Reviewerly/exHarmony huggingface dataset:
Co-Capsule Networks Based Knowledge Transfer for Cross-Domain Recommendation

Cross-domain recommendation (CDR) technology is proved to be an effective way to tackle the difficulties encountered by traditional recommender technology (e.g. CF), such as data sparsity and cold-start. However, on account of the heterogeneity, it is difficult to enhance the representation of user preferences with the informative knowledge of shared user learned from auxiliary domain. In this paper, we propose a CDR method with co-capsule networks based knowledge transfer to implement the recommendation for the cold-start users. Concretely, the model captures the preference of users with a two-tier structure, the attentive GRU is employed to learn the primary intent from item level and the capsule network is used to further refer the user interests in feature level. After studying the mapping matrix, NeuMF is adapted to execute the recommendation task. We conduct extensive experiments on public datasets and the results demonstrate that the proposed model outperforms many state-of-the-art models.
Boosting Black-box Adversarial Attack with a Better Convergence

While the neural network plays a significant role in image recognition, it's vulnerable to the adversarial examples, which is a potential threat to the deep learning systems. However, the adversarial examples can also be used to validate the effectiveness of the networks. And the transferability of the adversarial examples needed to be improved. Learning rate decay is a trick that is often used in the model training, and we apply this method into the generation of the adversarial examples. The Step Size Decay Iterative Fast Gradient Sign Method and Step Size Decay Momentum Iterative Fast Gradient Sign Method are proposed in this paper, which lead to better convergence and improve the attack performance of the adversarial examples on black-box models. Extensive experiments on ImageNet have validated the effectiveness of our methods.
Ion Baciu – a musician who needs to be rediscovered

Abstract Ion Baciu is remembered in the history of Romanian music as the organiser of a musical institution, orchestra trainer and a performer of great depth of George Enescu’s works. His exceptional achievements were the foundation and training of the new symphonic ensemble of the Iași Philharmonic, equal to the few high-performing collectives of the country, which had reached a level of European quality, the assertion of superior conceptual-aesthetic standards in deciphering the particular features of Enescu’s music. The encomiastic chronicles of the most important musical critics, the audio-video recordings, the evocations of those who knew him intimately remain precious, incontestable documents regarding the value of an artist who deserves to be brought back to public consciousness. The micro-study reproduced in what follows sketches a few defining trajectories of Ion Baciu’s personality on the basis of archival samples, from bibliographic testimonies, some of them unresearched until nowadays, to patrimony recordings, unfortunately ignored. In the year of the celebration of nine decades from his birth, of four decades from the historic version of the opera Oedipe , on the commemoration of 120 years from George Enescu’s birth, one should emphasise the merits of the Conservatoire professor, philharmonic manager and conductor who contributed essentially to the inscription of Iași among the important musical centres of the country.
Documents Counterfeit Detection Through a Deep Learning Approach

The main topic of this work is on the detection of counterfeit documents and especially banknotes. We propose an end-to-end learning model using a deep learning approach based on Adapnet++ which manages feature extraction at multiple scale levels using several residual units. Unlike previous models based on regions of interest (ROI) and high-resolution documents, our network is feed with simple input images (i.e., a single patch) and we do not need high resolution images. Besides, discriminative regions can be visualized at different scales. Our network learns by itself which regions of interest predict the better results. Experimental results show that we are competitive compared with the state-of-the-art and our deep neural network has good ability to generalize and can be applied to other kind of documents like identity or administrative one.
SCIENTIFIC AND PEDAGOGICAL ESSENCE OF THE CONCEPTS OF "MORALITY" AND "SPIRITUALITY"

Тhe article examines the concepts of "morality" and "spirituality" based on research by domestic, foreign scientists, psychologists of different eras. The main directions of the study of concepts are highlighted. The main parameters of morality from a pedagogical, philosophical point of view are emphasized. Highlighting the main parameters of morality. Definition of the concept of "morality" based on historical analysis. The substantive part of the concepts of spiritual, moral and moral education. Spiritual and moral education is in demand in pedagogical practice and gives morality to the national identity. The characteristic of the content of the concepts of spiritual, moral, moral and moral education is presented, their features are determined. The relationship between morality and spirituality in cultural identity. The importance of spiritual and moral education is in demand in modern pedagogical science, it is this education that enriches a person with national identity and contributes to the development of spiritual needs, motivating him in his actions to the national ideal, self-actualization. Consideration of the compatibility of physiological needs with the moral and moral qualities of a person. Cognitive and aesthetic layers in the pyramid of needs. Study of the concept of morality in the framework of the activity approach. The educational process and its content. The life stage of a person and the process of self-improvement. The role of the system of rules and principles of human behavior in society and the system of personal beliefs and priorities in life.
Sanitizing hidden activations for improving adversarial robustness of convolutional neural networks

Deep learning is gaining significant traction in a wide range of areas. Whereas, recent studies have demonstrated that deep learning exhibits the fatal weakness on adversarial examples. Due to the black-box nature and un-transparency problem of deep learning, it is difficult to explain the reason for the existence of adversarial examples and also hard to defend against them. This study focuses on improving the adversarial robustness of convolutional neural networks. We first explore how adversarial examples behave inside the network through visualization. We find that adversarial examples produce perturbations in hidden activations, which forms an amplification effect to fool the network. Motivated by this observation, we propose an approach, termed as sanitizing hidden activations, to help the network correctly recognize adversarial examples by eliminating or reducing the perturbations in hidden activations. To demonstrate the effectiveness of our approach, we conduct experiments on three widely used datasets: MNIST, CIFAR-10 and ImageNet, and also compare with state-of-the-art defense techniques. The experimental results show that our sanitizing approach is more generalized to defend against different kinds of attacks and can effectively improve the adversarial robustness of convolutional neural networks.
Demonstration of Flashpen: A high-fidelity and high-precision multi-surface pen for Virtual Reality

Pen computing has become popular with tablet and wall-screen computers for digital precision tasks such as writing, annotating, and drawing. Digital pens have been made possible by the developments in input sensing technologies integrated into such screens. Virtual Reality systems, however, largely detect input using cameras, whose update rates are insufficient for capturing pen input with the necessary fidelity. In this demonstration, we showcase a digital pen for VR that accurately digitizes writing and drawing, including small and quick turns. Our prototype Flashpen repurposes an optical flow sensor from gaming mice, which digitizes minute motions at over 8,kHz when dragged across a surface.
WITHDRAWN: The economic importance of salted reeds and the factors affecting it geographically

Studying Qasab Salt Flats as an internal sabkha is important since it represents a unique model in the Al-Washem region in the Kingdom of Saudi Arabia. Distinctive morphological features surrounded by some of an aerial origin, others of a flood origin, and the third group of rocky appearance, also characterize it. All these factors have combined with the climate in forming this geomorphological phenomenon; this is what we observe in the sabkha al-qasb, one of the internal composts in the Kingdom of Saudi Arabia's arid environment. The salinity of the al-qasb is located in the Al-Washem area, west of Tuwaiq Mountain, and has an area of 33.6 km2. The climate, geology, topography, and the internal drainage system are the main factors that led to its formation. The study area includes four terrain units: the first is the salted ground itself, the second is flood features, floods, and squares, the third is dunes and sand beds, and the fourth is the leveling surfaces covered with saline soil of little thickness. Saline weathering is one of the most critical dangers to which buildings, facilities, and roads are exposed in the study area. The salt crust above the sabkha is the essential source of the saline weathering process as the rate of evaporation increases on the rate of precipitation, especially in the summer, which increases the precipitation of salts, the thickness of the salt crust in the sabkha increases. The field study clarified the current use of sabkha and the possibility of benefiting from the marsh environment through industrial development and using the mineral and chemical elements from sabkha deposits.
IMPROVED SENTIMENT ANALYSIS FOR DRAVIDIAN LANGUAGE-KANNADA USING DICISION TREE ALGORITHM WITH EFFICIENT DATA DICTIONARY

Abstract The trend of exploring the micro blogging services is being hit door to door. The creation and sharing of short text messages is at its top gear. Reviews, being one of the fastest growing online networking services, which receives around millions of reviews per day, has emerged as an invaluable source of the sentiment analysis. Sentiment analysis refers to recognize the sentiment, polarity, opinion or emotion of a text. Websites like news Kannada, prajavani, http://wedunia.com, and oneindianews have reviews written in Kannada which has a higher preference than the common languages. In the current system, research are carried in sentiment analysis for regional language such as Hindi, Bengali, Kannada etc., are very few compared to English language. Here we propose a system which is computationally identifying and categorizing opinions expressed in a piece of text written in Kannada language, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral.
Proposing Perturbation Application Tool for Portable Data Security in Cloud Computing

Cloud computing is an emerging field of computing in a shared resources environment, where multiple clients share the computing resources. The data will be stored on the shared third-party cloud servers and hence it will be in other’s hand. There may be the chance of mishandling the data, if the data is placed in original form. Perturbation is a technique that will not only reduce the understanding level of the data but may convert the data sufficiently in such a form that will not be in the preview of any language parser. The main feature of this perturbation technique is that it will provide the facility to implement the perturbation in an innovative form. This paper proposes an innovative “Perturbation Application Tool (PAT)” that provides the facility to implement noise addition algorithm with encryption. We have also illustrated the implementation and outcomes of the PAT tool and this tool is working successfully for securing the portable data.
An Interactive Graphical Visualization Approach to CNNs and RNNs

Deep Learning models such as Convolutional Neural Nets (CNNs) and Recurrent Neural Nets (RNNs) are routinely used due to their utility and benefits in various practical applications such as e.g., natural language processing and image recognition. However, despite such adoption, these techniques typically present opaqueness of their internal workings to users. The current black box approach to deep learning makes models difficult to understand and fine-tune, and the related lack of information directly influences productivity and hinders innovations in reliable and consistent model development. In this paper, we present a novel web-based, graphical approach to visualizing CNNs and RNNs to address the above adoption challenges. Our approach features an interactive graphical user interface, where the user can view the overarching network architecture and data flow, the weights and corresponding input processing at each layer, and some interpretable aspects of the model as a whole. We show the effectiveness of our visualization techniques on the MNIST dataset corresponding to an image recognition application. Our work contributes to the effective graphical visualization approaches for complex neural networks and thus makes it easier to manage, manipulate, and increase the performance of these networks.
Proposed Method to Generated Values of the RADG Design by Lattice and Logistic Map

The typical scheme used to generate values of RADG (Reaction Automata Directed Graph) are lattice based cryptography and chaos logistic map. Lattice based cryptography is efficient method against post quantum computers consists of set of points of n-dimension \(\in {\mathbb{R}}^{n}\) in lattice space. Logistic map is a mathematical model used to describes the growth of populations by give chaos value between 0 and 1. In this paper, lattice basis \(\in {\mathbb{R}}^{2}\) is used to generate set of points consider as values of RADG design, based on Gaussian lattice cryptography, Babai’s algorithm. Logistic map used to generate and distribution point on RADG states.
PARENTING STYLE, TIPE KEPRIBADIAN DAN PRESTASI AKADEMIK MAHASISWA PAI ANGKATAN 2015 IAIN CURUP

This study aims to determine the effect of parenting style and personality types on the academic performance of PAI students of the 2015 IAIN Curup class. This research is quantitative descriptive. The results of this study are the first good parenting style because tcount= -1.12 is between ttable that is -2.63 and 2.63. Both personality types are good because tcount= 0.40 is between ttable that is -2.63 and 2, 63, The three academic achievements are very high because tcount = 3.08 is greater than ttable that is -2.63 and 2.63. fourth, there is a positive influence between parenting style on personality types because rcount = 0.432 is greater than rtabel 1% level = 0.1956, fifth, there is a positive influence between parenting style on academic achievement because rcount = 0.432 is greater than rtable level 1% = 0 , 1956, Sixth there was a positive influence between personality types on academic achievement because rcount = 0.52 is greater than rtable of 1% = 0.1956. seventh, there is a positive influence between parenting style and personality type together on academic achievement because rcount = 0.58 is greater than rtable of 1% = 0.1956.
Discussion on Big Data Processing Mode of Test Vessel Based on Cloud Computing

In order to build a reasonable cloud computing model architecture of marine comprehensive test vessel, this paper integrates the complex and heterogeneous big data, and analyzes the feasibility of the application of big data processing mode of marine test vessel. This paper proposes an optimized cloud data platform architecture, which can meet the daily data analysis of marine comprehensive test vessel. Finally, combined with practical application, this paper verifies the feasibility The effectiveness of the method improves the information processing efficiency of the ocean test vessel.
An Improved Image Classification Method for Specific Reality Environments

This paper proposes an improved image classification method based on specific data features and practical product requirements in the real enterprise environment. In order to make the model capable of filtering images outside the predefined classes and increasing the precision of the model, two probability thresholds are set at the last layer of the model, and "the uncertain class" is introduced to control the output of the model. The predefined classes are subdivided to sub-classes in training procedure and reassemble after training, which improves the stability and robustness of the model. A parallel architecture is also proposed to improve the efficiency of the entire system. The model is evaluated on a real image dataset collected by ourselves. Compared with the traditional deep model, the proposed method achieves faster convergence, higher accuracy and stability. Especially, it can filter out most of uncertain classes that traditional models cannot recognize. The system is already available in the practical enterprise environment, and it is highly efficient and extendable.
Design and Implementation of Information Subscription System Based on Intelligent Crawler

With the continuous growth of data volume in the information age, it is becoming more and more difficult for people to obtain information they care about. The traditional way of manually collecting information from such massive data is inconvenient and inefficient. In order to solve this problem, we design an web information subscription system based on intelligent crawler. In this paper, the first section introduces the design of the system, which includes the two confirmations to determine the specific monitoring area, the original xpath-based information positioning and block method, the task management based on apscheduler and so on. The second section introduces the implementation of the system, which included the process of how to operate the system. The third hows the running interface and results of the system. And finally the experiments show that the system can help users quickly obtain useful web information.
Quantum advantage for computations with limited space

Quantum computers promise the ability to solve problems that are intractable in the classical setting1, but in many cases this is not rigorously proven. It is often possible to establish a provable theoretical advantage for quantum computations by restricting the computational power2–8. In multiple cases, quantum advantage over these restricted models was demonstrated experimentally9–12. Here we consider space-restricted computations that use only one computational classical or quantum bit with a read-only memory as input. We show that n-bit symmetric Boolean functions can be implemented exactly in this framework through the use of quantum signal processing13 and O(n2) gates, but in the analogous classical computations some of the functions may only be evaluated with probability $$1/2 + O\left(n/{\sqrt{2}}^n\right)$$ . We experimentally demonstrate computations of three-bit to six-bit symmetric Boolean functions by quantum circuits with an algorithmic success probability that exceeds the classical limit. This shows that in computations, quantum scrap space offers an advantage over analogous classical space, and calls for an in-depth exploration of space–time trade-offs in quantum circuits. In general, it isn’t known when a quantum computer will have an advantage over a classical device. Now it’s proven that computers with limited working memory are more powerful if they are quantum.
Research on Media Data Analysis System Based on Big Data Technology

The system designed in the paper uses the method of collecting Twitter tweets to test the collection system. The specific collection content is tweet collection based on keywords, testing the collection rate of tweets and system stability. In the test, the bandwidth of each collection node is limited by using bandwidth limiting software to simulate different network agent environments. The collection system in different environments is tested by changing the collection task and the bandwidth limit of each collection node. The results prove that the algorithm proposed in the paper can significantly improve the evaluation accuracy of media data, and has a significant effect on analyzing the collected objects.
SILVER: Generating Persuasive Chinese Product Pitch

Building a silver-tongued salesbot is attractive and profitable. The first and pivotal step is to generate a product pitch, which is a short piece of persuasive text which both convey product information and deliver persuasive explanations related to customer demand. Recent advances in deep neural networks have empowered text generation systems to produce natural language descriptions of products. However, to produce persuasive product pitches, deep neural networks need to be fed with massive amounts of persuasive samples, which are not available due to huge labelling cost. This paper proposes SILVER, a persuasive Chinese product pitch generator, which addresses the issue of insufficient labeled data with data-level, knowledge-level and model-level solutions. At the data level, SILVER employs statistic analysis to automatically derive weak supervision rules that correlate with persuasive texts. At the model level, SILVER apply the weak supervision rules to re-rank outputs from an ensemble of models to enhance pitch generation performance. Finally, at the knowledge level, SILVER incorporates attribute hierarchy to embed product information in the pitch. Both automatic and human-involved evaluations on real data demonstrate that SILVER is able to produce more fluent, catchy and informative snippets than state-of-the-art text generation approaches.
Super-Resolution: 1. Multi-Frame-Based Approach

The multi-frame super-resolution (MF SR) problem with input in the Bayer domain is considered. Shots are assumed to be obtained by the consumer camera and provided in RAW format, which means that they were not subject to any prior enhancement or compression. In the introduction, general ideas being used in super-resolution are summarized, and the place of the MF SR for consumer cameras among other SR problem settings is shown. Different aspects needed for an end-to-end MF SR solution are covered. Real-time applications are discussed. The detailed structure of the problem matrices facilitating fast computational approaches is considered. Finally, a filter-bank framework for MF SR reconstruction is described. Along with the reconstruction algorithm, it includes supplementary modules, such as motion estimation in the Bayer domain, an integrated noise reduction sub-algorithm, a directionality estimation sub-algorithm, fallback logics, and post-processing for additional colour artefact reduction. The core solution was formulated as an inverse quadratic problem of joint demosaicing and MF SR. Closed-form solutions for different space-invariant subpixel motions between frames were obtained and represented in the form of a filter-bank, which allows the computation of a solution of the SR problem using adaptive filtering, where filters are selected depending on the subpixel motion between frames. Using the apparatus of multilevel matrices made it possible to reduce the computational complexity from \( \mathcal{O}\left({n}^6\right) \) to \( \mathcal{O}\left({n}^2\log\ n\right) \). The relation between solutions of the SR problem and the different warping parameters was also studied, and it is proven that certain solutions can be derived from solutions with other parameters by using simple transforms. This makes it possible to reduce the amount of memory used for storing filters by up to 80 times (for 2× magnification, three input frames). The developed solution showed superior results compared to subsequent demosaicing and single-channel SR and was tested on real raw images captured by a cell phone camera in burst mode.