Reviewerly/exHarmony huggingface dataset:
Only Image Cosine Embedding for Few-Shot Learning

Few-shot learning in computer vision is a very challenging task. Many approaches have been proposed to tackle the few-shot learning problem. Meta learning, a method of learning to learn, is introduced into few-shot learning problem and has achieved pretty good results. But there is still a very big gap between the machine and our human in the few-shot learning tasks. We think it’s because the existing methods do not make full use of global knowledge (similar to the priori knowledge of human understanding of images) thus lacking a world view of the task. In other words, they focus too much on local information and neglect the whole task. In this paper, we rethink about the few-shot learning problem, and propose that we should make full use of global knowledge. Seen data set is used to obtain a embedding function between images and feature vectors, the images are embedded onto a hypersphere in the manner of cosine embedding. By taking this embedding function as global knowledge, we train a classifier to classify the corresponding embedded vector of images. The experiment proved that our approach significantly outperforms both baseline models and previous state-of-the-art methods. It surpasses most existing methods in terms of flexibility, simplicity and accuracy. Codes are available at https://github.com/SongyiGao/OICEFFSL .
Perception-Based Visual Authentication Codes

This paper will present a new application of personal cognitive features in creation of new protocols, oriented for user authorization. Such new solutions will be based on cognitive information systems, which allow to evaluate personal cognition features. In particular will be described some application of perceptual, and personal characteristics in creation of security procedures, oriented for authentication tasks, as well as secure information management in distributed systems. The area of cognitive cryptography will be presented in the context of conducted research.
Can You Trust the Blockchain? The (Limited) Power of Peer-to-Peer Networks for Information Provision

We investigate the potential and limits of privacy-preserving corporate blockchain applications for information provision. We provide a theoretical model in which heterogeneous firms choose between adopting a blockchain application or relying on traditional third-party intermediaries to inform the capital market. The blockchain's ability to generate information depends on each firm's data profile and all firms' endogenous adoption decisions. We show that blockchain technology can improve the information environment and outperform traditional institutions, with firms' adoption decisions serving as a credible value signal and the application uncovering firm values by analyzing all participating firms' data. However, we also characterize an adverse mixed-adoption equilibrium in which neither of the two channels realizes its full potential and information provision declines not only for individual firms but also in aggregate. The equilibrium is a warning sign that has broad implications for policymakers' regulatory effort and investors' assessment of corporate blockchain applications.
MDP Autoencoder

This paper proposes a novel deep reinforcement learning (RL) architecture, which learns a dynamics model in latent space that is behaviorally grounded to the observed space and applies the framework of MDP homomorphisms to provide bounds for the loss in performance. In contrast to traditional model based reinforcement learning algorithms, this approach models the next latent state while predicting the rewards and chance of termination after current step, instead of reconstructing the future observations. This results in a much simpler model that can encode the underlying dynamics of the observed system. Experiments show that the proposed approach learns concise latent Markov models that can encode policies which achieve scores close to optimal.
A Combination of CMAES-APOP Algorithm and Quasi-Newton Method

In this paper, we present an approach for combining the CMAES-APOP with a local search in order to make a hybrid evolutionary algorithm. This combination is based on the information of population size in the evolution process of the CMAES-APOP algorithm while the local search is quasi-Newton line search algorithm. We will give some conditions to efficiently active the local search inside CMAES-APOP. Some numerical experiments on multi-modal optimization problems will show the efficiency of proposed approach.
Technical development and IT application trends of medical ultrasound instrument

The technical development of diagnostic ultrasound got a fast-innovative period in last 20 years. The resolution and quantitative analysis of ultrasound images have achieved to a high level. Along with emerging IT technology such as tele-medicine, big data, cloud computing and robot technology, extending ultrasound application area by new platform, breaking out ordinary ultrasound limitation, has become ultrasound instrument development direction. Taking tele-ultrasound, ultrasound cloud computing technology and robot as topics, this paper summarize new specialties and development trend of ultrasound instrument including the technical difficulties and real market application obstruction and try to explore future study direction cross medicine and information processing and cloud computing.
LANet: A Ladder Attention Network for Image Semantic Segmentation

A Ladder Attention Network (LANet) is proposed to recalibrate feature maps by using channel information in image semantic segmentation. Unlike previous works which use global average pooling to obtain global information of channel map, we use sub-region average pooling (SAP) to extract more abundant channel information. Meanwhile, the attention of low-stage channel is related to that of high-stage channel. In order to make the attention information of low-stage channel better transmit to high-stage features, we add the attention expansion mechanism of low-stage channel. On the other hand, noting the influence of channel number on multi-scale features fusion of Atrous Spatial Pyramid Pooling (ASPP) model in DeepLabv3 network, we allocate different channels to different convolution or pooling operations without increasing the total number of channels. Finally, experimental results on MIT Scene Parsing Benchmark dataset (SceneParse150) are included to demonstrate the effectiveness of the proposed model.
Multiagent Light Field Reconstruction and Maneuvering Target Recognition via GAN

The lack of sample data and the limited visual range of a single agent during light field reconstruction affect the recognition of maneuvering targets. In view of the above problems, this paper introduces generative adversarial nets (GAN) into the field of light field reconstruction and proposes a multiagent light field reconstruction and target recognition method based on GAN. The algorithm of this paper utilizes the characteristics of GAN to generate data and enhance data, which greatly improves the accuracy of light field reconstruction. The consistency mean of all observations is obtained by multiagent data fusion, which ensures the reliability of sample data and the continuity of maneuvering target recognition. The experimental results show that the accuracy of light field reconstruction reaches 94.552%. The accuracy of maneuvering target recognition is 84.267%, and the more the agents are used, the shorter the recognition time.
Privacy-Preserving Sequential Data Publishing

Machine learning in artificial intelligence relies on legitimate big data, where the process of data publishing involves a large number of privacy issues. m-Invariance is a fundamental privacy-preserving notion in microdata republication. Unfortunately, if for big data release, the existing generalization based m-Invariance requiring to modify the origin microdata incurs the problems of data utility loss and poor aggregate querying performance. Furthermore, due to the high dimension of quasi-identifiers in big data, unaffordable generalization operations makes it difficult to be practical. In this paper, we remedy the drawbacks above to achieve m-Invariance in big data release. We first propose a new anatomy based m-Invariance definition and framework, where the anatomy approach tries to achieve privacy by breaking the correlations between the sensitive attributes and non-sensitive identifiers. We next establish a series of criteria for anatomy to cope with republication due to the data dynamics. We then develop an algorithm to realize the above ideas. Theoretical and experimental analysis confirm the advantages of our anatomy based m-Invariance approach in the terms of data utility, aggregate querying accuracy and capacity to process high dimension of quasi-identifiers in big data release.
Using Audio Characteristics for Mobile Device Authentication

This work attempts to use the innate manufacturing defects in hardware components as identification characteristics for mobile phones. Different components of mobile phones related to I/O operations, such as sensors, were assessed for suitability. From this process, efforts were focused on using both the phone’s speaker and microphone in combination to generate samples containing hardware defects which could then be classified. In our approach, a known audio was played using a cellphone’s speakers and recorded using the same device’s speaker, creating an audio sample. Multiple different groups of samples were taken to test the impact of certain variables on the sample accuracy. The collected samples then had their frequency responses extracted and classified. Different classifiers were used to classify samples with some configurations of classifiers and sample groups achieving over 99.9% accuracy. The results presented in this paper indicate that the manufacturing defects in speakers and microphones could potentially be used for the purposes of device identification.
Securing Software Updates for Trains

We propose the secure procedure for the automated railway update and maintenance. The proposed procedure is derived from the Uptane update framework. Testing and validation phase, additional manual approval procedure and update progress control are integrated into the Uptane framework in order to conform to the railway safety requirements and norms. The possible metadata and repository customization is proposed and specific railway update attacks are discussed.
Semi-supervised Deep Learning Using Improved Unsupervised Discriminant Projection

Deep learning demands a huge amount of well-labeled data to train the network parameters. How to use the least amount of labeled data to obtain the desired classification accuracy is of great practical significance, because for many real-world applications (such as medical diagnosis), it is difficult to obtain so many labeled samples. In this paper, modify the unsupervised discriminant projection algorithm from dimension reduction and apply it as a regularization term to propose a new semi-supervised deep learning algorithm, which is able to utilize both the local and nonlocal distribution of abundant unlabeled samples to improve classification performance. Experiments show that given dozens of labeled samples, the proposed algorithm can train a deep network to attain satisfactory classification results.
Collaborative Ontology Authoring in the Domain of the Holy Quran Knowledge

The following topics are dealt with: antenna radiation patterns; antenna feeds; microstrip antennas; 5G mobile communication; quality of service; microstrip antenna arrays; ultra wideband antennas; learning (artificial intelligence); routing protocols; multifrequency antennas.
Deep Embeddings for Brand Detection in Product Titles

In this paper, we compare various techniques to learn expressive product title embeddings starting from TF-IDF and ending with deep neural architectures. The problem is to recognize brands from noisy retail product names coming from different sources such as receipts and supply documents. In this work we consider product titles written in English and Russian. To determine the state-of-the-art on openly accessed “Universe-HTT barcode reference” dataset, traditional machine learning models, such as SVMs, were compared to Neural Networks with classical softmax activation and cross entropy loss. Furthermore, the scalable variant of the problem was studied, where new brands are recognized without retraining the model. The approach is based on k-Nearest Neighbors, where the search space could be represented by either TF-IDF vectors or deep embeddings. For the latter we have considered two solutions: (1) pretrained FastText embeddings followed by LSTM with Attention and (2) character-level Convolutional Neural Network. Our research shows that deep embeddings significantly outperform TF-IDF vectors. Classification error was reduced from 13.2% for TF-IDF approach to 8.9% and to 7.3% for LSTM embeddings and character-level CNN embeddings correspondingly.
Improving a Language Model Evaluator for Sentence Compression Without Reinforcement Learning

We consider sentence compression as a binary classification task on tokens. In this paper we improve on a language model evaluator model by incorporating a score from a neural language model directly into the loss function instead of resorting to reinforcement learning. As a result, the model learns to remove individual tokens and to preserve readability at the same time while maintaining the desired level of compression. We compare our model with a state-of-the-art model, which uses a policy-based reinforcement learning method for evaluating compressed sentences on readability. We perform automatic evaluation and evaluation with humans. Experiments demonstrate that we were able to improve on the strong baselines. We also provide human-evaluation of 200 gold compressions from Google dataset setting a baseline for human-evaluation in upcoming studies.
Pedagogical Meaning of Study of Transformation Musical Practices of Peoples of the World in Modern Education

Nowadays the Russian education is living a raw of system changes, where formation of student’s personal agency, of skill for learning and for self-development during the whole life, for elaborating the way of actualization of their own intentions are becoming the main tasks of the learning process. The abilities for self-understanding, self-development and self-actualization are connected with a category of personal transformation. The keys for its understanding could be found in the experience of traditional cultures that despite their bent for constancy are full of transformational motifs. The important role in this process belongs to “symbols of transformation”. One of the most effective between them is music that accompanies all the important events of human life in the context of traditional culture. The main mechanism for personal transformation is activity of musical experiencing. The comparative study of traditional cultures gives possibility to offer the following typology of transformational practices of work with musical experiencing: 1. Musical practices for passing the borderline states between dream and wakefulness. 2. Rites of passage and initiation. 3. Practices of individual and joint intoning of experiencing. 4. Healing practices. 5. Transformational practices of storytelling sessions. 6. Rituals of spiritual renovation. 7. Spiritual musical practices in education. 8. Dance transformational practices. In all of them music takes on the role: of internal subjective experience; of “live sound” that influences the whole human organism but not only the acoustic analyzer; of means for symbolization of experiencing; of a way of internal “growing” – from concentration in point of painful experiencing into the wide, endless “Self”; of marker of internal changes; of a metaphor of experiencing activity for transformation of internal world. The study of transformational musical practices in modern education helps to achieve the deeper level in world culture study, to understand the variety, typology and mechanisms of musical influence on a human in different traditions; widens the range of methods for work with musical experiencing; forms pedagogical tools for modern developmental, psychotherapeutic and educational practices; reveals the role of music as means for symbolization of experiencing; shows spiritual foundations of musical traditions of different peoples that form emotional and axiological attitude to them; enriches with wide range of cultural symbols for understanding and composing person’s own life.
PERSONALITY-ORIENTATION AS A MARKER OF INNOVATION IN THE EDUCATIONAL FIELD

The educational realities of the 21st century are changing significantly under the influence of information technologies, the use of Internet resources and virtual practices in the educational process, the introduction of new standards of knowledge transfer. As a result, a polyspace of communication appears, where a confident and comfortable person who has broken the shackles of the Modern worldview, has abandoned the narratives and limitations of the Modern. One can observe a specific anthropo-axiological turn in education, which is a sign of the increasing role of the subjective factor. This necessitates qualitative changes in the educational process, the widespread use of innovative technologies and the latest methodological approaches, the attraction of information technologies capable of providing training for highly qualified specialists, which will ensure the prosperity and well-being of the country. However, only the changes in the educational process are not enough, since there is a need to form a new image of educator, who is aware of the cognitive, axiological, praxeological needs of the generation born in the age of globalization, postmodernism and total consumption, and capable of perceiving significant amounts of information, but requires the skills for its analysis. That is why the search-and-problematic thinking style, the ability to analyze information and to highlight the main point of interest are of great importance. The main guideline of modern education is the intellectual development of a person capable of productive cognitive activity, constant self-improvement and self- education. Therefore, methods of creative learning, acquisition and production of knowledge, the ability to formulate and solve problems, and independently search for necessary information in finding the answer to the problems posed by the teacher or the students themselves become especially relevant and required.
The Implementation of the Differentiated Approach in Mathematics Teaching of Younger Schoolchildren

The article reveals the goals and methods of organizing the differential education of primary schoolchildren in mathematics, which involves taking into account the individuality, level of ability and degree of independence of each schoolchild, as well as providing him with timely assistance in case of difficulties. Methods of differentiation and means of assisting students are described, which are used by the teacher in the classroom to solve various types of arithmetic problems. The effectiveness of a differentiated approach to the formation of mathematical literacy of primary schoolchildren is emphasized, which makes it possible to guarantee compliance with the requirements of the Federal State Educational Standard of Primary General Education with the planned learning outcomes.
Detection of Suspicious Activity in ATM Booth

This paper focuses on safety and security issues in Automated Teller Machine (ATM) counters, which includes detection of covered faces, person wearing helmet, more than one person at ATM at a time, person in drunken state, people fighting, etc. We use combine method of Viola–Jones Algorithm and modified Circle Hough Transform for helmet detection. Results show better decision rates and attained high precision and low false positive rate.
Single-stage cluster sampling: Clusters of equal size

Abstract Similar to strata, population units may instead be grouped into clusters. Usually, units within clusters are geographically or genetically close to one another—all households on a city block, individuals within a single family. In (single-stage) equal size cluster sampling, the total population consists of N clusters, with equal numbers of population units within each cluster. A sample of n clusters is selected by SRS, y values of all population units within clusters are measured, and an unbiased estimator of the population mean is the simple average of cluster means in the sample. An ANOVA sum of squares partition can be used to show that this strategy will outperform SRS with mean-per-unit estimation whenever the mean square between clusters is less than the finite population variance. This means that it is desirable for clusters to have similar means but a great deal of variability within clusters, a contrast with the desirable characteristics of strata (little variability within strata, substantial difference between stratum means). Many methods of collection of samples in fisheries (seines, nets) and wildlife (mist nets, live traps) involve collection of individuals as clusters. Unfortunately, clusters (e.g., human families) often consist of closely related or similar individuals. Because within cluster variation is often relatively low, it is often advantageous and cost-effective to instead adopt two-stage cluster sampling (considered in Chapter 9) for which only a sample of units within each selected cluster are examined, thereby allowing more clusters to be examined for the same total survey cost.
The Use of Regional Museums Materials in the Process of Patriotic Education of Primary Schoolchildren

The article discusses the opportunities that expositions of regional museums provide for the education of younger schoolchildren in the spirit of respect and love for their homeland. The successful experience of cooperation of primary school teachers with the museums of Borisovka, Belgorod Region, is presented. On the example of conducting extra-curricular activities at the integrated regional training course “My land is my native Belgorod region”, the effectiveness of such pedagogical work is substantiated, which allows to familiarize children with the socio-historical and cultural heritage of their small homeland, educate them as patriotic citizens of Russia.
Non-positive Face Chin Detection Method Based on Curve Fitting

Abstract The head recognition system needs precise contours of the face area, whereas the non-positive face color contours often contain neck areas, which will seriously affect the extraction of contour features. Therefore, the neck area in the outline of the human face needs to be removed to obtain the final facial contour. In this paper, the chin curve is fitted based on the extremum of the contour features of the left and right sides of the human chin region, and then the neck area can be removed. The experimental results show that the method proposed in this paper can obtain the non-positive face profile without the neck.
Corners based spatial decomposition method for image watermarking

We propose a watermarking scheme, which uses the content of the image and with the help of corners decomposition method. In this work, the Harris corner detector is used for getting image corner points, which are combined, with a rectangle decomposition method to have a set of rectangular insertion blocks. The proposed scheme can devise into two steps. The embedding step that aims to include one bit of message on each calculated image rectangle, in the end of the insertion step we obtain the watermarked image that includes the full message. The detection step is the inverse process of the first step, where the watermark is extracted and the message decoded from the watermarked image. After a benchmark test, we got an encouraged result. Then we arrive to detect 100% of message embedded (embedded between 9 to 20 bits) in various images without altering the visual quality of the original images (PSNR up than 53dB), and with low algorithm complexity (0.3 second as average in the embedding scheme).