Reviewerly/exHarmony huggingface dataset:
Exploring a Filter and Wrapper Feature Selection Techniques in Machine Learning

Nowadays, huge amounts of data are generated by many fields such as health care, astronomy, social media, sensors, and so on. When working with such data, there is a need for the removal of irrelevant, redundant, or unrelated data. Among various preprocessing techniques, dimensionality reduction is one such technique used to clean data. It helps the classifiers by reducing training time and improving the classification accuracies. In this work, the most widely used feature selection techniques were analyzed in machine learning for improving the classification as well as prediction accuracies.
Face Detection for Real World Application

Face Detection has become a very prevalent issue in Machine Learning, not only in machine learning but in any field, one can think of. Due to this, it has gained a wide fan base and many people are working every day to improve the accuracy of object detection models using deep learning. But this improved performance comes at the price of increased computational overhead, which limits the ability of a machine learning model to be utilized on devices having small Graphical Processing Units. The core intent of this paper is to compare computation time for models such as Histogram of Oriented gradients (0.4 seconds) and ResNet (48.5 seconds) with BlazeFace (0.09 seconds), a model developed by google in the year 2020 and is a mobile device friendly model and fits well with real time application which need instant feedback and on top of that cannot handle bulky computations required for deep learning models.
Attrition in software companies: Reason and measures

The amount of people leaving an organization plays a critical role in business success. Higher employee turnover could be serious as they are an essential part of every business. The loss of key people would have significant impacts on organizational and financial development as well as on maturity. The word "Attrition" refers to scaling down of employees in an organization. It occurs because of retirement, resignation or death of the employee. There can be many reasons for an employees to leave an organization like he may be getting higher job position in some other company or he may want to change his profession or he may leave an organization for higher studies, etc. A further contributing factor to turnover is the absence of employees' confidence in their representatives, which results in a lack of motivation and therefore in low employee retention. From the employee's side they state that lack of recognition and lack of growth within the organizations. Unfair treatment of employees is also another reason. Recently, gender discrimination has also contributed attrition. Finally, the present generation finds their jobs less challenging.
Evaluating Various Tokenizers for Arabic Text Classification

The first step in any NLP pipeline is to split the text into individual tokens. The most obvious and straightforward approach is to use words as tokens. However, given a large text corpus, representing all the words is not efficient in terms of vocabulary size. In the literature, many tokenization algorithms have emerged to tackle this problem by creating subwords, which in turn limits the vocabulary size in a given text corpus. Most tokenization techniques are language-agnostic, i.e., they do not incorporate the linguistic features of a given language. Not to mention the difficulty of evaluating such techniques in practice. In this paper, we introduce three new tokenization algorithms for Arabic and compare them to other three popular tokenizers using unsupervised evaluations. In addition, we compare all the six tokenizers by evaluating them on three supervised classification tasks: sentiment analysis, news classification and poem-meter classification, using six publicly available datasets. Our experiments show that none of the tokenization techniques is the best choice overall and that the performance of a given tokenization algorithm depends on many factors including the size of the dataset, nature of the task, and the morphology richness of the dataset. However, some tokenization techniques are better overall as compared to others on various text classification tasks.
<i>POD</i>: A Parallel Outlier Detection Algorithm Using Weighted kNN

Outlier detection algorithms based on k nearest neighbors ( kNN) can effectively find outliers from massive data, but most algorithms are difficult to adapt to high-dimensional data sets. In order to highlight the importance of attributes in k nearest neighbors, we propose a weighted kNN query method, which uses the Z-order curve to find the kNN. The method first applies information entropy to calculate each attribute weight, and then uses the Z-order curve to encode high-dimensional data into Z-value. The weighted kNN of each object are searched according to its Z-value. Meanwhile, a novel outlier detection algorithm is presented based on the minimum distance and average distance between each object and its weighted kNN. On this basis, we propose a parallel outlier detection algorithm called POD to improve the efficiency of the outlier detection. Finally, we implement and evaluate POD algorithm on a 10-nodes Hadoop cluster, on which synthetic and UCI standard data are tested. Experimental results show that POD achieves high performance in terms of effectiveness, scalability and extensibility.
A ghostfree contrast enhancement method for multiview images without depth information

High dynamic range (HDR) images greatly improve visual content quality, but pose challenges in processing, acquisition, and display. Images captured in real-world scenarios with multiple nonlinear cameras, extremely short unknown exposure time, and a shared light source present the additional challenges of incremental baseline and angle deviation amongst the cameras. The disparity maps in such conditions are not reliable; therefore, we propose a method that relies on the accurate detection and matching of feature points across adjacent viewpoints. We determine the exposure gain among the matched feature points in the involved views and design an image restoration method to restore multiview low dynamic range (MVLDR) images for each viewpoint. Finally, the fusion of these restored MVLDR images produces high-quality images for each viewpoint without capturing a series of bracketed exposure. Extensive experiments are conducted in controlled and uncontrolled conditions, and results prove that the proposed method competes for the state-of-the-arts.
Derin Öğrenme Tabanlı Transfer Öğrenme Yöntemiyle İnsan ve Araçların Sınıflandırılması

There has been a significant increase in the use of deep learning algorithms in recent years. Convolutional neural network (CNN), one of the deep learning models, is frequently used in applications to distinguish important objects such as humans and vehicles from other objects, especially in image processing. After the ImageNet Large Scale Visual Recognition Competition (ILSVRC) in 2012, the use of ESA in applications is becoming quite common. With the development of image processing hardware, the image processing process is significantly reduced. Thanks to these developments, the performance of studies on deep learning is increasing. In this study, a system based on deep learning has been developed to detect and classify objects (human, car and motorcycle / bicycle) from images captured by drones. Two datasets, the image set of Stanford University and the drone image set created at Afyon Kocatepe University (AKÜ), are used to train and test the deep neural network with the transfer learning method. Training and testing processes are carried out using a total of 3841 images, 2591 from the Stanford dataset and 1250 from the AKÜ dataset. The precision, recall and f1 score values are evaluated according to the process of determining and classifying human, car and motorcycle / bicycle classes using GoogleNet, VggNet and ResNet50 deep learning algorithms. According to this evaluation result, high performance results are obtained with 0.916 precision, 0.895 recall and 0.906 f1 score value in the ResNet50 model.
A Comprehensive Survey on Identification and Analysis of Phishing Website based on Machine Learning Methods

Phishing is a cybercrime which is carried out by imitating a legal website to trick users to steal their personal data, including usernames, passwords, account numbers, national insurance numbers, etc. Phishing frauds may be the most widespread cybercrime used today. Machine learning focuses on computer algorithms which improves automatically through experience. Machine learning methods were utilized to detect phishing URLs that typically evaluates an URL based on a feature or set of features extracted from it. This paper presents an approach to identify phishing websites using trained machine learning models. It also delivers a detailed analysis of phishing attacks with a comparison on machine learning approaches used for analysis and classification of phishing and legitimate websites.
Validating Development Indicators for Smart University: Quality Function Deployment

The article shows the application of a quality management tool called “Quality Function Deployment” at university. This tool allows managers to justify the choice of controlled parameters for their monitoring and further assessment as the most priority evaluated characteristics of the competitiveness for a smart university from the external consumers of educational services known as stakeholders. In contrast to already known studies in the area of education system development, this research reflects the possibility of predicting the effectiveness of further activities of smart organization performers. The results obtained allow managers developing a mechanism for assessing the level of a smart organization development in the context of existing classification characteristics. The practical importance is the possibility of system monitoring for the estimated characteristics of competitiveness.
Spatial-temporal Regularized Multi-modality Correlation Filters for Tracking with Re-detection

The development of multi-spectrum image sensing technology has brought great interest in exploiting the information of multiple modalities (e.g., RGB and infrared modalities) for solving computer vision problems. In this article, we investigate how to exploit information from RGB and infrared modalities to address two important issues in visual tracking: robustness and object re-detection. Although various algorithms that attempt to exploit multi-modality information in appearance modeling have been developed, they still face challenges that mainly come from the following aspects: (1) the lack of robustness to deal with large appearance changes and dynamic background, (2) failure in re-capturing the object when tracking loss happens, and (3) difficulty in determining the reliability of different modalities. To address these issues and perform effective integration of multiple modalities, we propose a new tracking-by-detection algorithm called Adaptive Spatial-temporal Regulated Multi-Modality Correlation Filter. Particularly, an adaptive spatial-temporal regularization is imposed into the correlation filter framework in which the spatial regularization can help to suppress effect from the cluttered background while the temporal regularization enables the adaptive incorporation of historical appearance cues to deal with appearance changes. In addition, a dynamic modality weight learning algorithm is integrated into the correlation filter training, which ensures that more reliable modalities gain more importance in target tracking. Experimental results demonstrate the effectiveness of the proposed method.
Artificial Noise Assisted Secure Mobile Crowd Computing in Intelligently Connected Vehicular Networks

Growing computation requirements in emerging intelligently connected vehicle (ICV) networks has posed a big challenge for vehicles to process computation-intensive tasks on-board, and thus computation partition and offloading plays a critical role. Owing to broadcast nature of wireless channels, confidential messages without proper security protection in ICVs may be easily exposed to malicious users. In this paper, a mobile crowdsourcing (MCS) based mobile crowd computing framework for ICV networks is proposed, where multiple vehicles act as workers to provide computing services for end user (EU). In particular, artificial noise (AN) assisted physical (PHY) layer security approaches are used to enhance the security in offloading links. Ergodic secrecy rates in different offloading phases in time-varying channels are derived. In addition, an incentive-driven mechanism is introduced to encourage workers to share their idle computing resources, and an optimization problem is formulated to minimize the overall price paid for computing tasks, subject to energy consumption and delay constraints. Finally, Monte Carlo simulations verify the analysis on the ergodic secrecy rates.
Learning Robust Feature Transformation for Domain Adaptation

There is a growing importance of feature extraction in transferring valuable knowledge from a source domain to a different but related target domain. However, when the target data are contaminated by unpredictable and complex noises, the ability of most existing feature extraction methods would be limited. In this paper, we deeply investigate the robust property of Kernel Mean P-Power Error Loss (KMPE-Loss), and thus propose a novel Robust Transfer Feature Learning (RTFL) method to enhance the robustness of domain adaptation. The key idea of RTFL is to learn a shared transformation by: 1) detecting and neglecting the contaminated target points without any specific assumption on noises; 2) reconstructing the remaining clean target points using the corresponding source-domain neighborhood; 3) incorporating a relative entropy based regularization to reap theoretic advantages. Consequently, the distribution difference between two domains is accurately reduced for knowledge transfer. We propose an alternative procedure to optimize RTFL with explicitly guaranteed convergence. As an extension, the transformation based matrix in RTFL is restricted to a small dimension basis, admitting the highly reduced computation complexity. Extensive experiments in various domain adaptation tasks demonstrate the superiority of our methods.
SADPonzi: Detecting and Characterizing Ponzi Schemes in Ethereum Smart Contracts

Ponzi schemes are financial scams that lure users under the promise of high profits. With the prosperity of Bitcoin and blockchain technologies, there has been growing anecdotal evidence that this classic fraud has emerged in the blockchain ecosystem. Existing studies have proposed machine-learning based approaches for detecting Ponzi schemes. However, these state-of-the-art approaches face several major limitations, including lacking interpretability, high false positive rates and the weak robustness to evasion techniques, These limitations mean that existing real-world methods for detecting Ponzi schemes are ineffective.
Reinforcing Pretrained Models for Generating Attractive Text Advertisements

We study how pretrained language models can be enhanced by using deep reinforcement learning to generate attractive text advertisements that reach the high quality standard of real-world advertiser mediums. To improve ad attractiveness without hampering user experience, we propose a model-based reinforcement learning framework for text ad generation, which constructs a model for the environment dynamics and avoids large sample complexity. Based on the framework, we develop Masked-Sequence Policy Gradient, a reinforcement learning algorithm that integrates efficiently with pretrained models and explores the action space effectively. Our method has been deployed to production in Microsoft Bing. Automatic offline experiments, human evaluation, and online experiments demonstrate the superior performance of our method.
Mass-wasting processes input in proximal metalliferous sediments: A case study of the Pobeda hydrothermal fields

Metalliferous sediments associated with oceanic hydrothermal systems contain various amounts of metals of a dual origin: from suspended particulate matter of an overlying plume and from weathered massive sulfide edifices. This study investigates the influence of the latter component on metal enrichment in sediments proximal to sulfide bodies for the Pobeda hydrothermal fields of the Mid-Atlantic Ridge (MAR). This region consists of three separate venting fields Pobeda-1, 2, and 3 located at different depth levels in the MAR rift valley. Hydrothermal precipitates within the site occur as massive sulfide edificies and metalliferous sediments. Detailed mineralogical and geochemical analysis of all these components revealed that a significant amount of hydrothermally-derived material in the sediments derives from overlying plume particulate superimposed by mass wasted oxidized sulfides from the nearest edifices. This last component is mostly abundant in the sediments of the deepest Pobeda-2 field, which complicates using bulk geochemistry to decipher the history of hydrothermal activity there. For the two remaining fields, Pobeda-1 and Pobeda-3, a reconstruction of vent discharge history indicates that it began almost simultaneously at the time interval recorded in core sediments. However it waned steadily at the Pobeda-3 field, whereas at Pobeda-1 field it exhibited a significant decrease in intensity and then a reverse movement upward until recently.
Red Alert for Power Leakage: Exploiting Intel RAPL-Induced Side Channels

RAPL (Running Average Power Limit) is a hardware feature introduced by Intel to facilitate power management. Even though RAPL and its supporting software interfaces can benefit power management significantly, they are unfortunately designed without taking certain security issues into careful consideration. In this paper, we demonstrate that information leaked through RAPL-induced side channels can be exploited to mount realistic attacks. Specifically, we have constructed a new RAPL-based covert channel using a single AVX instruction, which can exfiltrate data across different boundaries (e.g., those established by containers in software or even CPUs in hardware); and, we have investigated the first RAPL-based website fingerprinting technique that can identify visited webpages with a high accuracy (up to 99% in the case of the regular network using a browser like Chrome or Safari, and up to 81% in the case of the anonymity network using Tor). These two studies form a preliminary examination into RAPL-imposed security implications. In addition, we discuss some possible countermeasures.
Still Image Action Recognition Using Ensemble Learning

In recent years, human action recognition in still images has become a challenge in computer vision. Most methods in this field use annotations such as human and object bounding boxes to determine human-object interaction and pose estimation. Preparing these annotations is time-consuming and costly. In this paper, an ensembling-based method is presented to avoid any additional annotations. According to this fact that a network performance on fewer classes of a dataset is often better than its performance on whole classes; the dataset is first divided into four groups. Then these groups are applied to train four lightweight Convolutional Neural Networks (CNNs). Consequently, each of these CNNs will specialize on a specific subset of the dataset. Then, the final convolutional feature maps of these networks are concatenated together. Moreover, a Feature Attention Module (FAM) is trained to identify the most important features among concatenated features for final prediction. The proposed method on the Stanford40 dataset achieves 86.86% MAP, which indicates this approach can obtain promising performance compared with many existing methods that use annotations.
Use of Innovation and Emerging Technologies to Address Covid-19-Like Pandemics Challenges in Education Systems

In this crisis of the COVID19 pandemic, for which the public and private educational system was not prepared, the measures of confinement and social distancing forced us all to experiment with various distance education strategies. The experiences have been diverse, between the absurd and creative. The digital transformation of our societies has been underway for a long time, and it has accelerated in recent years with the advance of many emerging technologies. These emerging technologies to address covid-19-like pandemics challenges in education systems been-or is being-implemented in several countries. This paper introduces the digital transformation of education and the significant trends in technologies applied to education. We track current the education systems' response to COVID-19 across the world and in Canada and Russia, particularly.
Restoration Medical Images from Speckle Noise Using Multifilters

In digital image processing and precisely in the medical field, most of the medical images are usually corrupted by different types of noises which give importance to achieve high-quality images to facilitate the observation operations for the given medical applications. The techniques of de-noising have become a basic practice in the applications of medical imaging to study of processing and biological structure of the medical images, which cause to be the processes of restoring the medical images that have a wide challenge in the scope of medical image processing. Therefore, to overcome these challenges, many restore algorithms have been developed like, Gaussian, median, and Weiner filters. These filters are used for the most generally affected noises in the medical images such as Gaussian, Speckle, Salt and Pepper, and Poisson noises, etc. This paper focuses on a process of adding noise using the speckle noise of the medical image and removes this noise by using three types of filters as a mean, median, and Weiner filters, these filters applied in different stages on the different medical image.
Representation Learning on Knowledge Graphs for Node Importance Estimation

In knowledge graphs, there are usually different types of nodes, multiple heterogeneous relations, and numerous attributes of nodes and edges, which impose the challenges on the task of Node Importance Estimation (NIE). Indeed, existing NIE approaches, such as PageRank (PR) and Node-Degree (ND), are not designed for handling knowledge graphs with the rich information related with these multifarious nodes and edges. To this end, in this paper, we propose a representation learning framework to leverage the rich information inherent in these multifarious nodes and edges for improving node importance estimation in knowledge graphs. Specifically, we provide a Relational Graph Transformer Network (RGTN), where a relational graph transformer is first proposed to propagate node information with the consideration of semantic predicate representations. Here, the assumption is that different predicates may have distinct effects on the transmission of node importance. Then, two separate encoders are designed to capture both the structural and semantic information of nodes respectively, and a co-attention module is developed to fuse the two separate representations of nodes. Next, an attention-based aggregation module is adopted to map the representations of nodes to their importance values. In addition, a learning-to-rank loss is designed to ensure that the learned representations can be aware of the relative ranking information among nodes. Finally, extensive experiments have been conducted on real-world knowledge graphs, and the results illustrate that our model outperforms the existing methods consistently for all the evaluation metrics. The code and the data are available at https://github.com/GRAPH-0/RGTN-NIE.