Reviewerly/exHarmony huggingface dataset:
A Journey From Neural Networks to Deep Networks

The chapter is about deep learning fundaments and its recent trends. The chapter mentions many advanced applications and deep learning models and networks to easily solve those applications in a very smart way. Discussion of some techniques for computer vision problem and how to solve with deep learning approach are included. After taking fundamental knowledge of the background theory, one can create or solve applications. The current state-of-the-art of deep learning for education, healthcare, agriculture, industrial, organizations, and research and development applications are very fast growing. The chapter is about types of learning in a deep learning approach, what kind of data set one can be required, and what kind of hardware facility is required for the particular complex problem. For unsupervised learning problems, Deep learning algorithms have been designed, but in the same way Deep learning is also solving the supervised learning problems for a wide variety of tasks.
Integrated Early Warning System for Molten Metal Splash Accidents Based on Internet of Things

Today, early warning for molten metal splash accidents often uses artificial method. However, artificial early warning method has low monitoring efficiency and high risk, which has not met the safety needs nowadays. On the other hand, with the development of emerging technologies such as the Internet of Things and artificial intelligence, it is possible to realize automated integrated early warning. In this paper, from the two aspects of “parameter warning” and “behavior warning”, an integrated early warning system for molten metal splash accident based on Internet of Things was proposed. This paper provides a detailed introduction to the system architecture, system functions, and implementation methods. The system is intelligent and efficient, which can guarantee the safety of molten metal operations.
Adaptive Confidence Filter Update for High Uncertainty Environments

It is known that the commonly used extended Kalman filter (EKF) is prone to divergence in the presence of large updates. In light of the demands of upcoming spaceflight missions, this paper proposes a confidence-based method as an alternative for estimation in highly uncertain environments. In order to test the effectiveness of the proposed update, simulations are created wherein the performance of the proposed update is compared to an EKF equipped with an underweighting scheme, as well as to a second order EKF. Results of the simulations indicate the confidence-based filter as being more robust in select cases than the underweighted filter, without being overly conservative.
Program Design Criteria and Simple Design Models

Three program design criteria—separation of concerns, design for reuse, and design only what is needed—are described. As an experienced programmer, it is expected that you’ve seen these three program design criteria before. This is followed by an introduction to four types of design models. Two design models are described that represent the structure of the solution. A hierarchy chart is typically used to describe a structured solution while a class diagram is used to describe an object-oriented solution. Two design models are described that represent the behavior of the solution. Both of the behavior models—Nassi–Shneiderman diagram and statechart—may be used to describe a structured or object-oriented solution.
GISKOP

Cryptography is a method of storing and transmitting data in a particular form. Only those for whom it is intended can read, use it, and return it back to the original data by using various techniques. The International Data Encryption Algorithm "IDEA" is a block cipher that works with 64-bit plaintext block and ciphertext blocks and it has a 128-bit input key. This paper describe the designing and implementation of a modified key schedule operation of IDEA called GISKOP. It uses the same number of rounds and output transformation that operates using 128 bit user input plaintext and a modified way of key scheduling operation of 256 bit keys. The modified algorithm uses Serpent key scheduling operation to derive the different sub keys to be used in each rounds. The algorithm was implemented to provide better security on user's password within the Document Management System to protect user's data within the cloud database. It has gone through initial testing and evaluations with very encouraging results.
A Recommender Approach for an E-Learning Platform Based on Social Network Analysis and Collaborative Filtering

Improving the teaching process in the e-learning platforms is a challenging job, several works in recent years trying to find new methods and approaches to make the learning operation an easy task for all kind of learners. The proposed methods must take into account the difference in learners’ profiles and also the capacity of learning of each learner. In this paper, we proposed a new approach for defining the best way in which the learner must learn, our approach is based on social network analysis (sentiment analysis) and collaborative filtering using the social networks profiles of learners (Facebook and Twitter) and their knowledge.
Adversarial Query-by-Image Video Retrieval Based on Attention Mechanism

The query-by-image video retrieval (QBIVR) is a difficult feature matching task across different modalities. More and more retrieval tasks require indexing the videos containing the activities in the image, which makes extracting meaningful spatio-temporal video features crucial. In this paper, we propose an approach based on adversarial learning, termed Adversarial Image-to-Video (AIV) approach. To capture the temporal pattern of videos, we utilize temporal regions likely to contain activities via fully-convolutional 3D ConvNet features, and then obtain the video bag features by 3D RoI Pooling. To solve mismatch issue with image vector features and identify the importances of information for videos, we add a Multiple Instance Learning (MIL) module to assign different weights to each temporal information in video bags. Moreover, we utilize the triplet loss to distinguish different semantic categorites and support intraclass variability of images and videos. Specially, our AIV proposes modality loss as an adversary to the triplet loss in the adversarial learning. The interplay between two losses jointly bridges the domain gap across different modalities. Extensive experiments on two widely used datasets verify the effectiveness of our proposed methods as compared with other methods.
Helicopter Collision Avoidance Algorithm for Automatic Hovering

One of the major accident causes of helicopter is collision into terrain and into near-ground obstacles. As a countermeasure to such collision risk, collision avoidance system especially for low level flight is indispensable. In this research, the obstacle alert system and the collision avoidance algorithm was developed to be integrated into AFCS. Results of evaluation using a simulator showed that the algorithm can prevent helicopter from collision with realistic level of autopilot authority. In the flight experiment conducted at Taiki Multipurpose Airfield, obstacle alert message was displayed as expected, although there was problem to be improved.
Unsupervised Feature Selection Using Information-Theoretic Graph-Based Approach

FeatureKundu, Sagarika Saroj Das, Abhirup Das, Amit Kumar selection is a critical part of any machine learning project involving data sets with high dimensionality. Selecting n optimal subset consisting of important features reduces the execution time and increases the predictive ability of the machine learning model. This paper presents a novel graph-based feature selection algorithm for unsupervised learning. Unlike many of the algorithms using correlation as a measure of dependency between features, the proposed algorithm derives feature dependency using information-theoretic approach. The proposed algorithm—Graph-based Information-Theoretic Approach for Unsupervised Feature Selection (GITAUFS) generates multiple minimal vertex covers (MVC) of the feature graph and evaluates them to find the most optimal one in context of the learning task. In our experimental setup comprising 13 benchmark data sets, GITAUFS has shown a 10% increase in the silhouette width value along with a significant feature reduction of 90.62% compared to the next best performing algorithm.
An Extended Playfair Encryption Technique Based on Fibonacci Series

The rapid advancement in networking technologies leads to the sharing of information by millions of users at a much higher rate. Information sharing over unprotected network may lead to compromise of the sensitive and confidential information to unauthorized person. Cryptography is one of the several ways to guard sensitive and confidential information. Before sharing the information over the Internet, it must be encrypted to preserve its confidentiality. Encryption is a process of converting readable information into scrambled form so that no unauthorized person can make use of it. In this paper, the Playfair encryption technique is taken into consideration for encrypting the information to be shared. In its simplest form, the Playfair encryption technique generates a 5 × 5 key table by taking a key as input and encrypts the diagraphs of actual message. The key generation process is improved by modifying it with Fibonacci series. The Fibonacci series is used create a random key, which is passed to generate 5 × 5 key table. This key table is used to encrypt the actual message using rules defined by Playfair encryption algorithm. In this paper, the limitations of 5 × 5 key table are removed by using an 8 × 8 key table, which provides much higher level of security to the message being encrypted.
A Latent Variable CRF Model for Labeling Prediction

A latent variable conditional random fields (CRF) model is proposed to improve sequence labeling, which utilizes the BIO encoding schema as latent variable to capture the latent structure of hidden variables and observation data. The proposed model automatically selects the best encoding schema for each given input sequence. Through experimentation, it is demonstrated that the proposed model unveils the latent variable while performing robustly on sequence-labeling prediction tasks.
Cross-modal retrieval by an end to end way

Abstract Cross-modal retrieval has attracted most attention in the recent years. For the image and text, how to measure the semantic similarity among them is still a challenging problem in cross-modal retrieval task. In our paper, we propose a cross-modal retrieval method that uses a CNN to obtain the semantic similarity between image and text. Most methods to solve this problem employ two separate parts for each modality to obtain the semantic similarity between them. However, in our work we just use a CNN to get the semantic similarity without having to use separate networks. In addition, we are aiming to solve the problem between long text and image, so we use the topic model to process the text. We evaluate our approach on Wikepedia dataset.
Hangar Detection from Satellite Images with Mask-RCNN and YOLOv2 Algorithms

The contribution of this paper is twofold. It first proposes a new dataset of high resolution satellite images of hangars located at civil and military airports. It also presents a hangar detection problem results from satellite images using this new dataset obtained by Mask R-CNN and YOLOv2 algorithms. The satellite dataset contains one thousand pictures obtained from Google Earth at 8, 11, 17, 29 degrees angles from height of 500,800 and 1000 meters. Among 1000 hangar images 650 and 200 images are used for training and validation, respectively, while the remaining 150 images are used for detection test purposes. The total number of hangar object instants in the dataset images is about 3000. The detection of hangars is a challenging problem as the dataset contains camouflaged and non-camouflaged targets in different sizes. Among the two approaches used in the detection problem Mask R-CNN utilizes a regional based algorithm and enables instance segmentation with a bounding box. YOLOv2, on the other hand, is a regression based algorithm, used in real-time applications, and provides a bounding box only. The object detection accuracy in terms of Average Precisions by using Mask R-CNN and YOLOv2 algorithms to detect different sized camouflaged and non-camouflaged hangar objects was obtained as 72% and 74%, respectively.
Voting-Based Multiple Classification Approach for Turkish News Texts

Nowadays, there are numerous sources on the internet that produce news on a daily basis. Through this growing knowledge base, it makes it difficult for users to access the information and news they are looking for. It is important to classify the information for fast and efficient search and access. In this study, a dataset consisting of Turkish news content Kemik prepared by Yıldız Technical University, Natural Language Processing Group, used. A hierarchical approach based on a voting structure is adopted by using machine learning based approaches. In order to solve the problem, firstly Tf-Idf method is applied for word 1-3- ngrams and character 2-6-ngrams. Thus, the 2000 dimensional feature vector is pre-trained. By using FastText, 300-dimensional feature vectors and 2 feature vectors are combined to produce 2300-dimensional feature vectors.. In order to determine the one that will increase the classification accuracy among these vectors, Support Vector Machines method is applied and Tf-Idf method which has the robust accuracy is determined as the main feature extraction method. Next, Support Vector Machines, K-Nearest Neighborhood Method, Random Forest, Logistic Regression, XGBoost methods are used for the classification of news texts. Estimated label values from all classifiers are voted for each sample and the label with the highest voting rate is considered as the final estimate. In this study, it is aimed to open the way to reach the right information quickly by classifying news topics. Finally, the feature vector size has been reduced using Principal Component Analysis and it is possible to gain processing speed without reducing performance. In both approaches, it is seen that the performance achieved by voting is higher than the individual performance rates of the classifiers.
Research on Dynamic Decision-making of Post-disaster Rescue Based on GCC Framework

Collective adaptive systems (CAS) have been attracting increasing attention in the field of artificial intelligence (AI), in which collaboration of agents plays a key role. These systems aim to accomplish a certain goal though collaborating between a variety of agents with different tasks, which adapt to changes of environment to be of adaptability. To solve the issue of collaboration of agents in an uncertain and highly dynamic environment, our research team had proposed a Goal-Capability-Commitment (GCC) based mediation for multi-agent collaboration, which generates the collaboration planning driven by capability based on global context states in real-time dynamic environment. As a case study for the application of GCC model, this paper adopts GCC to model the RoboCup Rescue Simulation System (RCRSS). As the result of modelling, the GCC domain model is applied to RCRSS where an efficiently quantitative evaluation is provided.
Towards Secure Computation of Similar Patient Query on Genomic Data Under Multiple Keys

Genomics plays an especial role in our daily lives. Genomic data, however, are highly-sensitive and thus normally stored in repositories with strict access control insurance. This severely restricts the associated processing on genomic data, in which multiple institutes holding their own data hope to conduct specific computation on the entire dataset. Accordingly, researchers attempt to propose methods to enable secure computation on genomic data among multiple parties. Nevertheless, most of the existing solutions fall short in efficiency, security or scalability. In this paper, we focus on providing a secure and practical solution to perform similar patient query on distributed Electronic Health Records (EHR) databases with genomic data. To achieve this, we propose a privacy-preserving framework to execute similar patient query on genomic data owned by distributed owners in a server-aided setting. Specifically, we apply multi-key homomorphic encryption to the proposed framework, where each data owner performs queries on its local EHR database, encrypts query results with its unique public key, and sends them to the servers for further secure edit-distance computation on genomic data encrypted under multiple keys. Security and performance analysis show that our system achieves satisfactory efficiency, scalability, and flexibility while protecting the privacy of each data contributor.
Analyzing Natural Language Context in Human-Machine Teaming using Supervised Machine Learning

Building a foundation for trustworthiness and trust verification in multi-asset teaming is the research challenge of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR). The Design Reference Mission (DRM) for ATTRACTOR is a search and rescue mission objective governed by a multi-member team consisting of human and machine operators. A crucial component to the effort is the communication between humans and autonomous agents throughout both planning and execution stages of the mission. Intuitive communication methods and modalities are posited as critical enablers for certifying trust and trustworthiness. This paper reports on the data collection and analysis conducted in support of the Human Informed Natural-language GANs Evaluation (HINGE) project to attain explainable and trusted communication between human-machine assets. Two identically curated image description datasets were acquired for HINGE, both consisting of two unique input modalities (typed vs. verbal) and retrieved in two distinct contexts (general vs. specific). The gathered datasets were assessed and compared using Parts-of-Speech (POS) features, sentence similarity metrics, and linguistic analysis. Then, the datasets were modeled and tested separately and in combination with one another using machine learning algorithms. The comparison and testing results reveal a superior dataset, by which a preferred context and input is understood, for generating image representations of missing persons using a Generative Adversarial Network (GAN).
The Evolution of Imperative Programming Paradigms as a Search for New Ways to Reduce Code Duplication

Abstract The cause for imperative programming paradigm shift is the impossibility of developing software systems of a new level of complexity. We consider the evolution of programming paradigms: structured, procedural, and object-oriented. We demonstrate new ways of code duplication reducing have appeared with the shift of paradigm. We conclude the factor of code duplication reducing determines the direction of programming paradigm evolution. We discover the constraints, which were introduced in the paradigms, simplify the development of software systems. We conclude the new constraints allow the development of more complex software systems. The main reason for the code duplication is the low qualification of programmers. Therefore, in the process of learning programming, one should pay attention to code duplication and ways to reduce it.
Support Vector Machine Pre-pruning Approaches on Decision Trees for Better Classification

Incorporation of the structural risk minimization of Support Vector Machine to pre-prune the decision trees based on empirical risk minimization is conducted to develop a combined algorithm. It is named as Support Vector Machine Pruned Decision Trees (SVMPDT) algorithm. Pre-pruning of decision trees (DT) is applied to the datasets through the synergistically adjusted regularization parameter of SVM. This is done by the proposed new approach derived from the study on the synergy effects between the pre-pruning weighting fraction of DT and the regularization parameter of SVM. The regularization parameter of SVM is customized and adjusted based on the different features and characteristics of DT from each applied dataset. After applying the proposed algorithms to the assigned datasets, it is shown to be more accurate in classification when compared with typical SVM without getting its parameter adjusted accordingly and the typical DT classification without applying pre-pruned weighting fraction as well as the default SVMDT algorithms without getting the DT to be pre-pruned. This is because its regularization parameter of SVM can be optimally adjusted with the newly proposed formulations on the pre-pruned weighting fraction of DT in a synergy way such that the classification accuracies can significantly be improved.
Prospects and development of mixed reality technologies

In this article, discusses the gadgets of companies that are the main representatives of the technology of mixed reality. Comparison of technologies was carried out and their differences were determined both in technical design and in the field of application. Comparison of technical specifications and the scope of application for each of the representatives, taking into account the capabilities and support of the software. Microsoft HoloLens was the first working representative of mixed reality. Due to the fact that he worked on the operating system of Windows Holographic, he received the widest application, as far as this personal computer is attached to your head. You can identify the scope of application for each of the devices. The most widely used at the moment is HoloLens, because it works on one variation of the Windows operating system, which allows you to use all commonly used programs in mixed reality mode. Use is available in all areas where necessary from the use of doctors during or during the preparation of the operation, and for the developers of any graphic elements, designers and architects. It supports a full set of programs running on this platform. Meta 2 is a device, to work with which it is necessary to have a sufficiently powerful personal computer, as far as the device itself is used as a monitor, only in mixed reality. Meta 2 for the moment, can be used in conjunction with Microsoft Office, Adobe Creative Suite, and Spotify. Although the list of applications is currently limited, it can now be used to create layouts and designs. And Adobe Creative Suite support allows you to use them to create media content and instantly mix it with the real world. Magic Leap One is very similar to Microsoft HoloLens, also an independent device that does not require additional connection to a PC. Magic Leap One - At the moment, with the support of a small amount of software, this device allows you to arrange online conferences between the users of Magic Leap One anywhere in the world. Allows you to design your own world through Project Create, manage the created elements, and organize the interaction between them. At the moment, it has limited scope due to small software, due to the use of its own operating system. It is noted that compared to the technology of virtual reality, mixed reality allows you to complement the real world with digital content, without loss of visual contact with others.