with a random offset. These cases are analyzed and discussed in the paper, presenting the results obtained on three representative databases of eye images.
NSMuseum – A Case Study for Developing Cross-platform Mobile Applications in Augmented Reality

This paper presents a method to streamline the process of developing a mobile application in augmented reality, in order to maximize exposure to a wider audience.In addition, a comparative analysis will be developed between the bestknown ways to develop cross-platform applications in augmented reality, ARFoundation and Vuforia.The process described in this paper aims to add value to such an application, integrating Unity with another cross-platform technology, Flutter.As an example of this development process, a cross-platform application was created that offers an appearance as close as possible to the native one and an experience in augmented reality as pleasant as possible, being accompanied by a modern user interface.In addition to this, the example also focused on the integration of augmented reality in the museum visiting experience, enhancing the educational process.Our cross-platform approach has allowed the creation of a single application running on both iOS and Android operating systems, so both the cost, development time and resources have been reduced.
Armstrong and the FM Revolution

This chapter unravels Howard Armstrong's creation of wide-band frequency modulation — FM. It follows a trail of research from hundreds of thousands of tests and measurements, tens of thousands of hours of work for Armstrong and his assistants, to ultimate triumph. Like most revolutions, Armstrong's FM had been a long time in the making. The chapter argues that Armstrong began exploring the nature of frequency modulated waves in as thorough a way as he had the operation of the vacuum tube and the phenomenon of static. The chapter then tracks Armstrong's fundamental changes in the radio receiver. The chapter investigates the challenges and hearings faced by Armstrong. It became clear to all in radio that new portions of the spectrum were needed for television, police, and government communications as well as FM. The chapter then recounts how the introduction of FM began to move more swiftly.
Residual Networks for Image Clustering

Clustering is an unsupervised learning process which groups objects into clusters using a similarity measure, like Euclidean distance. It basically classifies data into structures that are more easily understood and manipulated. Clustering has many utilities in real time applications as it helps to discover hidden trends and relationships inside data. In this work, we propose an approach for Image Clustering which uses Residual Networks (ResNets). Firstly, a Residual network performs image classification. Then, a softmax function is applied to obtain the decimal probabilities of all the classes, which are then used for clustering. The proposed method is extremely fast and more accurate than the K-means clustering algorithm. Experimental results show that this approach achieves 98.53% clustering accuracy on MNIST, 94.81% on Intel Image dataset and 96.94% on a self-created custom dataset.
A right to read object code – software copyright law and the development of a decompilation exception in South Africa

The opportunity to read and extract knowledge from a lawfully obtained copy of any published work is a cornerstone of copyright law, except in the case of computer programs. This article examines the development of an exception to copyright protection that will permit decompilation of object code as a means to access its underlying ideas. It illustrates that the persistent link between software and literary works leads to an unduly narrow view of decompilation. This contention is supported by a basic technical illustration of the decompilation process to support the submission that reading object code should not amount to copyright infringement in any way. Thereafter, the proposed decompilation exception in South African copyright law is critically analysed in light of the developmental objectives expressed in national policy, to formulate an alternative approach based on the inherent flexibilities in international law and local conditions.
Point2Token: A Multi-Tagging Answer Retrieval Framework for Question Answering

Question answering plays a crucial role in the chatbot systems, in which it retrieves the answer from the given context and return the predicted span as a result to users. Previous work mostly modelled this task as a multi-classification problem. However, the models cannot gain a promising result due to the scarcity of the probability distribution over the whole given context. In this paper, we propose a novel approach to solve the problem mentioned above. We model the question answering task as a multiple binary classification problem and introduce PointerNet in our model decoder to predict whether it belongs to a start or end position in each token within context. The experimental results on a well-studied dataset show that our model outperforms the baseline models, which proves our model effectiveness.
RLAC: Random Line Approximation Clustering

We explore how Random Projections can be used as an Approximate method for Projection Pursuit Clustering in high dimensional data. Traditional data transformations such as PCA for dimensionality reduction have been shown to be beneficial in clustering. However, their objective is not always relevant to the cluster structure producing undesirable results. On the other hand, Projection Pursuit methods present promising results in finding different "interesting" directions while being easily modified, though they came with high computational costs. In an attempt to provide a lightweight and simplified approach for Projection Pursuit clustering, we designed and implemented the Random Line Approximation Clustering (RLAC), a hierarchical divisive clustering algorithm that incorporates attributes from the Random Projection method.
Machine Learning Methods for Predicting The Necessity of Caesareans Section of Childbirth

In the process of delivery usually the baby comes out of the vagina but under some circumstances a cesarean section is performed. Caesarean section, on the one hand can have short-term and long-term effects for the mother but on the other hand it is a life-saving procedure for both mother and child. The purpose of this study is to predict whether or not a caesarean section is necessary for a pregnant mother using a machine learning classifier model and some features from medical records as input data. In this study, some machine models were explored including Logistic Regression, K-Nearest Neighbor, Random Forest, Decision Tree, Gradient Boosting and Support Vector Machine. The experiment found that Logistic Regression model achieved the highest performance as measured by average accuracy 95%, average precision 86%, average recall 95%, and average F1 86%.
Identifying Correlation of Coal Seams in the Tien Hai Area, Northern Vietnam by Using Multivariate Statistic Methods

In northern Vietnam, the Tien Hai area is considered a high potential area of coal deposits. Twohundred fifty-six geochemical coal samples of 13 cores in the Tien Hai area investigate coal seams andcoal deposits to identify the correlation of coal seams. According to the statistical method and clusteranalysis of geochemical samples, the results indicate that the Mg, V, As, Ca, Zn, Cr, Co, K, Na, Sr, Fe,Ge, Re, U, Mo, Th, and Ga elements are good indicator elements of the major and trace elements in coal.Most of them comply with the normal or lognormal distribution rules. Besides, the Yb, Sc, Ho, Er, Tm,Lu, Y, Tb, Pr, Dy, and Sm elements are also good indicator elements for rare earth elements in the region.Therefore, the selected elements are used to identify the correlation of the coal seams in the Tien Hai area.Based on the similarity degree between studied objects, the results of grouping boreholes in coal seamsshow that the correlation of coal seam TV2-11 is suitable and acceptable, the coal seams TV3-6a, TV3-6b, and TV3-6c can be grouped into the coal seam TV3-6. These results present that the models can helpstudy geochemical coal samples and identify the correlation of the coal seams in the Tien Hai area.Additionally, the statistical analysis shows a remarkable degree to determine the correlation of the coalseams. Geochemical coal data can help to evaluate the indicator elements of the major, trace elements,and rare earth elements in coal seams and coal rashing of adjoining and pillar rocks in the Tien Hai area,northern Vietnam.
Investigation of an Input Sequence on Thai Neural Sequence-to-Sequence Speech Synthesis

This work aims to apply neural sequence-to-sequence speech synthesis to Thai TTS. Firstly, the most important contribution is on the investigation of the most appropriate unit of an input sequence for the neural sequence-to-sequence Thai TTS. We found that the system with phoneme input was superior to the system with character input. Secondly, We explored the benefits of the word and/or syllable boundaries information in both character-based and phoneme-based. We found that word delimiting can improve the naturalness of the synthesized speech. This means word segmentation is considered a necessary step for text processing tasks in neural sequence-to-sequence Thai TTS. Finally, we explored our TTS system in the real setting. Our results indicated that the neural TTS using ground truth or predicted input features for phoneme-based can generate a high-quality synthesized speech at the same level.
Cross-lingual textual entailment using deep learning approach

Natural Language processing is dealing with natural language understandings and natural language generation which enable computers to understand and analyze human language. Cross-lingual Textual Entailment (CLTE) is one of the applications of NLU if there exists premise (P) as a source language and hypothesis (H) as a target language. CLTE is challenging for transferring information between under resource (Amharic) language and high resource (English) language. To solve this problem, we have proposed Cross-lingual Textual Entailment model using deep neural network approaches. We have used Bi-LSTM to transfer sequential information, XLNet for handling a position of word and its boundary, MLP for classification and prediction outputs, and FastText to word representations. Neural machine translation is utilized for translating English sentences into Amharic sentences with IBM5 alignment. We have combined Amharic dataset with SNLI dataset and annotated based on multi-way classification. The NMT predicts 96.01% of the testing accuracy. We have obtained 89.92% training and 86.89% testing accuracy for the proposed model. The issue with this research is that it ignores multiple inferences.
Learning Multi-scale Underexposure Image Correction

Images captured in low light conditions usually suffer from color distortion and poor visibility. Although remarkable success has been made, existing methods are still unstable when applied to various scenes. Therefore, we propose a robust deep multi-scale network for underexposure image correction. We construct Gaussian pyramid to explore the complementary and redundant information in different scales, and further extract, fuse illuminations with progressive fusion strategy across scales. This process not only boosts the end-to-end training but also promotes the cooperative representation. Moreover, fine-fused illuminations in multi-scales bridge the gap between the restoration knowledge of underexposure images and the perceptual quality preference to normal light images. Extensive experiments with advanced methods and ablation studies demonstrate our method outperforms others under a variety of metrics in terms of qualitative and quantitative comparison.