Reviewerly/exHarmony huggingface dataset:
Substitution Permutation Network based Lightweight Ciphers with Improved Substitution Layers for Secure IoT Applications

This work aims to improve the design strategies of encryption algorithms desirable for secure IoT communication. Reduced hardware architectures for S-box modules of the lightweight ciphers specifically KLEIN, Midori and PRINCE are proposed. The proposed S-boxes are designed based on reuse of minterms technique to obtain minimum hardware in terms of logic gates. It is observed that the modified S-box contained in the substitution layer results in a decrease in the resource utilization of the overall cipher. The architectures also achieve better performance in terms of throughput and energy as compared to the other implementations of state-of-the-art ciphers. Simulation results for each cipher are determined from the PAR (Place and Route) report, and are compared fairly on the same FPGA platforms to bring uniformity in comparison.
Learning human-object interaction detection via deformable transformer

The goal of human-object interaction (HOI) detection is to localize both the human and object in a picture and recognize the interactions between them. HOIs are always scattering in the image. The traditional methods based on CNNs are unable to aggregate the information scattered in the image. Many new methods utilizing the contextual features cropped from the outputs of the CNNs, which sometimes are not effective enough. To overcome the challenge, we utilize the deformable transformer to aggregate the whole features output form the CNNs. The attention mechanism and query-based predictions are the keys. In view of the success of the methods based on graph neural networks, the attention mechanism is proved to be effective to aggregate the contextual information image-wide. The queries can extract the features of each human-object pair without mixing up the features of other instances. The deformable transformer can extract effective embeddings and the prediction heads can be fairly simple. Experimental results show that the proposed method is effective in HOI detection.
Classification of Texts Using a Vocabulary of Antonyms

In this work we try to classify 5,063 blog texts written by 68 authors using XLM Roberta Large Xnli model (from Facebook AI) and a vocabulary of Russian antonyms. We find that the zero-shot classification does not provide sufficiently good results for this task. We suggest that maybe the benchmarks need additions if a model can provide good results on the main benchmarks and not good results in other tasks.
Uncertainty Method Based on Dynamic Multi-Sensor Information Fusion in Power System

This paper proposes a multi-sensor information fusion method under uncertainty. Firstly, this paper proposes the method of relative proximity measurement for multi-sensor dynamic clustering under uncertainty. An optimal fusion mode for the same group of multi-sensor information fusion based on Bayesian estimation technology and compatibility measurement is proposed. In this paper, a new model for dynamic multi-sensor fusion system is described. The efficient fusion of information from different sources enables the system to respond promptly to the uncertain environment. The experimental results show that the model has high sensitivity and practicability, especially in the uncertain environment of intelligent system.
Carrier optimization using CAD software and biologically inspired algorithms

This paper presents a comparative analysis of the application of biologically inspired algorithms and methodologies in CAD software packages, for determining the optimal profile values in carriers with two independent variables. As an example of a biologically inspired algorithm to solve this optimization problem, the scroll algorithm was applied, while the SolidWorks software package was used as an example of CAD.
Optimization of Demand and Supply Equilibrium equation by using Salp Swarm Algorithm

The operation of demand and supply in a market is known as the market mechanism. The market will be in equilibrium at price (P), when quantity (Q) will be bought and sold. In equilibrium the quantity of a good supplied by producers equals the quantity demanded by consumers. Due to the fact that traditional deterministic methods or algorithms do not cope well to solve a large number of problems in practice; The aim of this paper is to solve approximately the demand and supply equilibrium equation which has an imperative role to describe the relation between consumers/ producers and price/quantity by means of the Salp Swarm Algorithm (SSA), inspired by the swarming behavior of salps when searching foods in deep oceans as well as the Genetic Algorithm (GA) inspired by the process of natural selection. The demand and supply equilibrium equation as an Initial Value Problem (IVP) is considered as an optimization problem, since it can almost be solved by classical mathematical tools with less precision. The effectiveness of the proposed method is tested via a simulation study between the exact results, the SSA and GA results. The comparison between these performances after many replications shows that SSA is a very powerful and can produce robust solutions on low dimensional problems with minimal error.
Learned Gradient Descent Performance in Bicubic Super-Resolution Task

In most cases, being an ill-posed problem, image restoration opts to restore a high-quality image from a low-quality one, assuming that some degradation model produced given low-quality input. A lot of restoration methods were proposed for the case when linear degradation operator and i.i.d. Gaussian likelihood is assumed. However, such methods are known not to generalize well. They show a sub-par performance on real data, for which the actual degradation model is neither linear nor even exactly known. The state-of-the-art machine learning allows for overcoming this issue and learn a restoration model to the real data. The main drawback of such approaches is overfitting since to learn an inverse mapping between the low-quality and high-quality samples, they rely entirely on data. They do not utilize limited but existing knowledge of how degradation was performed. In this paper, we study learned gradient descent based image restoration and synthesis. Both linear and non-linear known restoration problems are considered, and research on how a known degradation model may be incorporated in a learned gradient-based restoration procedure is provided. Our results demonstrate that explicit usage of the degradation model and its learned linear and non-linear approximations boost restoration quality compared to a baseline without this feature.
Some clustering-based exact distribution-free <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e7048" altimg="si501.svg"><mml:mi>k</mml:mi></mml:math>-sample tests applicable to high dimension, low sample size data

Testing homogeneity of k(≥2) multivariate distributions is a challenging problem in statistics, especially when the dimension of the data is much larger than the sample size. Most of the existing tests often perform poorly in this high dimension, low sample size (HDLSS) regime, and many of them cannot be used at all. In this article, we propose some nonparametric tests for this purpose. These tests have the distribution-free property in finite sample situations. They are based on a high dimensional clustering algorithm that makes a partition of the data to form a contingency table. Using the cell frequencies of that table, we construct the test statistics. We can develop tests based on a k-partition of the data or estimate the number of partitions from the data and construct tests based on it. Under appropriate regularity conditions, we prove the consistency of these tests in the HDLSS asymptotic regime. We also consider a multiscale approach, where the results for different number of partitions are aggregated judiciously. Extensive simulation study and analysis of some benchmark datasets illustrate the superiority of the proposed tests over some existing methods.
The Impact of Cloud Computing and Virtualization on Business

In the begin cloud computing was seen as a thing of "startup" and visionary users, but nowadays it is a reality in organizations of any sector, type and size. Investments in data centers are replaced by the consumption of IT resources from a cloud provider. There are several types of virtualization, and that essentially means the abstraction of computational resources for different purposes; one of the ways, widely used, is to create virtual machines, which simulate a complete environment with software and hardware resources for the user. Despite being a technique that has existed for a long time, the growth of cloud computing has made this concept to be widely discussed. Significant innovations in virtualization, distributed computing, high-speed communications, and IoT, have aroused more and more interest in Cloud Computing. In this sense, we will analyze and evaluate the impact of cloud computing and virtualization on organizations.
Blockchain Technology for Hospital Management: A Visualisation and Review of Research Trends

This paper aims for mapping scientific articles and visualisation on application of blockchain technology for hospital management and research trends. This article provides a wide range of research estimation approaches available for hospital management systems. The major domains for implementations can be electronic patient records management; health insurance; supply chain of medicine and drugs, telemedicine and remote monitoring of patients. This paper had identified the most influential authors as Du X and Chen, The, leading countries were China and India. The most influential organisation for research was King Saud University of Saudi Arabia. Recommendations of future research recommends to focus on technology, supply chain management of hospitals; data security etc.
Identifiable Generative models for Missing Not at Random Data Imputation

Real-world datasets often have missing values associated with complex generative processes, where the cause of the missingness may not be fully observed. This is known as missing not at random (MNAR) data. However, many imputation methods do not take into account the missingness mechanism, resulting in biased imputation values when MNAR data is present. Although there are a few methods that have considered the MNAR scenario, their model's identifiability under MNAR is generally not guaranteed. That is, model parameters can not be uniquely determined even with infinite data samples, hence the imputation results given by such models can still be biased. This issue is especially overlooked by many modern deep generative models. In this work, we fill in this gap by systematically analyzing the identifiability of generative models under MNAR. Furthermore, we propose a practical deep generative model which can provide identifiability guarantees under mild assumptions, for a wide range of MNAR mechanisms. Our method demonstrates a clear advantage for tasks on both synthetic data and multiple real-world scenarios with MNAR data.
Evaluating the Performance of Autoregressive Model for Solar Radiation Forecasting

The solar radiation data taken from 14 meteorological stations in Nigeria has been analyzed. The periodic component of the data which covered a period of 13 (mostly 1977-1989) years was removed via Fourier analysis while the residual series was subjected to autoregressive analysis. It was evident from the t-test and autocorrelation plots of the modified (i.e. without the periodic component) series that there exist significant persistence at nine stations including Sokoto, Nguru, Kano, Maiduguri, Bauchi, Yola, Minna, Ibadan, and Benin. The autocorrelation at Jos, Bida, Ikeja, Enugu and Port Harcourt were however found to be insignificant. As the sample partial autocorrelation function cuts off after lag 1, a non-seasonal autoregressive model of order 1, AR (1), was identified for stations with autocorrelation. The Q-statistic of error series suggested that the models were adequate as identified. Moreover, the exploratory plots of the model residual series showed agreement with the quantitative statistics and thus enforces the inference that the models were adequate for monthly mean daily global solar radiation forecasts at some of the study stations. It is interesting to note that all the stations within the sub-sahelian region showed significant persistence whereas all the stations in the coastal region except Benin were found with insignificant autocorrelation. Expectedly, the performance evaluation of the model gave impressive result for the stations within the sub-sahelian region but a relatively weak result for the coastal region. The result for the midland region was mixed whereas it was difficult to conclude on the Guinea savannah region with result from only one station.
Challenges and possible severe legal consequences of application users identification from CNG-Logs

Due to the increase in the use of Internet-connected devices and the limited number of available Internet Protocol Version 4 (IPv4) addresses, a system necessity that would share the same Internet Protocol (IP) address among multiple end-users emerged. These systems that will allow end-users to share the same IP address are commonly called Carrier-Grade Network (CGN) Address Translation (CGNAT). Even though the laws may vary among different countries, many of the current nations impose Internet Service Providers (ISPs) to keep internet users’ activity as logs. Usually, law enforcement is provided with the exact date, public IP address, and the public port number to detect cybercriminals. Unfortunately, though, when reverse-tracking CGN logs methodology is followed, while the precision of actor detection decreases, the false positive rate increases. Therefore, this methodology has significant issues and can cause innocent people to be treated as criminals. As a case study, this study investigated criminal cases in Turkey, where the defendants are accused of being part of a terrorist organization due to the use of a mobile chat application called ByLock. We explored incorrect usage of CGNAT records to detect users of a specific application and how it affected masses from a criminal perspective. In this paper, the dangers of such an approach will be investigated in-depth by providing an actual criminal case example from the ByLock cases. Furthermore, the paper also compares the reverse-tracking of CGNAT logs approach with the approach followed in another mobile chat application (EncroChat) in terms of cybercriminal mass investigations.
Research on recommendation algorithm based on UserAttri-PCS

Aiming at two kinds of problems: cold start and data sparsity in the traditional collaborative filtering algorithm, this paper calculates similarity of attribute features and proportion feature as two recommendation basis to solve the above problems based on the user's personal information and their preference for item attribute. UserAttri-PCS algorithm in this paper is proposed based on the weight of two parts of the recommendation, and is revised according to the user's rating habits and criteria. Experimental results verify the effectiveness of the UserAttri-PCS recommendation algorithm. Compared with the traditional user-based collaborative filtering algorithm, UserAttri-PCS can improve the recommendation performance and its accuracy.
Application of Artificial Intelligence in Digital Breast Tomosynthesis and Mammography

Medical data mining is known as methods of extracting data from human services databases to help clinicians get the best diagnosis. In this field, and among cancer diseases, breast cancer is the highest deadly disease in the world in recent years. Therefore, data mining techniques will be the largest part used in this study. To complement the previous research related to breast cancer detection, this paper proposed a model to help solve the difficulty of determining the degree of risk of the disease and obtain the best results. Aiming of reducing the costs and time used in diagnosing the disease too. The experiment used a dataset computed from a digitized fine needle aspirate (FNA) image of a breast mass dataset; diagnostic (WBCD) available in UCI machine learning repository. The model is the application of classification techniques to the breast cancer data collected, which, in turn, predicts the severity of a patient's breast cancer. In addition, this paper classifies and diagnose cancers using Deep-learning algorithms only, hybrid Machine learning algorithms and feature selection methods (applied correlative rules to find out what traits related to breast cancer severity).
EDUCATIONAL WORK IN THE DISTANCE FORM AT THE UNIVERSITY: PURPOSE, TASKS, DIRECTIONS, FORMS, PROBLEMS

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Exploration of Teaching Method of Students’ Independent Proposition

Aiming at obsolescence and deficiencies of the question bank in the current teaching of computer major courses, we propose a teaching method of student independent proposition, which can not only enrich the question bank as well as ensure the novelty of the exercises, but also effectively obtain feedback data of teaching quality based on the answers to exercises. In this paper, we first discuss the teaching significance of independent proposition and some key issues such as quality management encountered in practice. Then, we propose solutions to the problems of correctness checking and difficulty grading based on some natural language processing and deep learning technologies. Finally, to prove the effectiveness of our approach, we introduce the specific application in teaching practice, including the construction and use of the online education system based on the artificially constructed question bank.
Improved AM-LSTM for Power Transformer Error Forecasting Model

In the economic dispatch of power system, how to reasonably use the past and present of power transformer to speculate its future value has very long-term socio-economic value. Power transformer error forecasting is mainly used to predict the error in the next few hours or days. Power transformer data has obvious temporal characteristics, and the traditional RNN model is more and more applied in this field. However, the RNN model may have gradient explosion or gradient disappearance. Therefore, based on the long-term and short-term memory neural network (LSTM), an improved AM-LSTM short-term load forecasting model is presented. The model improves the activation function in LSTM unit into weighted activation function group, and adds attention mechanism to improve the prediction accuracy.
English Accent Recognition Using Deep Machine Learning

The article highlights aspects of the use of deep machine learning to recognize the accents of the English language. The software has been developed to determine the percentage of how close audio recordings are to each of 8 most common English accents. A convolutional neural network consisting of 2 convolutional layers, 1 max pooling layer, and 2 dense layers was trained across 2 epochs on a set of 5,516 audio recordings taken from the English Multi-speaker Corpus for Voice Cloning resource. The forecasting accuracy of 89.07% was achieved on the test data presented by 11 thousand MFCC matrices with a dimension of 50×87.
Parallel Multi-Branch Model with Multi-Resolution Interaction for Human Pose Estiation

Abstract In recent years, human pose estimation has become a research hotspot in the field of computer vision, and has extensive application value in many fields, such as activity recognition [1], action detection, self-drving, etc. This paper tries a novel multi-stage network structure to complete the task of human pose estimation, and focus on improving the loss of spatial accuracy caused by repeated downsampling and upsampling. The network structure contains multiple resolution subnets. After the original resolution is learned in the first stage, a low-resolution branch will be generated in each subsequent stage, and these branches can learn feature representations in parallel. Through continuous multi-scale and multi-resolution fusion, necessary information interaction between subnets of different resolutions can be carried out to obtain richer feature maps and make the predicted key point heat maps more accurate. This paper uses COCO keypoint detection dataset to obtain the relevant results of pose estimation, which verifies the effectiveness of this network.
Race Recognition Using CNN Architecture

This work is aimed at Race Recognition, which has ample amount of applications, few are: surveillance systems, video/image analysis, criminal investigations, etc. Even face recognition has same or similar applications. However, this project explicitly aims at solving the Race Recognition problem. Recently this state-of-art has advanced due to the increasing popularity and immense growth of deep learning. There are CNN architectures that have been developed in the past few years involving many layers of very deep neural networks such as GoogLeNet/Inception, VGGnet, and few more, which are suitable for object detection, image classification etc. For this work, CNN architecture is used in the pursuit of making a robust algorithm for Race Recognition. The previously built CNN architectures like LeNet, VGGNet, and AlexNet which are vastly used in the fields of computer vision, inspires this work. “Dataset of Ethnic Facial Images of Ecuadorian People” is the dataset which is used for this work. It has image data classified into four categories of races-Afro-Ecuadorians, Europeans, Indigenous, and Mestizos. Since it is comparatively a small dataset, it is augmented. An accuracy of 94% was achieved with this CNN model in the task of race recognition. Further, race recognition can be used in behavioral studies, non-verbal communication studies, forensic investigation etc.
New Harmony Search Algorithm for Mobile Robot Path Planning

How to find a reasonable and effective path for mobile robot is a key research point attracting scholars’ attention at present. To expand and deepen the research on path planning methods, a new harmony search (NHS) algorithm is devised. At first, we considered a path as a multidimensional variable composed of many key points, and then the path length and collision degree are regarded as two optimization targets. Secondly, according to the path feature, the iteration rule of improvising new harmony vector is modified to perfect the quality of generated path, and the iteration rule of renewing harmony memory is also changed to increase the chance to escape from local traps. Finally, simulation results on path planning demonstrate that the designed algorithm can get an optimal path and it has stronger global search ability and faster convergence speed compared with other harmony search algorithms.