Reviewerly/exHarmony huggingface dataset:
Making Noisy Quantum Channels Transparent

One of the major obstacles faced by quantum-enabled technology is the environmental noise that causes decoherence in the quantum system, thereby destroying much of its quantum aspects and introduces errors while the system undergoes quantum operations and processing. A number of techniques have been invented to mitigate the environmental effects; however, much of these techniques are specific to the environment and the quantum tasks at hand, limiting their applicability. Here we propose a protocol that makes arbitrary environments effectively noise-free or transparent. The protocol exploits non-local superposition in evolution as a quantum resource. Consequently, it enables full protection of quantum information and entanglement from decoherence, and perfect quantum communications across arbitrary noisy channels for any finite-dimensional quantum system. We also propose experimental schemes to implement this protocol on linear optical and atomic systems.
CBIR using content frequency and colour features

Due to the diversity of the image content, we propose in this study a new technique for content-based image retrieval (CBIR) to characterise the image. In this scenario, all images are characterised by their frequency content and their colour information. Indeed, using the high resolution spectral analysis methods, especially the 2D estimation of signal parameters via rotational invariance techniques (ESPRIT) we extract from the image its content frequency and with statistical moment its colour information in order to construct a new vector descriptor. The experimental results applied to the coil-100 database show the robustness of our approach, the precision average can be reaches 90.57%.
DEVELOPMENT OF THE PAGARALAM COLLEGE OF TECHNOLOGY OF COMPUTER NETWORK TO ACCOMMODATE DIGITAL CAMPUS

The use of information and communication technology is very important in supporting all daily human activities. In the world of education, information and communication technology has a very important role or benefit because many activities in the world of education take advantage of this technology. Pagaralam College of Technology is one of the colleges utilizing information and communication technology, computer networks, but the existing network conditions are not optimal so that the quality needs to be improved to make it better. Also with the increasing competition in higher education management, it is very important for a college to think about how the institution can survive and be superior in determining its competitiveness with other colleges so that the digitization of the campus or the creation of a digital campus is one of the strategies that must be implemented in the face of development existing information and communication technology. This study aims to improve the quality of computer networks to prepare the Pagaralam College of Technology to accommodate the need to become a digital campus, using SWOT analysis methods and TopDown Network Design, network quality testing, or Quality of Service (QoS), and development methods using PPDIOO. (Prepare, Plan, Design, Implement, Operate, Optimize). This research results in a better or more reliable new computer network design or blueprints to realize or accommodate a digital campus.
Avoiding local minima in multilayer network optimization by incremental training

Training a large multilayer neural network can present many difficulties due to the large number of useless stationary points. These points usually attract the minimization algorithm used during the training phase, which therefore results inefficient. Extending some results proposed in literature for shallow networks, we propose the mathematical characterization of a class of such stationary points that arise in deep neural networks training. Availing such a description, we are able to define an incremental training algorithm that avoids getting stuck in the region of attraction of these undesirable stationary points.
ROLE FACTORS AS A PEDAGOGICAL INNOVATION OFINTERPERSONAL INTERACTION OF CADETS

The article analyzes the problem of the role structure of cadets, the relevance of which is due to the reforms in the system of higher military education, the complexity of training programs and the increasing requirements for the training of unit commanders. It is noted that in the process of interpersonal interaction in the military team, it is assumed that certain roles will be performed. A new method for diagnosing cadets’ role models as participants in the pedagogical process is proposed, using the innovative Kaleidoscope technique, which includes 10 main roles related to the meso-level. The differentiation of roles according to three criteria is considered: activity-passivity, individualism-sociality and spontaneity-traditionalism. The purpose of the article is the disclosure of the features of the role structure of cadets as subjects of the pedagogical process in the context of interpersonal interaction. A three-factor structure was obtained, explaining more than 67.36 % of the total variability, consisting of the moral and emotional aspect of the role-playing behavior of a serviceman in professional activities, role-playing professionally significant qualities of the defender of the fatherland and the expressive-creative potential in implementing role-playing behavior in the pedagogical process. It is determined that the selected role factors reflect a contradictory unity and include, on the one hand, the role of subordination to the standards and regulations of the military community, and on the other hand, the roles that fulfill the expectations of the homeland defender, the winner, the hero, and the role of leader. It is concluded that the consideration of role factors acting at the personality level as content-informational education introduces new changes in the interaction of participants in the pedagogical process at a military university. In addition, it is noted that the role-based interactions implement the conditions for reprogramming the role profile of the participants in the pedagogical interaction, which contributes to the exit from the individual space into the meta-space.
An automated method for detecting suspicious nodes in Bitcoin address graph

Financial innovation has entered a new era with cryptocurrencies. Bitcoin is the first decentralised cryptocurrency and the most popular in the world. The main features of this new technology are immutability, decentralised trust and anonymity. Bitcoin anonymous and untraceable system facilitates cash-out and laundering of cybercrime proceeds. Bitcoin currency flow provides an address graph that assigns the flow of bitcoin between two addresses. Identifying suspicious nodes in the bitcoin network is similar to the problem of recognising the origins in the contact network of different applications, i.e., virus propagation, rumour source in social networks, and poison spread in water networks. In order to investigate money laundering in bitcoin, we proposed an automated method to identify suspicious addresses in the bitcoin address graph. We chose two centrality measures to be calculated on the graph. The measures are betweenness centrality and closeness centrality. The nodes with the highest values for the measurements are suspicious. The accuracy of the proposed method is further investigated by comparing the fraudulent candidate nodes with other scenarios. It is shown that the identified nodes are correct candidates for further investigations.
Content Modification in Operation Management Over the Last Three Decades

The objective of this research is to carry out a content analysis of operation management (OM) textbooks during the last three decades. Three sets of 11 textbooks each were included. The sets are c...
Response of Python Bivittatus Snake Keepers to the Regulation System for Life Protection

We are delighted to introduce the Virtual Conference on Social Science In Law, Political Issue And Economic Development 2020 with theme “Realizing Society 5.0 for a Better Life”. The technical program has brought researchers and practitioners around the world to a good forum for discussing, leveraging and developing all scientific and technological aspects that are relevant to economic business, education and humanities. Moreover, it is a great pleasure to have the keynote and invited speakers of VCOSPILED 2020, Prof. Dr. Pujiono, Assoc. Prof. Ilham Sentosa, Ph.D and Dr. Siska Diana Sari, S.H., M.H who will share their knowledge and best innovative research findings in economic business, education and humanities. This conference is held by Kresna Acitya Media Nusantara in collaboration with Universitas Muhamadiyah Sidoarjo and Relawan Jurnal Indonesia. Kresna Acitya Media Nusantara is a company that is dedicated to maximize the impact of scientific publication, and Relawan Jurnal Indonesia, is a non-profit organization in the field of scientific publication. The conference will be held virtually using Zoom webinar on October 15, 2020. This conference has successfully acquired 180 participants and 55 presenters from 4 countries, Indonesia, Malaysia, Taiwan and Uzbekistan. Thus, all selected papers will be
Exploration on the Innovative Methods of Teaching Management System in Colleges and Universities Based on the Cultivation of Innovative Talents

With the rapid development of society, the demand for talents is increasing. Therefore, as an important base for talent transportation, colleges and universities must promote the innovation of teaching mode and teaching system, and pay attention to the cultivation of innovative talents. Therefore, this paper analyzes the importance of university management system innovation, studies the problems existing in the current management system of colleges and universities in China, and explores the innovation methods of university management system from the perspective of innovative talent training.
Deep Filter Bridge For Malaria Identification And Classification In Microscopic Blood Smear Images

Malaria is a major global infectious health disease threat. The process of identifying and quantifying malaria is one of the most challenging tasks in the field of microscopy image processing due t...
ONCAR: an ontology-based approach for car automation modelling

Smart cars are vehicles conceived with technological components to ease the driving and the management of the car, and to assist drivers with safety and security, energy saving and comfort services. However, the design of these complex systems involves diverse aspects including automated behaviours and user's needs and information. A modelling language with a semantic aspect will allow a close modelisation to these requirements. Also, adequate design methods to ensure the effectiveness of such systems are needed. Yet, research to design intelligent vehicles is still limited. In this paper, we aim to offer a complete design method. For that, we define a complete ontology for car automation.
Rethinking the Evaluation of Neural Machine Translation.

The evaluation of neural machine translation systems is usually built upon generated translation of a certain decoding method (e.g., beam search) with evaluation metrics over the generated translation (e.g., BLEU). However, this evaluation framework suffers from high search errors brought by heuristic search algorithms and is limited by its nature of evaluation over one best candidate. In this paper, we propose a novel evaluation protocol, which not only avoids the effect of search errors but provides a system-level evaluation in the perspective of model ranking. In particular, our method is based on our newly proposed exact top-$k$ decoding instead of beam search. Our approach evaluates model errors by the distance between the candidate spaces scored by the references and the model respectively. Extensive experiments on WMT'14 English-German demonstrate that bad ranking ability is connected to the well-known beam search curse, and state-of-the-art Transformer models are facing serious ranking errors. By evaluating various model architectures and techniques, we provide several interesting findings. Finally, to effectively approximate the exact search algorithm with same time cost as original beam search, we present a minimum heap augmented beam search algorithm.
Partially fake it till you make it: mixing real and fake thermal images for improved object detection

In this paper we propose a novel data augmentation approach for visual content domains that have scarce training datasets, compositing synthetic 3D objects within real scenes. We show the performance of the proposed system in the context of object detection in thermal videos, a domain where 1) training datasets are very limited compared to visible spectrum datasets and 2) creating full realistic synthetic scenes is extremely cumbersome and expensive due to the difficulty in modeling the thermal properties of the materials of the scene. We compare different augmentation strategies, including state of the art approaches obtained through RL techniques, the injection of simulated data and the employment of a generative model, and study how to best combine our proposed augmentation with these other techniques.Experimental results demonstrate the effectiveness of our approach, and our single-modality detector achieves state-of-the-art results on the FLIR ADAS dataset.
Exploring real-time super-resolution generative adversarial networks

Image super-resolution is an essential technology for improving user quality of experience of internet videos. As the state-of-the-art deep learning-based super-resolution technology, the enhanced super-resolution generative adversarial networks (ESRGAN) has the best performance in the perceptual quality of reconstructed images, and the efficient sub-pixel convolutional neural network (ESPCN) has the best real-time performance. This work proposes real-time super-resolution generative adversarial network (RTSRGAN). RTSRGAN takes the advantages of ESRGAN and ESPCN so as to simultaneously satisfy the demands on the real-time performance and the resulting pleasant artifacts of super-resolution at the client side. Our experimental studies demonstrate our proposed RTSRGAN can be used for super-resolution at the client side to enhance the real-time performance as well as ensure the image perceptual quality.We also find that RTSRGAN is suitable for restoring imageswith regularly changing texture featureswithout requiring training for individual image categories.
CONVERSATIONAL IMPLICATURE IN DIALOGUE OF “LAYAR TERKEMBANG” NOVEL BY SUTAN TAKDIR ALISJAHBANA

This research deal t with Conversational Implicature in Dialogue of LayarTerkembang Novel By SutanTakdirAlisjahbana based on Grice’s Theory and it was aimed to finding out the types of conversational implicature, and to describe why the certain type of conversational implicature becomes dominant. This research was conducted by using descriptive qualitative method. The source of the data was taken from LayarTerkembang novelby SutanTakdirAlisjahbana, Then the dialogue of the novel as the data for the research. The findings showed that there were two types of conversational implicature in the procedures namely Generalized Conversational Implicature belong to 33 utterances and Particularized Conversational Implicature belong to 36 utterances. So, the most dominant types of conversational implicature between the main characters in the novel was particularized conversational implicature because it more contained ambiguity, implicit meaning, conveyed an additional meaning, and their response did not relevant with every question and also it cannot easily to understand what he/she was talking about. Keywords : Conversational Implicature; Generalized Conversational Implicature; Particularized Conversational Implicature
Using Artificial Neural Network to Provide Realistic Lifting Capacity in the Mobile Crane Simulation

Simulations are often used for training novice operators to avoid accidents, while they are still polishing their skills. To ensure the experience gained in the simulation be applicable in real-world scenarios, the simulation has to be made as realistic as possible. This paper investigated how to make the lifting capacity of a virtual mobile crane behave similarly like its real counterpart. We initially planned to use information from the load charts, which document how the lifting capacity of a mobile crane works, but the data in the load charts were very limited. To mitigate this issue, we trained an artificial neural network (ANN) using 90% of random data from two official load charts of a real mobile crane. The trained model could predict the lifting capacity based on the real-time states of the boom length, the load radius, and the counterweight of the virtual mobile crane. To evaluate the accuracy of the ANN predictions, we conducted a real-time experiment inside the simulation, where we compared the lifting capacity predicted by the ANN and the remaining 10% of the data from the load charts. The results showed that the ANN could predict the lifting capacity with small deviation rates. The deviation rates also had no significant impact on the lifting capacity, except when both boom length and load radius were approaching their maximum states. Therefore, the predicted lifting capacity generated by the ANN could be assumed to be close enough to the values in the load charts.
Development of an image segmentation model based on a convolutional neural network

This paper has considered a model of image segmentation using convolutional neural networks and studied the process efficiency based on models involving training the deep layers of convolutional neural networks. There are objective difficulties associated with determining the optimal characteristics of neural networks, so there is an issue related to retraining the neural network. Eliminating retraining by determining the optimal number of epochs only would not suffice since it does not provide high accuracy.The requirements for the set of images for training and model verification were defined. These requirements are best met by the image sets PASCAL VOC (United Kingdom) and NVIDIA-Aerial Drone (USA).It has been established that AlexNet (Canada) is a trained model and could perform image segmentation while object recognition reliability is insufficient. Therefore, there is a need to improve the efficiency of image segmentation. It is advisable to use the AlexNet architecture to build a specialized model, which, by changing the parameters and retraining some layers, would allow for a better process of image segmentation.Five models have been trained using the following parameters: learning speed, the number of epochs, optimization algorithm, the type of learning speed change, a gamma coefficient, a pre-trained model.A convolutional neural network has been developed to improve the accuracy and efficiency of image segmentation. Optimal neural network training parameters have been determined: learning speed is 0.0001, the number of epochs is 50, a gamma coefficient is 0.1, etc. An increase in accuracy by 3 % was achieved, which makes it possible to assert the correctness of the choice of the architecture for the developed network and the selection of parameters. That allows this network to be used for practical tasks related to image segmentation, in particular for devices with limited computing resources
Reducing numerical precision preserves classification accuracy in Mondrian Forests.

Mondrian Forests are a powerful data stream classification method, but their large memory footprint makes them ill-suited for low-resource platforms such as connected objects. We explored using reduced-precision floating-point representations to lower memory consumption and evaluated its effect on classification performance. We applied the Mondrian Forest implementation provided by OrpailleCC, a C++ collection of data stream algorithms, to two canonical datasets in human activity recognition: Recofit and Banos \emph{et al}. Results show that the precision of floating-point values used by tree nodes can be reduced from 64 bits to 8 bits with no significant difference in F1 score. In some cases, reduced precision was shown to improve classification performance, presumably due to its regularization effect. We conclude that numerical precision is a relevant hyperparameter in the Mondrian Forest, and that commonly-used double precision values may not be necessary for optimal performance. Future work will evaluate the generalizability of these findings to other data stream classifiers.
Ensemble Feature Selection Approach for Imbalanced Textual Data Using MapReduce

Feature selection is a fundamental pre-processing phase in text classification. It speeds up machine learning algorithms and improves classification accuracy. In big data context, feature selection...
Modelling Cognitive Dynamic Units of Analysis

We outline a new approach (Vaccari, 1998; Vaccari, 1998a) to cognitive modelling developed in the Dynamic System Theory (DST) paradigm. It is based on structured models representing a set of interacting functional systems (Vaccari, 1998) amenable to be formally represented by independent generative models which are solved simultaneously taking into account their interactions (Delaney &amp; Vaccari 1989). These generative models may be specified at a ‘mathematical level’ with different (continuous/discrete) formalisms, while at the implementation level we use the discrete event formalism, which allows different time scales and can represent practically any continuous time/discrete time model. Conceptualising systems as structured models refers to the process of structuring models (i.e. using composition) from a conceptual point of view and not simply at the implementation level by means of mathematical artifices. A global functional system (whose invariant activity emerges from the interplay of the activities of its functional subsystems) is formally represented by a structured DST model
Denoising User-aware Memory Network for Recommendation

For better user satisfaction and business effectiveness, more and more attention has been paid to the sequence-based recommendation system, which is used to infer the evolution of users' dynamic preferences, and recent studies have noticed that the evolution of users' preferences can be better understood from the implicit and explicit feedback sequences. However, most of the existing recommendation techniques do not consider the noise contained in implicit feedback, which will lead to the biased representation of user interest and a suboptimal recommendation performance. Meanwhile, the existing methods utilize item sequence for capturing the evolution of user interest. The performance of these methods is limited by the length of the sequence, and can not effectively model the long-term interest in a long period of time. Based on this observation, we propose a novel CTR model named denoising user-aware memory network (DUMN). Specifically, the framework: (i) proposes a feature purification module based on orthogonal mapping, which use the representation of explicit feedback to purify the representation of implicit feedback, and effectively denoise the implicit feedback; (ii) designs a user memory network to model the long-term interests in a fine-grained way by improving the memory network, which is ignored by the existing methods; and (iii) develops a preference-aware interactive representation component to fuse the long-term and short-term interests of users based on gating to understand the evolution of unbiased preferences of users. Extensive experiments on two real e-commerce user behavior datasets show that DUMN has a significant improvement over the state-of-the-art baselines. The code of DUMN model has been uploaded as an additional material.