Reviewerly/exHarmony huggingface dataset:
Best Selection of Generative Adversarial Networks Hyper-Parameters using Genetic Algorithm

Abstract Generative Adversarial Networks (GANs) are most popular generative frameworks that have achieved compelling performance. They follow an adversarial approach where two deep models generator and discriminator compete with each other In this paper, we propose a Generative Adversarial Network with best hyper-parameters selection to generate fake images for digits number 1 to 9 with generator and train discriminator to decide whereas the generated images are fake or true. Using Genetic Algorithm technique to adapt GAN hyper-parameters, the final method is named GANGA:Generative Adversarial Network with Genetic Algorithm. Anaconda environment with tensorflow library facilitates was used, python as programming language also used with needed libraries. The implementation was done using MNIST dataset to validate our work. The proposed method is to let Genetic algorithm to choose best values of hyper-parameters depending on minimizing a cost function such as a loss function or maximizing accuracy function. GA was used to select values of Learning rate, Batch normalization, Number of neurons and a parameter of Dropout layer.
Understandability in class diagrams

Understandability of class diagrams is a quality attribute that should be evaluated, due to the importance of class diagrams for the design of software products and for product management when continuous improvement is of importance for an organization. However, the evaluation of class diagrams is a difficult task if the understandability concept itself is not understood. Furthermore, software products most of the times are evaluated after the system is finished. This work proposes an understandability quality model that is reusable, extensible and customizable. It may be used as a guide to evaluate the class diagrams before starting the implementation phase of software systems. After the validation of the model, it was found that the model is highly prescriptive so areas for improvement in the class diagrams are detected straightforward.
Optimized Transmission of Encrypted Images over Wireless Channels

The transmission of images over wireless channels is increasing with advances in wireless communication technologies. However, ensuring secure and efficient transmission is a major challenge because intrusive receivers may be present, wireless channels have high bit error rates and limited bandwidth. This paper proposes a wireless transmission scheme for encrypted images to establish reliable and robust communications. The proposed scheme comprises on the transmitter side, encryption, compression, channel coding and modulation, and their inverse processes at the receiver. The Discrete Wavelet Transformed and the Huffman coding are employed for image compression, whereas convolutional coding is used as a technique for error correction. The system is implemented with the USRP NI-2920 modules and LabVIEW Communications 2.0 software, allowing the transmission of encrypted images both in real and simulated environments. The results show that the proposed system is robust and efficient against channel degradation.
Supporting Online Teaching Laboratories with Semantic Web

This paper presents an ontological design to locate laboratory resources, such as videos, documents, instruments, devices, etc., facilitating collaboration and user customization. A semantic based online laboratory is presented, which gives students and instructors a common access point that can be accessed anytime, anywhere, and which can be customized for user preferences, profile privileges and access limitations. The use of ontologies is the basic mechanism which allows devices and instruments to be defined semantically, rather than by the use of technical concepts. This model can be used in any teaching laboratory. A case study in a networking course is presented; in this, undergraduate students can remotely control instruments and devices. The effectiveness of our approach has been measured and evaluated through usability methods.
A Document Processing Scheme for Journal Submissions Based on Locality Sensitive Hashing and Scale-invariant Feature Transform

This paper proposes a blockchain-based document processing scheme that can be used in the journal submission system. The Hash algorithm and Merkle tree are used to realize the immutability of information; the interplanetary file system (IPFS) and asymmetric encryption are used to realize document storage and sharing. Through the Local Sensitive Hash Algorithm (LSH) and SIFT algorithm to achieve multiple submission recognition. The solution has the characteristics of decentralization, security and credibility, and cannot be tampered with at will. It can realize multi-platform data sharing and decentralized processing without revealing the original text, and can realize the identification of multiple submissions of the same paper in different journals.
The User Experience effect of Applying Floating Action Button (FAB) into Augmented Reality Anatomy Cranium Media Learning Prototype

A learning media's success is determined by two factors, namely the instructional design and user interface design. Most of the existing AR anatomy-based teaching media still do not pay attention to the learning media's UI and UX design. Elements such as navigation menus, text, icons, buttons, and others still accumulate, leading to the interface feel dense. This problem also can shift the user's attention when using this learning media. We tried to apply the FAB (Floating Action Button) to high-fidelity AR-based anatomy learning media prototype to overcome this. In this study, the FAB functions are as navigation and the main action of the prototype. We chose the FAB as a primary action and navigation since its ability to attract user attention and expand and narrow itself. FAB is recommended as navigation if the user needs to access certain parts or application functionality quickly regardless of hierarchy and limited navigation space. To find out the effect of user experience on FAB in this AR-based teaching media. Researchers use UEQ (User Experience Questionnaire). The results of the research show that the prototype using FAB produces three scales at the “above average” level, two scales at the “Good” level, and one scale is at the “excellent” level. Overall, the resulting scale shows pretty good results.
An Evaluation of Security Key in Mobile Cloud

The quick advancement of distributed computing and portable registering trigger novel figuring worldview. Versatile Cloud Computing as unique of the significant parts of distributed computing. As per the present circumstance there is a tremendous expanding use of portable registering, misusing its troublesome because of its natural issues, for example, visit detachments, portability, and asset shortage. Portable Cloud Computing furnishes versatile clients with information stockpiling and handling administrations on a distributed computing stage. MCC can be characterized as a mix of versatile web and distributed computing, which is the utmost well known apparatus for portable clients to get to requests and administrations on the Internet. Right now, the particular worries in portable distributed computing. We give a broad review of portable distributed computing research.
On Broadcast in Generalized Network and Adversarial Models.

Broadcast is a primitive which allows a specific party to distribute a message consistently among n parties, even if up to t parties exhibit malicious behaviour. In the classical model with a complete network of bilateral authenticated channels, the seminal result of Pease et al. [Pease et al., 1980] shows that broadcast is achievable if and only if t < n/3. There are two generalizations suggested for the broadcast problem - with respect to the adversarial model and the communication model. Fitzi and Maurer [Fitzi and Maurer, 1998] consider a (non-threshold) general adversary that is characterized by the subsets of parties that could be corrupted, and show that broadcast can be realized from bilateral channels if and only if the union of no three possible corrupted sets equals the entire set of n parties. On the other hand, Considine et al. [Considine et al., 2005] extend the standard model of bilateral channels with the existence of b-minicast channels that allow to locally broadcast among any subset of b parties; the authors show that in this enhanced model of communication, secure broadcast tolerating up to t corrupted parties is possible if and only if t < (b-1)/(b+1)n. These generalizations are unified in the work by Raykov [Raykov P., 2015], where a tight condition on the possible corrupted sets is presented such that broadcast is achievable from a complete set of b-minicasts.This paper investigates the achievability of broadcast in general networks, i.e., networks where only some subsets of minicast channels may be available, thereby addressing open problems posed in [Jaffe et al., 2012; Raykov P., 2015]. To that end, we propose a hierarchy over all possible general adversaries, and identify for each class of general adversaries 1) a set of minicast channels that are necessary to achieve broadcast and 2) a set of minicast channels that are sufficient to achieve broadcast. In particular, this allows us to derive bounds on the amount of b-minicasts that are necessary and that suffice towards constructing broadcast in general b-minicast networks.
Causal Sufficiency and Actual Causation

Pearl opened the door to formally defining actual causation using causal models. His approach rests on two strategies: first, capturing the widespread intuition that X=x causes Y=y iff X=x is a Necessary Element of a Sufficient Set for Y=y, and second, showing that his definition gives intuitive answers on a wide set of problem cases. This inspired dozens of variations of his definition of actual causation, the most prominent of which are due to Halpern & Pearl. Yet all of them ignore Pearl's first strategy, and the second strategy taken by itself is unable to deliver a consensus. This paper offers a way out by going back to the first strategy: it offers six formal definitions of causal sufficiency and two interpretations of necessity. Combining the two gives twelve new definitions of actual causation. Several interesting results about these definitions and their relation to the various Halpern & Pearl definitions are presented. Afterwards the second strategy is evaluated as well. In order to maximize neutrality, the paper relies mostly on the examples and intuitions of Halpern & Pearl. One definition comes out as being superior to all others, and is therefore suggested as a new definition of actual causation.
Speaker Recognition Using Mel Frequency Cepstral Coefficient and Self-Organising Fuzzy Logic

Speaker recognition is a biometric technique based on the voice characteristics. Mel Frequency Cepstral Coefficients (MFCC) is one of the methods that used for extracting the features on speaker recognition system, where this method turn the voice into features characteristics that imitating the characteristics of human hearing. Self-Organising Fuzzy Logic (SOF) is a development of the fuzzy logic method. This paper discusses the combination of MFCC and the offline stage of SOF for increasing the accuracy of the system. 10 peoples are pronouncing the same keyword for text-dependent system, 300 voice data from 10 peoples consisting of 30 data for each people was used. After the data has been extracted at MFCC and normalized, the data was multiplied by the SOF covariance matrix for each class, and to obtain the final result K-Nearest Neighbour (KNN) was chosen. The accuracy performance of the system achieved 97,15%.
Unsupervised Transport Vehicle Control: Simulation Study and Performance Results

This paper presents current results of the research, aimed at the developing of motion control algorithms and systems for unsupervised electric vehicles, and discusses the results of the computer simulations and necessary parameters. The task of current research is to develop immune neural network based system structure and computer model for analyzing the situation and minimizing the collision probability by changing electrical vehicles movement parameters, to develop the computer model and to find appropriate parameters for best simulation results.
Chaotic wireless communication and related network information security inversion technology

As a new wireless communication technology, chaotic wireless communication mainly includes chaotic signal transmitting part, chaotic channel part and corresponding chaotic signal receiving part. Compared with the traditional wireless communication technology, chaotic wireless communication has stronger anti-interference ability and higher communication efficiency. However, the current chaotic wireless communication technology still has the problem of information security, and the corresponding information is easy to leak in the transmission process. In order to solve the problem of chaotic wireless network and its network information security, this paper proposes an information security inversion technology based on time series. The main technical contents include chaotic signal detection technology, chaotic channel modeling, chaotic signal interference channel problem analysis and corresponding precoding technology. In this paper, the inversion technology proposed in this paper mainly uses the focusing characteristics of time inversion technology to realize the short-term energy accumulation, and realizes the reduction of inter user interference and common channel interference through pinch algorithm. Experimental results show that the proposed inversion technique can reduce the bit error rate (BER) of chaotic wireless communication and significantly reduce the signal-to-noise ratio (SNR).
VIRTUAL LEARNING IN TIMES OF COVID19 CRISIS – ASSESSING AUGMENTED REALITY RESOURCES FOR STUDENT ENGAGEMENT AND LEARNING UNDER LOCKDOWN

VIRTUAL LEARNING IN TIMES OF COVID19 CRISIS – ASSESSING AUGMENTED REALITY RESOURCES FOR STUDENT ENGAGEMENT AND LEARNING UNDER LOCKDOWN
Towards a New Approach for Building Trust and Confidence in Machine Learning Data and Models: Based on an Integration of Artificial Intelligence and Blockchain (Part 1)

Sentiments and opinions trends analysis and study are two important disciplines of Big Data exploitation. In our case related to Business Intelligence (BI), they make it possible to detect and extract relevant information from raw unstructured textual data, which we call: Textual Business Intelligence (TBI). It is in this context that we propose a new approach allowing to build trust and confidence in machine learning data and therefore in models: based on an integration and a convergence of artificial intelligence and Blockchain. This first part of the article will be dedicated to the motivation and the state of the art.
Penerapan Aplikasi Resep Makanan Khas Toba Berbasis Android

At this time, smartphone technology is not only a medium for making calls, but also takes advantage of existing features, for example cameras, games and other applications. Indonesian cuisine is a reflection of the diverse cultures and traditions originating in the archipelago of the archipelago which consists of about 6000 islands and holds great importance in national culture. With the design of a mobile application that will be made and with the application that is expected to be able to help get information in the form of Indonesian specialties, for example the typical Toba food recipe which is also accompanied by various cooking tips from the database based on the categories needed by the user. When compared to a web browser, the estimation of costs and time is less due to access via a web browser which takes a long time because search results are not immediately obtained. So that people who like to cook don't need to be confused to be able to cook and serve Batak specialties that are healthy, tasty and delicious even though they are busy with their work.
Multidirection Object Detection in Aerial View of Traffic Target under Complex Scenes

Focusing on DOTA, the multidirectional object dataset in aerial view of vehicles, CMDTD has been proposed. The reason why it is difficult for applying the general object detection algorithm in multidirectional object detection has been analyzed in this paper. Based on this, the detection principle of CMDTD including its backbone network and multidirectional multi-information detection end module has been studied. In addition, in view of the complexity of the scene faced by aerial view of vehicles, a unique data expansion method is proposed. At last, three datasets have been experimented using the CMDTD algorithm, proving that the cascaded multidirectional object detection algorithm with high effectiveness is superior to other methods.
DIALOGUE STRATEGY OF INTERACTION IN THE CONTEXT OF FORMING THE EXPERIENCE OF INTERCULTURAL COMMUNICATION OF THE FUTURE MUSIC TEACHER

The problem of improving the quality of training of future teachers of art disciplines in the context of paradigmatic changes in the system of modern art education is actualized. The state of research of the scientific problem is clarified, the worldview positions of researchers related to the specifics of intercultural communication as a form of exchange of cultural meanings between the subjects of the educational process are singled out. The peculiarities of creating a dialogic field of intersubjective relations of students of faculties of arts in the music-pedagogical process of higher education, which is based on the principle of value interaction of representatives of different cultures, are revealed. The author focuses on the introduction of ideas of intercultural communication in the educational environment of higher education, which will promote the development of students' ability to understand the value of other cultures on the basis of tolerance, the formation of common meaning in dialogue between different activitiesKey words: dialogue strategy; artistic polysubjective communication, experience of intercultural communication, experience of intercultural communication, future teacher of music art.
Щодо мисленевого простору

The article examines some aspects of the mental space, the psychic one, and the internal space of thinking. The psychic space is not considered a “psychic reflection of the sensory stimuli” insofar as the first psychic movements that presuppose psychic space are not related to the sensory (for example, the prenatal states). The psychic space arises at the same time as the very psychic phenomenon. Some forms of changes in the mental space, and in the internal space of thinking, as well as the emergence of self-intentional states are also considered
JEDDAH MARINA YACHT

The proposed Jeddah Marina Yacht will contribute for the boat owners and marine enthusiasts in the city. The club has a range of facilities designed specifically for boat owners and yachts that meet their needs at all levels. The Yacht Club is expected to become the favourite lifestyle destination for boat owners in the city. The club provides its members and visitors with various facilities including broker offices, major outlets, shops, souvenir stores, yacht sailing shops. The Yacht Club also has a range of exclusive facilities including, a library, a private terrace and a coffee shop, making the club an ideal location to spend time with friends. Business guests can benefit from the club's meeting and conference facilities, equipped with state-of-the-art technology and equipment, as well as a private lounge for receptions and events overlooking the waterfront. The development of water games city will be established in the site to serve the residential and tourists also stimulates the services of construction and maintenance works on all types of small boats and yachts.
PERBANDINGAN ANALISIS DATA FITUR NOMINAL MULTI-KATEGORI MENGGUNAKAN METODE ADAPTIVE SYNTHETIC NOMINAL (ADASYN-N) SERTA ADAPTIVE SYNTHETIC-KNN (ADASYN-KNN)

Growing need for efficient algorithms for data manipulation, analysis, and intelligent use has been a very active research area in machine learning field. However, some research areas still not fully developed, especially when unbalanced data classification is needed. Datasets with this class imbalance occur because of an unbalanced ratio between one case and another. This class imbalance will be detrimental to data mining because machine learning in data mining has difficulty in classifying minority classes (small instances) correctly. There are several approaches to handling imbalances, one of which is by using the original data sampling method. The first sampling method approach to overcome class imbalance is undersampling which is a method to balance classes by randomly reducing the majority class instances. Over-sampling is a method of balancing class distribution by randomly replicating instances in minority classes. This study presents comparison of over-sampling techniques to overcome problem of class imbalances in datasets with nominal-multi categories features between Adaptive Synthetic-Nominal (ADASYN-N) and Adaptive Synthetic-kNN (ADASYN-KNN) methods. There are seven datasets with nominal-multi categories features which have an unbalanced class distribution. Then the dataset that has been over-sampled with both methods is classified using the Random Forest method. Furthermore, a comparison of the accuracy of the original dataset and the dataset of the ADASYN-N and ADASYN-KNN over-sampling techniques was carried out. Keywords: ADASYN-KNN , ADASYN-N, class imbalance, nominal, multi-category, over-sampling.
The use of ellipse-based estimator as a sub-key distinguisher for Side-Channel Analysis

Side-Channel Analysis has become a relevant tool to analyze a cryptographic device. Here, an adversary looks for information leakage from emanation sources such as power consumption, thus obtaining sensitive information with a lower effort than the mathematical approach. In this manuscript, a distinguisher based on the Mahalanobis distance is applied. Instead of computing an inverse covariance matrix because of lack-sampling problems with tested datasets, a shrinkage calculation is implemented, thus obtaining efficient Mahalanobis distance implementations. The current approach is evaluated using different standardized tests such as stability and success probability, which are computed using unmasked public traces obtained from the Advanced Encryption Standard’s typical implementations with a 128-bit key. We show that this technique’s efficiency is better than Pearson correlation using few traces in terms of probability detection.
Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing  Vertical and Horizontal Convolutions

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architecture has been proposed and widely used, but its performance remains still unsatisfactory. To further cope with these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations: 1) Since the discrimination of features learned via square convolutional kernels needs to be further improved, we propose to utilize non-square vertical and horizontal convolutional kernels in the double-branch encoder, so features learned by the two branches can be expected to complement each other. 2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation on small-sized targets. Together, the above two schemes give rise to a novel double-branch encoder segmentation framework for medical image segmentation, namely Crosslink-Net. The experiments validate the effectiveness of our model on four datasets. The code is released at https://github.com/Qianyu1226/Crosslink-Net.