Reviewerly/exHarmony huggingface dataset:
Conclusions and Future Research Directions

This chapter concludes the book. Throughput the book, we introduced the problem of event attendance prediction, and proposed a context-aware data mining approach to solve it. Experimental results based on the collected dataset demonstrated that the proposed approach can predict event attendance with high accuracy. Finally, we point out future research directions.
Face Recognition in Different Light Conditions

Facial biometrics continues to be the preferred biometric bench-mark even though other human body signatures are also used. But there are many problems with face recognition. A slight change in the image, which may be due to illumination, or to the environment, can drastically affect the results and accuracy. These changes may not even be noticeable to the human eye, but can affect the face recognition process. There are some advantages, such as contactless, easy to use and, keeping in mind the current situation, reduces the risk of getting infected by touching buttons or by simply waiting in the queue for a long time. In this paper, an effort is made to review different kinds of face recognition methods comprehensively. PCA, LDA, SVM, and various hybrid techniques are used for the face recognition approach. This review examines all of the aforementioned techniques as well as various parameters that pose challenges to face recognition, such as illumination, pose variations, and facial expression.
Utilizing Multi-Agent Systems Approach in Firefly Algorithm

Generally, using collective intelligence is one of the interesting topics is researchers of recent years, which its purpose is modeling creatures’ simple behaviors and their interaction with the environment and neighbor creatures to obtain more complex behaviors. We could utilize algorithms based on collective intelligence to solve complicated problems like optimization problems. So far, various algorithms have been purposed in this field which firefly algorithm is a variant of these. In this algorithm, each member acts as a better response concerning itself. However, this algorithm has some drawbacks like the consistency of parameters value, lack of balance between local search and global search, and others. On one hand, multi-agent systems are software systems that contain sets of agents. These agents perform their tasks together to solve a problem and reach the desired purpose. In this paper, we have tried to utilize a multi-agent system, in addition to meta-heuristic optimization algorithms, to improve the performance of the firefly algorithm to better cooperate warms populations with each other. The results of the experiment show the acceptable performance of the proposed algorithm.
SSLChecker

In this work we devise a SSLChecker tool, for testing server side vulnerabilities in SSL/TLS implementations. We integrate into our tool central vulnerabilities exposing to attacks and evaluate SSLChecker over them. The goal of SSLChecker is to help: (1) the web server operators to identify vulnerabilities and mitigate them, and (2) to warn users of accessing potentially vulnerable servers. We set SSLChecker as public service with open source code on github.
Immutable Secure Data Exchange and Storage for Urban Air Mobility Environments

Urban air mobility (UAM) is a concept that proposes to develop short-range aerial vehicles to overcome increasing surface congestion. Within the UAM environment, UAM operators work collaboratively to manage aerial vehicles in the urban environment. Providers of Services for UAM (PSU), UAM operators, and Supplemental Data Service Providers (SDSP) support flight operations within the UAM environment. The development of UAM systems and the associated data exchange and service interactions will be at risk due to numerous cybersecurity attacks. This research focuses on the secure data exchange and storage of this decentralized UAM environment to address these challenges. This research intends to leverage a permissioned blockchain approach to address cybersecurity threats that may impact a UAM environment.
Deep Sentence Denoising beyond Grammatical Error Correction

Clear and efficient communication requires more than grammatical correctness to ensure fluency and semantic correctness, especially for non-native speakers. Thus, we propose a new task – Sentence Denoising, to go beyond Grammatical Error Correction (GEC). We define a rich and linguistics-inspired noise taxonomy consisting of 13 types of noise, and categorize them into vagueness, redundancy, and incoherence. We then generate and study 4 types of noise out of the 13 because they serve as building blocks. Methods are proposed to inject targeted noise into sentences for building datasets. We publish them and give benchmarks for denoising both individual noise and compound noise. Finally, an efficient training approach is designed for denoising combinations of noise.
Trends In Natural Language Processing : Scope And Challenges

Quantum cryptography is a comparatively new and special type of cryptography which uses Quantum mechanics to provide unreal protection of data/information and unconditionally secure communications. This is achieved with Quantum Key Distribution (QKD) protocols which is a representation of an essential practical application of Quantum Computation. In this paper the authors will venture the concept of QKD by reviewinghow QKD works, the authors shall take a look at few protocols of QKD, followed by a practical example of Quantum Cryptography using QKD and certain limitations from the perspective of Computer Science in specific and Quantum Physics in general.
Enhancement of traditional clothes pattern recognation using Convolutional Neural Network

In general, Traditional clothes in Indonesia has its own characteristics to recognize the origin of the clothes. The formation of traditional clothes patterns in Indonesia from the crafts of remote communities in the regions of Indonesia. The article is that Indonesian citizens still find it difficult to distinguish traditional clothes that are scattered throughout Indonesia. To support this research, we use the Convolutional neural network method for types VGG16, VGG19 and MobileNetV2. To support the recognition process for a variety of traditional clothes patterns, the pattern processing process has started from preprocessing and image segmentation. This research was conducted on 5 types of experiments with supporting parameters, namely the number of training images and test images, rotation and scale of each image, image class and CNN parameters. After we conducted experiments on a collection of images that could be found in various scales and degrees, the average classification accuracy using VGG 16 was 79,23% in condition 2, but the highest classification accuracy was between 84,2%-92,5%. Meanwhile, if using VGG 19 the average classification accuracy is 79,95% with condition 1 for the highest accuracy between 92,6%-96,3%. If using mobilenetV2 the average classification accuracy is 83,44% for the highest accuracy between 87,3%-96,4%. Basically Research for traditional clothes pattern recognition using Deep CNN has succeeded in recognizing some traditional clothes patterns up to more than 92%.
Correction to “Correntropy-Based Autoencoder-Like NMF With Total Variation for Hyperspectral Unmixing”

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , it should be noted that the second value of each column in the last row of <xref ref-type="table" rid="table1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Table I</xref> (i.e., 0.52%, 0.35%, 0.51%, and 0.72%) is calculated using average deviation rather than standard deviation. In order to be consistent with the title of <xref ref-type="table" rid="table1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Table I</xref> in <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , the corresponding standard deviations are provided here.
THE POTENTIAL USE OF THE ELECTRONIC TEXTBOOK WHEN TEACHING THE SUBJECT OF MAN AND NATURE IN 5TH GRADE

The paper textbook or its set is one of the main tools for teaching and learning. As an alternative to paper textbooks, electronic textbooks for the subjects of Man and Nature, and Math for 5th grade came out in 2018. During the survey carried out by the TAMO group, the teachers of the Man and Nature subject have indicated that the main issue limiting the use of the electronic textbook is the school’s technological base. The textbook is expected to have the functionality, which would make the job of the teacher easier, complementing it with tools for teaching and the possibility for easier differentiation and customization. The electronic textbook of the Man and Nature subject was developed on the basis of general education program, recommendations of the international assessment of student achievement research and other documentation. The textbook is focused on active self-study, is adapted to students of different achievement levels, and promotes critical thinking, creativeness, data analysis and interpretation, research activity skills. The content of the textbook subject is divided into separate fragments, which the teacher can construct while creating his/her own subject according to the situation of a specific class. The teacher can take the advantage of the set of differentiated tasks, by appointing the set of tasks to satisfactory, general or higher level students or by adapting the set to a specific student. The teacher receives the information about the achievements of the students by using the result analysis tools and by analysing the student’s profile, where the learning progress can be seen. The most effective use of the textbook set can be reached if each student would have a personal computer or a laptop available or could learn using a mobile phone. However, the content of the textbook can be demonstrated with the help of a projector or an interactive board or a textbook and assign the tasks to be done at home using the “Flipped Classroom” method. This is done when the students analyse the content of the subject and carry out the tasks independently on their own devices, and then it is discussed and explained during the lesson. Keywords: textbook, electronic textbook, technological base, differentiation, customization.
Aesthetic-Aware Recommender System for Online Fashion Products

Recommender systems become widely used on the web, especially in e-commerce. In online fashion platforms, personalization becomes prevalent as user's decisions are driven not only by textual features but also the aesthetic features of products. We propose a novel machine learning approach that makes personalized fashion recommendations based on aesthetic and descriptive features of fashion products. We also introduce a new model that discovers the hidden correlations between fashion products that are frequently bought together as pairs. Our experiments on four real fashion datasets demonstrated that our aesthetic-aware recommender can achieve recommendations with accuracy up-to 89% when compared to related fashion recommenders.
End to End Autorship Email Verification Framework for a Secure Communication

The paper proposes an alternative email account protection to prevent a very specific targeting email attacks where an attacker can impersonate a legitimate/trusted sender to steal personal information to the recipient. Authorship mechanism based on the analysis of the author’s writing style and implemented through binary traditional and deep learning classifiers is applied to build the email verification mechanism. A flexible architecture, where the authorship component can be placed in different locations, is proposed. Due to its location and consequently to the email data available, can be exploited an individual writing style, or an end to end writing style learning related to the sender-receiver communication. The system is validated on two different dataset (i) the well-known public Enron dataset, with the experiments showing the author verification accuracy of 96.5% and 99% respectively for the individual and end to end writing style learning and (ii) our private dataset, with accuracy results of 98.3% and 97%. An alternative classification training, that exploits the partition of the dataset in subsets having approximately the same length, is presented. From the results obtained is proved how such training approach outperforms the traditional training where emails of different lengths are contained in the same training dataset. The overall results obtained proved that the authorship mechanism proposed is a promising alternative support technique exploitable as an email anti-scam or anti-theft tool to guarantee secure email communication.
Use Image Clustering to Facilitate Technology Assisted Review

During the past decade breakthroughs in GPU hardware and deep neural networks technologies have revolutionized the field of computer vision, making image analytical potentials accessible to a range of real-world applications. Technology Assisted Review (TAR) in electronic discovery though traditionally has dominantly dealt with textual content, is witnessing a rising need to incorporate multimedia content in the scope. We have developed innovative image analytics applications for TAR in the past years, such as image classification, image clustering, and object detection, etc. In this paper, we discuss the use of image clustering applications to facilitate TAR based on our experiences in serving clients. We describe our general workflow on leveraging image clustering in tasks and use statistics from real projects to showcase the effectiveness of using image clustering in TAR. We also summarize lessons learned and best practices on using image clustering in TAR.
Analysis of scene anomaly recognition method based on machine learning

Among the many video surveillance problems, we usually pay the most attention to anomalies. The so-called exception can have different definitions for different scenarios. The detection and judgment of abnormal phenomena often need to rely on other intelligent video surveillance technologies: moving target detection and tracking, human action recognition and behavior analysis, interactive behavior understanding, crowd analysis, etc. The research of this paper is mainly to detect and analyze abnormal behaviors in monitoring scenarios. For different monitoring scenarios, two different anomaly detection ideas are proposed. One is based on target detection and tracking, which extracts the motion information of the target area by locating the position of the person in the scene, and analyzes abnormal behavior on the basis of action recognition. Another idea is to analyze the scene based on the abnormalities of the video image texture features. The former has a relatively robust detection effect when it can accurately track and locate moving targets, while the latter is suitable for abnormal detection in crowded people.
Number Recognition System of Real time Indian Sign Language

Too many research has been done in the field of Human Computer Interaction (HCI). One of the system called Hand Gesture Recognition (HGR) gives solution to build the HCI systems. Now a days, computer is used as a interpreter between humans. The proposed system is used to recognize the real time static hand gesture of Indian sign language number system zero to nine. In this paper we propose a system for hand gesture recognition which is simple and fast. Based on the proposed algorithm, this system can automatically convert the input hand gesture into the text and audio. The system first capture the image of hand gesture shown by user using a simple webcam then using our proposed algorithm it recognize the gesture. This system can use for real time application due to the use of simple logic condition applied to recognize the gesture. The proposed system is size invariant and implemented using OpenCV.
Core: A Knowledge Graph Entity Type Prediction Method Via Complex Space Regression and Embedding

Entity type prediction is an important problem in knowledge graph (KG) research. A new KG entity type prediction method, named CORE (COmplex space Regression and Embedding), is proposed in this work. The proposed CORE method leverages the expressive power of two complex space embedding models; namely, RotatE and ComplEx models. It embeds entities and types in two different complex spaces using either RotatE or ComplEx. Then, we derive a complex regression model to link these two spaces. Finally, a mechanism to optimize embedding and regression parameters jointly is introduced. Experiments show that CORE outperforms benchmarking methods on representative KG entity type inference datasets. Strengths and weaknesses of various entity type prediction methods are analyzed.
A Comparative Study of Recent Feature Selection Techniques Used in Text Classification

As we all know, handling large amounts of data is a problem these days. Despite having so many resources to store, train and process the data, still it is required to reduce these datasets in order to reduce computational complexity, save time, cost and retrieve valuable information from large text documents. The presentation of a machine learning algorithm relies upon the dataset utilized. When the dataset is large, the learning algorithm tries to accommodate all the features which increases the dimensionality of the data. This high-dimensional data is not useful as it might contain irrelevant and redundant features. It becomes important to remove these features. Thus, pre-processing of data is required to compress and analyse the dataset for the purpose of text classification (TC). This can be achieved by using feature selection (FS) techniques. The fundamental goal of FS techniques is to acknowledge pertinent features and to get rid of repetitive attributes w.r.t. high-dimensional data (Shroff and Maheta in 2015 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2015 [1]). Nowadays, major FS methods use optimization algorithms (Brownlee in https://machinelearningmastery.com/ . 23 Dec 20 [Online]. Available: https://machinelearningmastery.com/tour-of-optimization-algorithms/ . Accessed 3 Feb 2021 [2]) to get an ideal component subset from high-dimensional information from feature space which decreases computational expense and builds classifier precision. Some of the recent feature selection techniques have been discussed in this paper which can prove to be useful for text classification (TC).
Workflow Scheduling for Remote Sense Application Using Data Transformation Graph in Inter-cloud

Inter-cloud environment provides massive computing resources to meet the increasing amount of data and computation for remote sensing applications. However, how to effectively map sub-tasks to different cloud service providers to achieve QoS(Quality of Service) index optimization is still a problem that needs to be studied. Remote sensing applications need to process trillions of bytes of data each time, unreasonable scheduling will lead to a large amount of data transmission across the cloud, which will seriously affect the performance of QoS indicators such as makespan and cost. By using data transformation graph(DTG) to study the data transmission process of global drought detection application, which is a remote sensing application, an optimized scientific workflow scheduling based on genetic algorithm is proposed for inter-cloud environment, which can minimize makespan and cost simultaneously. Experimental results show that this method can significantly optimize QoS index for data-intensive application like remote sense application and can effectively reduce the impact of performance bottlenecks.
Attention Guided Refinement Network for Crowd Counting

Crowd counting is generally a pixel-wise regression task and owns the broad application prospects. In order to generate high-quality density maps, recent remarkable works mostly adopt the long skip connection or encoder-decoder architectures which aggregate features via the step-by-step upsampling operations and direct contextual information fusion. Despite the great achievements, the researchers would ignore the negative effects of feature alignment and semantic imbalance caused by the above architectures. Faced with the mentioned problem, we propose a deep architecture called Attention Guided Refinement Network (AGRNet) for crowd counting. Our AGRNet is composed of two major schemes: the Tri-attention Refinement Module (TRM) and the Semantic Refinement Module (SRM). In particular, the TRM attempts to explore the discriminative triple attention and the transformation point offsets to jointly repair the serious spatial dislocation between adjacent features and enhance the description ability. Meanwhile, the SRM explicitly learns the semantic interdependence within features of diverse levels to relieve the semantic imbalance caused by the rough context fusion. Finally, extensive experiments are conducted on multiple crowd counting datasets, the ShanghaiTech, the UCFQNRF, the NWPU-Crowd, the JHU-CROWD++. The results have indicated the superior performance of our AGRNet against the state-of-the-art methods.
Task Independent Capsule-Based Agents for Deep Q-Learning

In recent years, Capsule Networks (CapsNets) have achieved promising results in tasks such as object recognition thanks to their invariance characteristics towards pose and lighting. They have been proposed as an alternative to relational insensitive and translation invariant Convolutional Neural Networks (CNN). It has been empirically proven that CapsNets are capable of achieving competitive performance while requiring significantly fewer parameters. This is a desirable characteristic for Deep reinforcement learning which is known to be sample-inefficient during training. In this paper, we propose DCapsQN, a task-independent CapsNets-based architecture in the deep reinforcement learning setting. We experiment in the model-free reinforcement learning setting, more specifically in Deep Q-Learning using the Atari suite as the testbed of our analysis. To the best of our knowledge, this work constitutes the first CapsNets-based deep reinforcement learning architecture to learn state-action value functions without the need for task-specific adaptation. Our results show that, in this setting, DCapsQN requires 92% fewer parameters than the baseline. Moreover, despite their smaller size, the DCapsQN provides significant boosts in performance (score), ranging between 10%–77% while further stabilising the Deep Q-Learning. This is supported by our empirical results which shows that DCapsQN agents outperform the benchmark Double-DQN agent, with Prioritized experience replay, in eight out of the nine selected environments.
Aperture measurement based on a conic invariant

With the advantages of low cost, universal applicability, and in situ detection, the conic invariant of the tested aperture diameter was obtained by ellipse geometric fitting on the aperture surface. The world coordinate system was established, and the transformation relationship between the world coordinate system and the camera coordinate system was obtained by the calibration. The improved Candy algorithm and coordinate transformation relationship could be used to project the sub-pixel coordinates of the aperture edge onto the aperture surface. Then, the ellipse geometric fitting was performed on the aperture edge curve surface to obtain the conic invariant. Finally, the conic invariant was used to measure the aperture diameter on the test bench.