Reviewerly/exHarmony huggingface dataset:
End-to-End Sinkhorn Autoencoder With Noise Generator

In this work, we propose a novel end-to-end Sinkhorn Autoencoder with a noise generator for efficient data collection simulation. Simulating processes that aim at collecting experimental data is crucial for multiple real-life applications, including nuclear medicine, astronomy, and high energy physics. Contemporary methods, such as Monte Carlo algorithms, provide high-fidelity results at a price of high computational cost. Multiple attempts are taken to reduce this burden, e.g. using generative approaches based on Generative Adversarial Networks or Variational Autoencoders. Although such methods are much faster, they are often unstable in training and do not allow sampling from an entire data distribution. To address these shortcomings, we introduce a novel method dubbed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">end-to-end Sinkhorn Autoencoder</i> , that leverages the Sinkhorn algorithm to explicitly align distribution of encoded real data examples and generated noise. More precisely, we extend autoencoder architecture by adding a deterministic neural network trained to map noise from a known distribution onto autoencoder latent space representing data distribution. We optimise the entire model jointly. Our method outperforms co mpeting approaches on a challenging dataset of simulation data from Zero Degree Calorimeters of ALICE experiment in LHC. as well as standard benchmarks, such as MNIST and CelebA.
Analyzing and predicting spear-phishing using machine learning methods

Phishing implies misdirecting the client by masking himself/herself as a reliable individual, to take the Critical material, for example, bank account number, credit card numbers, and so on; one of the noticeably utilized Phishing these days is spear phishing, and it is one of the effective phishing assaults given its social, mental boundaries. In this paper, we will mitigate the impact of spear phishing by utilizing the multi-layer approach. The multi-layer approach is the best method of managing the web interruption, as the intruder needs to experience shift levels. Practically all the scientists are dealing with the content of the email; however, this paper picks a novel method to counter the phishing messages by utilizing both the attachment and content of an email. We applied sentimental analysis on emails, including both content of the email and the attachment, to check whether they are spam or not using SVM classifier and Randomforest Classifier; the former showed 96 percent accuracy while, as later offers 97.66 percent accuracy. SVM showed false-positive 0 percent and false-negative 4 percent, while RandomForest showed 0 percent false-positive and 2.33 percent false-negative ratios. We also performed topic modeling using LDA(Latent Dirichlet Allocation)) from Gensim package to get the dominant topics in our dataset. We visualized the results of our topic model using pyLDvis. The perplexity and coherence score of our topic model is -12.897670565510511 and 0.44700287476452394, respectively.
ANA at SemEval-2020 Task 4: MUlti-task learNIng for cOmmonsense reasoNing (UNION)

In this paper, we describe our mUlti-task learNIng for cOmmonsense reasoNing (UNION) system submitted for Task C of the SemEval2020 Task 4, which is to generate a reason explaining why a given false statement is non-sensical.However, we found in the early experiments that simple adaptations such as fine-tuning GPT2 often yield dull and non-informative generations (e.g.simple negations).In order to generate more meaningful explanations, we propose UNION, a unified end-to-end framework, to utilize several existing commonsense datasets so that it allows a model to learn more dynamics under the scope of commonsense reasoning.In order to perform model selection efficiently, accurately and promptly, we also propose a couple of auxiliary automatic evaluation metrics so that we can extensively compare the models from different perspectives.Our submitted system not only results in a good performance in the proposed metrics but also outperforms its competitors with the highest achieved score of 2.10 for human evaluation while remaining a BLEU score of 15.7.
A Cartesian Genetic Programming Based Parallel Neuroevolutionary Model for Cloud Server’s CPU Usage Prediction

Cloud computing use is exponentially increasing with the advent of industrial revolution 4.0 technologies such as the Internet of Things, artificial intelligence, and digital transformations. These technologies require cloud data centers to process massive volumes of workloads. As a result, the data centers consume gigantic amounts of electrical energy, and a large portion of data center electrical energy comes from fossil fuels. It causes greenhouse gas emissions and thus ensuing in global warming. An adaptive resource utilization mechanism of cloud data center resources is vital to get by with this huge problem. The adaptive system will estimate the resource utilization and then adjust the resources accordingly. Cloud resource utilization estimation is a two-fold challenging task. First, the cloud workloads are sundry, and second, clients’ requests are uneven. In the literature, several machine learning models have estimated cloud resources, of which artificial neural networks (ANNs) have shown better performance. Conventional ANNs have a fixed topology and allow only to train their weights either by back-propagation or neuroevolution such as a genetic algorithm. In this paper, we propose Cartesian genetic programming (CGP) neural network (CGPNN). The CGPNN enhances the performance of conventional ANN by allowing training of both its parameters and topology, and it uses a built-in sliding window. We have trained CGPNN with parallel neuroevolution that searches for global optimum through numerous directions. The resource utilization traces of the Bitbrains data center is used for validation of the proposed CGPNN and compared results with machine learning models from the literature on the same data set. The proposed method has outstripped the machine learning models from the literature and resulted in 97% prediction accuracy.
FRACTAL ANALYSIS AS A METHOD FOR FEATURE EXTRACTION IN DETECTING OSTEOPOROTIC BONE DESTRUCTION

Fractal analysis was used in the study to determine a set of feature descriptors which could be applied in the process of diagnosing bone damage caused by osteoporosis. The subject of the research was CT images of vertebrae on the thoraco-lumbar region. The dataset contained images of healthy patients and patients diagnosed with osteoporosis. On the basis of fractal analysis and feature selection by linear stepwise regression, three descriptors were obtained. These were two fractal dimensions calculated by the variation method and fractal lacunarity calculated by the box counting method. The first two descriptors were obtained as a result of the analysis of gray images, and the third was the result of analysis of binary images. The effectiveness of the descriptors was verified using six popular supervised classification methods: linear and quadratic discriminant analyses, naive Bayes classifier, decision tree, [Formula: see text]-nearest neighbors ([Formula: see text]-NN) and random forests. The best results were obtained using the [Formula: see text]-NN classifier; they were as follows: overall classification accuracy: 81%, classification sensitivity: 78%, classification specificity: 90%, positive predictive value: 90% and negative predictive value: 77%. The results of the research have shown that fractal analysis can be a useful tool to extract features of spinal CT images in the diagnosis of osteoporotic bone defects.
The Impact of Video Conferencing Platform in All Educational Sectors Amidst Covid-19 Pandemic

&lt;p&gt;The Impact of COVID-19 Pandemic made the business world stop and also the economic sector. Health protocols standard has been observed like social distancing, the mandatory wearing of facemask, and avoiding mass gathering. The researcher is proposing to use a video conferencing platform beside the learning management system because this will replace the face to face setup and realtime feedback from the students to the teacher. Video conferencing has always been a key ingredient in the recipe to success for enterprises and other educational sector hoping to connect with customers, remote workers, and even with the students, (BEAUFORD, 2020). The results suggest that current policies and teaching strategies can be adapted due to the outbreak of COVID-19. In relation to previous studies on the use of videoconferencing in higher education, Video Conferencing such as Zoom and Google meet the demands in a broader consideration of the relevant challenges that arise when using certain videoconferencing systems in learning and teaching situations and that can be used in the current scenario, (Khatib, 2020). Using Video Conferencing will not violate any quarantine protocols and this will ensure the safety of both students and the teachers, in times like this, we really need to adopt the new technology platforms embracing the effects of COVID-19 and might lead to the opening of the New Normal in all sectors.&lt;/p&gt;
Machine Vision Robot with Real Time Sensing

Till now by giving the commands through voice we were able to control the robot but we can’t be there every where all the time so to give command through the voice some time we may face difficulty. So in our paper we are giving the commands through the text images. The robot captures it, after processing the image it react to the command which is in the text image. So here no need of using voice command. We have described an efficient method for localizing a mobile robot in an environment with landmarks. We assume that the robot can identify landmarks along with that it is able measure its bearings relatively. The inputs are given from the environment with the help of a camera. The image processing has been done using matlab. Different feature extraction techniques are used to process the image. We present results of simulations, with that we propose how to use our method for robot navigation. We are using Arduino in designing of the robot. In our paper we are going to add speedometer to the robot so that the distance travelled by the robot will be measured. Using the GSM module the distance travelled by the robot is sent to the user mobile. Also the robot sends the message to the user mobile whether the robot reached the destination successfully or not using the GSM module. Keywords : Image processing, land mark identification, GSM module, feature extraction, robot
Influence of the quality of educational services of a technical university on the employment of graduates

Introduction. Quality management of educational services is impossible without feedback from employers, graduates, and parents of students. Analysis of their opinions will help identify weaknesses in the educational process of the University, for the subsequent formation of a roadmap for managing the quality of education. The purpose of the study: to develop recommendations for improving the quality of education of students of the technical University by identifying dissatisfaction with professional, labor and socio-cultural training. Materials and methods. 1261 interested persons took part in the survey, including: 386 graduates (representatives of 225 enterprises and organizations), representatives of employers – 265 people (representatives of 173 enterprises and organizations), parents of students – 610 people. The quality of educational process was assessed by satisfaction with educational activities and satisfaction with the socio-cultural environment of the university. Research results. The results of the level of satisfaction with the quality of educational activities are analyzed, in particular: the level of satisfaction of graduates ranged from 66 to 88%, the greatest dissatisfaction is associated with the organization of practices, research work and the material and technical base of the University. Employers are more dissatisfied with the professional training of graduates, in particular bachelors. More than 70% of employers say that specialists are much better prepared to perform their work functions. In terms of assessing the socio-cultural environment, the satisfaction of students' parents ranged from 49 to 72%. The main comments are related to financial incentives (scholarships, surcharges), as well as the organization of leisure and recreation. The practical significance is to identify the most pressing issues that are not always obvious when viewed from the "inside" of the organization. Many problems are typical for higher education institutions in General, so the results of this study can be useful and applicable for internal audits, in-house surveys, and improving the quality of educational services in other universities.
Learning to See Through a Few Pixels: Multi Streams Network for Extreme Low-Resolution Action Recognition

Human action recognition is one of the most pressing questions in societal emergencies of any kind. Technology is helping to solve such problems at the cost of stealing human privacy. Several approaches have considered the relevance of privacy in the pervasive process of observing people. New algorithms have been proposed to deal with low-resolution images hiding people identity. However, many of these methods do not consider that social security asks for real-time solutions: active cameras require flexible distributed systems in sensible areas as airports, hospitals, stations, squares and roads. To conjugate both human privacy and real-time supervision, we propose a novel deep architecture, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi Streams Network</i> . This model works in real-time and performs action recognition on extremely low-resolution videos, exploiting three sources of information: RGB images, optical flow and slack mask data. Experiments on two datasets show that our architecture improves the recognition accuracy compared to the two-streams approach and ensure real-time execution on Edge TPU (Tensor Processing Unit).
Towards Interpretable Routing Policy: A Two Stage Multi-Objective Genetic Programming Approach with Feature Selection for Uncertain Capacitated Arc Routing Problem

The Uncertain Capacitated Arc Routing Problem (UCARP) is the dynamic and stochastic counterpart of the well-known Capacitated Arc Routing Problem (CARP). UCARP has a wide range of real-world applications. One of the main challenge in UCARP is to handle the uncertain environment effectively. Routing policy-based approaches are promising technique for solving UCARP as they can respond to the uncertain environment in the real time. However, manually designing effective routing policies is time consuming and heavily replies on domain knowledge. Genetic Programming Hyper-heuristic (GPHH) has been successfully applied to UCARP to automatically evolve effective routing policies. However, the evolved routing policies are usually hard to interpret. In this paper, we aim to improve the potential interpretability of the GP-evolved routing policies by considering both program size and number of distinguished features. To this end, we propose a Two Stage Multi-Objective Genetic Programming Hyper Heuristic approach with Feature Selection (TSFSMOGP). We compared TSFSMOGP with the state-of-the-art single-objective GPHH, a two-stage GPHH with feature selection and a two-stage Multi-Objective GP. The experimental results showed that TSFSMOGP can evolve effective, compact, and thus potentially interpretable routing policies.
Towards Decentralized IoT Updates Delivery Leveraging Blockchain and Zero-Knowledge Proofs

Internet of Things (IoT) devices are being deployed in huge numbers around the world, and often present serious vulnerabilities. Accordingly, delivering regular software updates is critical to secure IoT devices. Manufactures face two predominant challenges in providing software updates to IoT devices: 1) scalability of the current client-server model and 2) integrity of the distributed updates - exacerbated due to the devices' computing power and lightweight cryptographic primitives. Motivated by these limitations, we propose CrowdPatching, a blockchain-based decentralized protocol, allowing manufacturers to delegate the delivery of software updates to self-interested distributors in exchange for cryptocurrency. Manufacturers announce updates by deploying a smart contract (SC), which in turn will issue cryptocurrency payments to any distributor who provides an unforgeable proof-of-delivery. The latter is provided by IoT devices authorizing the SC to issue payment to a distributor when the required conditions are met. These conditions include the requirement for a distributor to generate a zero-knowledge proof, generated with a novel proving system called zk-SNARKs. Compared with related work, CrowdPatching protocol offers three main advantages. First, the number of distributors can scale indefinitely by enabling the addition of new distributors at any time after the initial distribution by manufacturers (i.e., redistribution among the distributor network). The latter is not possible in existing protocols and is not account for. Secondly, we leverage the recent common integration of gateway or Hub in IoT deployments in our protocol to make CrowdPatching feasible even for the more constraint IoT devices. Thirdly, the trustworthiness of distributors is considered in our protocol, rewarding the honest distributors' engagements. We provide both informal and formal security analysis of CrowdPatching using Tamarin Prover.
Research and Improvement of Encrypted Traffic Classification Based on Convolutional Neural Network

Aiming at the problems of low recognition rate and long training time of deep convolution neural network Alexnet in encrypted traffic classification, some improvement measures are put forward for the classical Alexnet network, mainly including the introduction of multi-scale convolution, deconvolution operation and batch standardization, which can extract more comprehensive features and reduce convolution kernel parameters. The performance of the improved Alexnet convolutional neural network is tested by encrypting the traffic dataset. The test results show that the recognition accuracy and precision of the improved model on the selected test set are 83.9% and 84% respectively, which are about 7.2% and 8% higher than those of the classical model. The improved Alex net model has a certain improvement in the performance of network encryption traffic classification recognition.
CorNet: Unsupervised Deep Homography Estimation for Agricultural Aerial Imagery

Efficient and accurate estimation of homographies among images is the first step in mosaicking crop fields for phenotyping. The current strategy uses sophisticated vehicles that have excellent telemetry to hover over a grid of waypoints, imaging each one. This approach simplifies homography estimation, but precludes more flexible, adaptive protocols that can collect richer information. It also makes aerial phenotyping impractical for many researchers and farmers. We are developing an alternative strategy that uses consumer-grade vehicles, freely flown over a variety of trajectories, to collect video. We have developed an unsupervised deep learning network that estimates the sequence of planar homography matrices of our corn fields from imagery, without using any metadata to correct estimation errors. The vehicle was freely flown using a variety of trajectories and camera views. Our system, CorNet, performed faster than and with comparable accuracy to the gold standard ASIFT algorithm in many challenging cases.
A method based on multi-standard active learning to recognize entities in electronic medical record

Deep neural networks(DNN)have achieved good results in the application of Named Entity Recognition (NER), but most of the DNN methods are based on large numbers of annotated data. Electronic Medical Record (EMR) belongs to text data of the specific professional field. The annotation of this kind of data needs experts with strong knowledge of the medical field and time labeling. To tackle the problems of professional medical areas, large data volume, and annotation difficulties of EMR, we propose a new method based on multi-standard active learning to recognize entities in EMR. Our approach uses three criteria: the number of labeled data, the cost of sentence annotation, and the balance of data sampling to determine the choice of active learning strategy. We put forward a more suitable way of uncertainty calculation and measurement rule of sentence annotation for NER's neural network model. Also, we use incremental training to speed up the iterative training in the process of active learning. Finally, the named entity experiment of breast clinical EMRs shows that it can achieve the same accuracy of NER results under the premise of obtaining the same sample's quality. Compared with the traditional supervised learning method of randomly selecting labeled data, the method proposed in this paper reduces the amount of data that needs to be labeled by 66.67%. Besides, an improved TF-IDF method based on Word2Vec is also proposed to vectorize the text by considering the word frequency.
The impact of strategic agility on employees’ performance in commercial banks in Jordan

This study aimed to determine strategic agility impact on employees’ performance in commercial banks in Jordan. A self-administrated questionnaire was developed according to research objective and hypotheses. The research population consisted of all managerial employees in Jordanian commercial banks. A random sample was selected consisting of 250 staff members who have senior administrative and supervisory positions in the commercial banks. Statistical techniques were used to test the research hypotheses. The research concluded a set of outcomes, the most important is strategic agility with its dimensions have an impact on employee performance in the commercial banks in Jordan. The research also concluded that strategic agility dimensions (strategic sensitivity, core capabilities, clarity of vision, strategic goals information technology selection, and share responsibility) influence on employee's performance in commercial banks in Jordan. The study recommended that commercial banks in Jordan have to adopt strategic agility approach to improve their employee's performance. The commercial banks must exert their best efforts to rapidly adapt to surrounding environmental variables. Commercial banks have to pay attention to human capital, which plays a major role in achieving good performance.
Unsupervised image segmentation with Gaussian Pairwise Markov Fields

Modeling strongly correlated random variables is a critical task in the context of latent variable models. A new probabilistic model, called Gaussian Pairwise Markov Field, is presented to generalize existing Markov Fields latent variables models, and to introduce more correlations between variables. This is done by considering the correlations within Gaussian Markov Random Fields models which are much richer than in the classical Markov Field models. The assets of the Gaussian Pairwise Markov Field model are explained. In particular, it offers a generalization of the classical Markov Field modelization that is highlighted. The new model is also considered in the practical case of unsupervised segmentation of images corrupted by long-range spatially-correlated noise, producing interesting new results.