Reviewerly/exHarmony huggingface dataset:
F2MH Cryptosystem: Preliminary analysis of an original attempt to revive Knapsack-based public-key encryption schemes

Public-key cryptography is an ubiquitous building-block of modern telecommunication technology. Among the most historically important types, the knapsack-based encryption schemes, from the early years of public-key cryptography, performed particularly well in computational resources (time and memory), and mathematical and algorithmic simplicity. Although their widespread adoption was readily curtailed by effective cryptanalyses to several different attempts, the question of whether or not there is any future for actual usage of knapsack-based asymmetric encryption schemes, and all its potential advantages remains unsettled. The goal of this paper is to present a novel construction, which offer consistent security improvements on knapsack-based cryptography. We propose two improvements upon the original knapsack cryptosystem that address the most important types of attacks: the Diaphantine approximations-based attacks and the lattice problems oracle attacks. The proposed defenses demonstrably preclude the aforementioned types of attacks.
Artificial intelligence as producer and consumer of copyright works: evaluating the consequences of algorithmic creativity

Artificial intelligence (AI) has been the subject of a great deal of academic interest in recent times; it has captured the attention of copyright lawyers fascinated by the thought of machines creating works of art, music and literature. There is no doubt that, as has often happened in the past during previous waves of technological advances, AI platforms—and especially, machine learning—have brought with them new opportunities as well as challenges. Machine learning is an AI application enabling programs to learn and progress automatically from experience. Its main feature is accessing data and often using it for the purpose of creating outputs, including music, literature, movies and art. Amounts of data are observed and analysed by the machine, which enables the latter to learn and then make creative decisions leading to final outputs that, as precise works of art, are often not foreseeable by the people who developed and started the initial program. Such a process is characterised by the absence of substantial human intervention or assistance after the program is operated, and by the use of algorithms—namely a sequence of instructions aimed at solving a problem or performing a computation. It can be deemed “algorithmic creativity”, or the way by which AI/machines create new works.Against this background, the article delves into thorny legal and policy issues. Is an AI-created work protected by copyright? Should it be? Who would be viewed as the author? Who should own such a work? These questions are the focus of the first part of this article: “AI as producer”. In the second part, “AI as consumer”, our analysis shifts to the questions of whether and to what extent the use of data fed into the system, for example to train the algorithms, may amount to copyright infringement, or may in certain circumstances be exempted under fair use, fair dealing or similar doctrines. In the third part we consider possible legal regimes for dealing with machine produced works including alternatives to copyright, such as a public domain scenario and a sui generis system. Finally, we provide our conclusions.
Object-based classification for Visual Question Answering

Visual Question Answering (VQA) problem is expected to provide an accurate answer in natural language for a given question about the image in natural language. This paper focuses on the visual question answering problem using deep learning based architecture. The proposed model is based on two popular neural network architectures: Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM). We have used CNN for encoding the given image and word embeddings to encode the questions and the feature extraction. Moreover, we have used LSTM for the question understanding. Finally, the results are compared with the Multi-Layer Perceptron (MLP) and Stacked Attention Network (SAN) models. The effectiveness of the proposed model have also been compared.
Incomplete angle reconstruction algorithm with the sparse optimization and the image optimal criterions

To solve the problem of artifact and image degradation caused by incomplete angle projection, this article presents an incomplete angle reconstruction algorithm based on sparse optimization and image optimization criterion (SO-IOC). Firstly, the joint objective function model is established based on the projection sparsity and the natural features of images. Secondly, by means of the idea of alternating direction method of multipliers, the augmented Lagrange method is used to decompose the reconstruction model into simple subproblems and the modified genetic algorithm is used for solving those subproblems. Finally, a multiobjective optimization operation is carried out to coordinate and select the candidate solutions to improve the quality of the reconstructed images. The algebraic reconstruction technique algorithm and the Split Bregman algorithm are compared with the SO-IOC algorithm. In the compared process, the mean relative error and the peak signal-to-noise ratio are used. The experimental results show the SO-IOC algorithm is best among the above three algorithms.
Extension of Naturalized Flow Using Linear Regression

Naturalized flow, which is streamflow representing natural hydrology, is one of the hydrologic input datasets for Texas’ water availability models (WAMs) that are used for the water rights permitting process, regional and statewide water planning, and other water management activities in Texas. The existing naturalized flow input for most of these models covers the period extending from the 1940s to the 1990s. Given that some river basins in Texas have, in recent years, experienced record low streamflow associated with the 2010—2015 drought event, having updated naturalized flows is essential for reliable estimates of water availability from surface water sources. We find that for some river basins, or some part of a river basin, naturalized flows have a close correlation with observed flows. Furthermore, monthly naturalized flow in upstream watersheds and downstream watersheds, and vice versa, are also correlated, since streamflow under natural condition typically increases gradually downstream. Adjacent watersheds may also have similar flow characteristics. Therefore, it is relatively easy and fast to extend the WAM’s naturalized flow input using a regression method. We introduce a simple and convenient methodology for updating naturalized flows using linear regression between historical gauged flow and existing naturalized flow, between upstream and downstream naturalized flow, and between naturalized flow in adjacent watersheds. Naturalized flows updated using this methodology can be used as an auxiliary dataset for water planning purposes until the Texas Commission on Environmental Quality (TCEQ) releases official updated naturalized flow datasets for the state. We present an evaluation of the extended naturalized flows for the Canadian River Basin WAM and Sulphur River Basin WAM where the datasets used to develop the regression equations are highly correlated (correlation coefficient, R>0.93 and standard error <0.12). A comparison of extended naturalized flows for the Sulphur River Basin WAM derived using our methodology with the recently-released official update to the Sulphur River Basin WAM indicates that the average monthly mean flow is only 0.3% higher than the official average at a selected control point (i.e., model node). Simulated firm yield and overall water right reliability are within 2% of the same metrics derived using the official dataset. This result provides a validation of our dataset and justification for using such extended naturalized flows as auxiliary datasets for research and surface water resources planning in Texas.
Fully Automatic Computer-Aided Detection of Breast Cancer based on Genetic Algorithm Optimization

Breast cancer is the second disease causing death in women. The screening of breast cancer for early detection is an effective way to reduce mortality. Generally, mammography is the most effective imaging modality recommended by radiologists. Therefore, a high number of mammogram images have to be analyzed. Indeed, the expertise of radiologists and the quality of the images to be analyzed play a crucial role in the accuracy of the diagnosis. The use of CAD systems allow improving the accuracy of diagnosis by reducing the number of false-positive and the false-negative. In this paper, to assist radiologists to make the right decision by showing them the probably suspect area, a full automatic CADe is presented. The preprocessing step aims at delimiting the ROI by removing artifact and pectoral muscle using global thresholding method, morphological operators and seeded region growing method, followed by contrast enhancement of the ROI found in the first step using the technique named “adaptive local gray level transformation based on variable s-curve”. At last, the ROI is segmented using k-means clustering. Both contrast enhancement method and the segmentation method are performed by genetic algorithm to optimize the outcomes of each step.
Munich to Dubai: How far is it for Semantic Segmentationƒ

Cities having hot weather conditions results in geometrical distortion, thereby adversely affecting the performance of semantic segmentation model In this work, we study the problem of semantic segmentation model in adapting to such hot climate cities. This issue can be circumvented by collecting and annotating images in such weather conditions and training segmentation models on those images. But the task of semantically annotating images for every environment is painstaking and expensive. Hence, we propose a framework that improves the performance of semantic segmentation models without explicitly creating an annotated dataset for such adverse weather variations. Our framework consists of two parts, a restoration network to remove the geometrical distortions caused by hot weather and an adaptive segmentation network that is trained on an additional loss to adapt to the statistics of the ground-truth segmentation map. We train our framework on the Cityscapes dataset, which showed a total loU gain of 12.707 over standard segmentation models. We also observe that the segmentation results obtained by our framework gave a significant improvement for small classes such as poles, person, and rider, which are essential and valuable for autonomous navigation based applications.
Improved random adaptive grouping approach for solving unconstrained LSGO problems

Abstract Large-scale global optimization (LSGO) problems are emergent in many domains of applied sciences. LSGO is a hard challenge for the majority of state-of-the-art optimization methods. Evolution algorithms (EAs) combined with Cooperative Coevolution (CC) are able to perform well when solving many real-world LSGO problems. The grouping of variables at the problem decomposition stage has a significant impact on the performance of the CC approach. This study proposes an improvement of the previously developed random adaptive grouping (RAG) approach for CC. The new method is titled as RAG2, and the whole optimization algorithm, based on RAG2, is called CC-SHADE-RAG2. The influence of the choice of the population size and the number of subcomponents on the algorithm performance have been investigated using the IEEE LSGO CEC’2013 benchmark. The set of test problems in the benchmark contains fifteen functions with dimensionality equal to one thousand. We have also compared the performance of the novel algorithm with some EAs, which are applied for solving LSGO problems.
Spatial Normalization of MRI Brain Studies Using a U-Net Based Neural Network

Over recent years, Deep Learning has proven to be an excellent technology to solve problems that would otherwise be too complex. Furthermore, it has seen great success in the area of medical imaging, especially when applied to the segmentation of brain tissues. As such, this work explores a possible new approach, using Deep Learning to perform spatial normalization on Magnetic Resonance Imaging brain studies. Spatial normalization of Magnetic Resonance images by tools like FSL, or SPM can be inefficient for researches as they require too many resources to achieve good results. These resources include, for example, wasted human and computer time when executing the commands to normalize and waiting for the process to finish. This can take up to several hours just for one study. Therefore, to enable a faster and easier method to normalize the data, a U-Net based Deep Neural Network was developed using Keras and TensorFlow. This approach should free the researchers’ time for other more relevant tasks and help reach conclusions faster in their studies when trying to find patterns between the analyzed brains. The results obtained have shown potential by predicting the correct brain shape in less than 10 s per exam instead of hours even though the model did not yet accomplish a fully usable spatial normalized brain.
The use of logical-linguistic apparatus to describe the functioning of medical systems

Abstract The article describes the developed medical systems. The basis of systems is the mathematical apparatus of fuzzy logic. The advantage of logical-linguistic models is their universality. The components of the model are easily upgradeable. The article discusses examples of models related to medicine. The first model is used to recognize cough sound signals. The sound signals database includes sound signals for coughing patients of a medical institution of the pulmonology department. The developed model has been tested in a hospital, and is also used in the educational process of several higher educational institutions. To develop the second model, statistical data related to the field of eye diseases were used. Based on the collected data, a model has been developed to support adoption in the diagnosis of eye diseases.
Tableaux for essence and contingency

Abstract We offer tableaux systems for logics of essence and accident and logics of non-contingency, showing their soundness and completeness for Kripke semantics. We also show an interesting parallel between these logics based on the semantic insensitivity of the two non-normal operators by which these logics are expressed.
Horizontal Correlation Analysis of Elliptic Curve Diffie Hellman

The world is facing a new revolutionary technology transition, Internet of things (IoT). IoT systems requires secure connectivity of distributed entities, including in-field sensors. For such external devices, Side Channel Analysis poses a potential threat as it does not require complete knowledge about the crypto algorithm. In this work, we perform Horizontal Correlation Power Analysis (HCPA) which is a type of Side Channel Analysis (SCA) over the Elliptic Curve Diffie Hellman (ECDH) key exchange protocol. ChipWhisperer (CW) by NewAE Technologies is an open source toolchain which is utilized to perform the HCPA by using CW toolchain. To best of our knowledge, this is the first attempt to implemented ECDH on Artix-7 FPGA for HCPA. We compare our correlation results with the results from AES -128 bits provided by CW. Our point of attack is the Double and Add algorithm which is used to perform Scalar multiplication in ECC. We obtain a maximum correlation of 7% for the key guess using the HCPA. We also discuss about the possible cause for lower correlation and few potentials ways to improve it. In Addition to HCPA we also perform Simple Power Analysis (SPA) (visual) for ECDH, to guess the trailing zeros in the 128-bit secret key for different power traces.
Climate forecasting uses backpropagation algorithm artificial neural network model for agricultural planning in Gowa regency

Abstract Climate is defined as the average size and variability of the relevant quantities of certain variables over a period of time with a period of time from monthly to annual or millions of years. This study aims to develop climate prediction models that are used for planning agricultural cultivation activities. The method used in predicting climate is Backpropagation Artificial Neural Network technique based on rainfall data in 1975-2018 in Pallangga sub-district, 1992-2018 in Bontomarannu sub-district and 1997-2018 in Bontonompo sub-district. The results showed that the climate classification according to Oldeman in Bontomarannu sub-district was in the B3 climate type suitable for planting rice crops twice and crops once a year while Pallangga sub-districts and Bontonompo sub-districts were in C3 climate type suitable for planting one-time rice crops and crops twice in one year.
Effectiveness of neural language models for word prediction of textual mammography reports

Radiologists are required to write free paper text reports for breast screenings in order to assign cancer diagnoses in a later step. The current procedure requires considerable time and needs efficiency. In this paper, to streamline the writing process and keep up with the specific vocabulary, a word prediction tool using neural language models was developed. Consequently, challenges as different languages (English, Dutch), small data sizes and low computational power have been overcome by introducing a novel English-Dutch Radiology Language Modelling process. After defining model architectures, the process involves data preparation, bilevel hyperparameters optimization, configuration transfer and evaluation. The model is able to improve the current workflow and successfully meet the computational constraints, based on both an intrinsic and extrinsic evaluation. Given its flexibility, the model opens the door for future research involving other languages and also an extensive set of real-world applications.
Face Recognition using Modified Power Law Transform and Double Density Dual Tree DWT

Face recognition is extensively used to establish the identity of an individual for several modern applications as it is contactless and non-invasive. In this paper, we propose a method for face recognition using modified power law transform and double density dual tree discrete wavelet transform. Various typical face databases are used to test the projected method involving face detection based on viola jones algorithm. The novel concept is introduced to modify the existing power law transform to enhance quality of face region for better recognition. The Double Density Dual Tree Discrete Wavelet Transforms (DDDTDWT) is used to extract features. The Euclidian Distance (ED) is used to match the obtained features of database and test face images to compute performance parameters. It is noticed that the performance of the proposed method is better compared to existing methods.
Persistence of RDF Data into NoSQL: A Survey and a Reference Architecture

RDF is being increasingly considered in a broad range of information management scenarios. Governments, large corporations, startups, and other organizations around the world are using RDF as a data model to represent and share knowledge. However, there is still a long evolutionary track with multiple challenges for RDF reaching the scale of the most recent Big Data intensive applications (e.g., Smart Cities, Sensor Networks, eHealth, Internet of Things). In this survey, we review the usage of NoSQL databases to the storage of large RDF graphs by rehearsing the latest surveys and expanding their findings by updating proposals and bringing light to aspects such as model mapping between RDF and NoSQL, triple indexing and partitioning, graph fragmentation and data caching. Moreover, we explain how the surveyed works extended the RDF capabilities so the datasets can benefit of the characteristics of scalability, schemaless data, and better overall performance of NoSQL databases. The survey summarizes the current state of art, discusses open problems, and proposes a Reference Architecture (RA). For the best of our knowledge, this is the first survey where the focus is solely on papers that use one or more NoSQL systems for the RDF persistence.
Using IoT and Blockchain for Healthcare Enhancement

Modern technologies such as Internet of Things (IoT) and blockchain have a valuable contribution in improving healthcare services. This paper aims at achieving and democratizing healthcare services by providing healthcare-as-a-service. It was achieved by developing medical devices with sensors for healthcare. It connects the medical devices such as temperature sensor to the network of medical physicians and nurses through the cloud. This paper presented integration between IoT and Blockchain as a secured platform to mitigate nurses' shortage. Blockchain was used in the proposed operational framework to store and validate patients' records. Remarkable results were found in decreasing the gap in the number of nurses for large scale of patients. Prototypical implementation of this proposed healthcare service was presented with all technical requirements to make it applicable easily.
[DC] Quality, Presence, and Emotions in Virtual Reality Communications

This doctoral thesis looks for the identification and evaluation of the factors that allow to improve the QoE of a remote client in telepresence and virtual reality scenarios. Specifically, quality and socioemotional concepts such as social and spatial presence, empathy, and emotions of being in a completely different place, as well as communicate and interact with people who are in that place. The main goals of my research are the analysis of the methodologies to evaluate video quality and socioemotional concepts, the implementation of additional tools using ML techniques to improve the QoE, and finally, experiments in real use cases.
From Artificial Intelligence to Deep Learning in Bio-medical Applications

Since their introduction in late 80s, convolutional neural networks and auto-encoder architectures have shown to be powerful for automatic feature extraction and information simplification. Using convolution kernels from image processing in 2D and 3D spaces for the stage by stage features retrieval processes, allows the architecture to be as flexible as the designer wants, considering that this is not a lucky fact. With the recent ten years of technological progress now we can compute and train those architectures and they have faced so many challenges for applications originating the most famous CNN architectures. This chapter presents an author position related to the artificial intelligence field and machine learning/deep learning appearance in the scientific world scene describing hastily the basis for each one and later, focusing on medical applications most of the socialized on the Annual IEEE Engineering in Medicine and Biology Society conference held in Hawaii in July 2018. While addressing the medical applications from cardiovascular to cancer diagnosis, we will briefly describe the architectures and discuss some features. Finally, we will present a contribution to the deep learning by introducing a new architecture called Convolutional Laguerre-Gauss Network with a kernel based on a spiral phase function ranging from 0 to 2π and a toroidal amplitude band-pass filter, known as the Laguerre-Gauss transform.
Comparative Study of Jaya Metaheuristic to Benchmark Functions and RF Circuits

The Jaya algorithm has been recently developed. It is a simple and powerful global optimization algorithm. The main feature of Jaya algorithm vis-à-vis other known metaheuristics algorithms is that it requires only few control parameters namely the number of generations the population size, and the number of design variables. In this paper, we deal with the application of Jaya to a benchmark function and to the optimal design of RF circuits, namely low-noise amplifiers. We show that it has a better convergence than the particular swarm optimization technique PSO, the backtracking search algorithm BSA and the genetic algorithm GA.