Reviewerly/exHarmony huggingface dataset:
Coupling Architecture Between INS/GPS for Precise Navigation on Set Paths

GPS offers the advantage of providing high long-term position accuracy with residual errors that affect the final positioning solution to a few meters with a sampling frequency of 1 Hz (Marston et al. in Decis Support Syst 51:176–189, 2011 []). The signals are also subject to obstruction and interference, so GPS receivers cannot be relied upon for a continuous navigation solution. On the contrary, the inertial navigation system has a sampling frequency of at least 50 Hz and exhibits low noise in the short term. In this research, a prototype based on development cards is implemented for the coupling of the inertial navigation system with GPS to improve the precision of navigation on a trajectory.
Word Representations, Seed Lexicons, Mapping Procedures, and Reference Lists: What Matters in Bilingual Lexicon Induction from Comparable Corpora?

Methods for bilingual lexicon induction are often based on word embeddings (WE) similarity. These methods must be able to project the WE to the same space. Uncontextualized WE proved to be useful for this task. We compare them to contextualized WE and Bag of Words, using specialized and general datasets. We also evaluate the impact of seed lexicons and check the existing reference lists validity, claiming that extracting the translation of some words in those lists is not useful and confirming the need to have more fine-grained reference lists.
Towards Cost Effective Server Population Estimation: A Case Study of Twitch

Internet services such as email and video streaming are daily necessity for many of us. To scale to the demand of service quality and continuity, major services today operate on server clusters. Growingly large, long-lasting, and dynamic, these server clusters are pretty much ecosystems themselves. Drawing from the observation, we see a window of opportunity applying techniques in ecology, e.g., Capture-Mark-Recapture (i.e., CMR) for server population estimation, a problem fundamental to cluster monitoring.
Multi Agents Based System Architecture for Market Research in E-Business

Companies and organizations in today’s business especially online business are found in dire need of hiring employees for market research. They pay handsome compensations for the services of such employees in return. In addition, market research deals with loads of internet surfing, spending time on studying potential markets and tapping potential customers. Few existing market research tools/platforms are able to generate potential clients and assist to run marketing campaigns; but, these also require prepared data sets that need to be mapped as per the software’s own requirements and capacities. Our idea is to employ Multi Agent System (MAS) to search particular markets and provide an efficient route to gather business opportunities that are incessantly created in the fast paced world today. Two basic methodologies for MASs are complementary to each other; one is iterative on each stage of development while the other one is a cyclic process of analysis and design. Amalgamation of these two, is used for designing our desired multi agent system for Market Research in E-Business (MREB). In order to make a prototype, we used Netbeans, JADE FIPA-compliant with SQL Server 5.7. It equips any company using this platform with an opportunity to get a huge list of suitable projects according to its product base and/or services expertise. We have achieved considerable results by our system MREB with some new features.
TEACHING STAFF PROFESSIONAL DEVELOPMENT IN THE CONTEXT OF INCLUSIVE EDUCATION

&#x0D; &#x0D; &#x0D; The article deals with the issues of providing support to pedagogical team in the inclusive education settings as the main condition for the success of inclusion implementation. As a methodological basis for the analysis, the teacher competence structure is used as an approach. There are given many ways to expand the circle for teachers support. Arguments are also made in favor of engaging and interested in organizations for all employees of an inclusive school to create an inclusive team and the necessity for interest in joint work of both the administration and the teachers, correctional teachers included.&#x0D; &#x0D; &#x0D;
Recommendation based on Emotional Preference

This paper describes an outline and a design of a recommender system that gives recommendations of contents based on the estimation of user's emotional preferences. Recommender systems are essential tools in the age of information overload, and they are helpful to increase comfortability and quality of life by filtering out annoying, unnecessary and troublesome information. The standard recommender system uses personal database, which stores the profile and preferences of a user. The key point of this study is a use of emotions in the personal preferences. Emotions are subjective quantities so that are difficult to deal with. This paper explains how our system identify emotions and how to deal with them in the recommender system. We also consider risk avoidance in information presentation of recommendations.
Single Color Image De-Raining in Outdoor Vision Systems: A 2-Step Approach

For the analysis of road user behavior, accident causation, crime scene evaluation etc., the images obtained from outdoor vision systems are used. Surveillance cameras, traffic cameras etc. are coming under outdoor vision systems. The quality of images obtained from these systems should be very high so that an accurate analysis can be performed. But, there are many conditions that can badly affect the quality of these vision systems. One such condition is the effect of an unusual weather like rain, snow etc. Rain streak can cause severe quality degradation on an image. So, image de-raining is an important task. In this paper, a simple and fast rain removal technique is discussed. Here, a 2-step processing is performed on a rainy image to remove rain streaks from that by employing two techniques namely rain streak detection and image smoothing. In the first step, the input image is converted into a low-frequency part which is almost completely free of rain streaks but is blurred. In the second step, an image smoothing and contrast enhancement is performed to produce a smoothened and enhanced image. Finally, the result of these two steps are added to produce the de-rained output image. This is a simple and fast method for image de-raining.
Data Size and its Impact for Feature Creation in Transfer Learning using Neural Networks

In this paper, it's looked upon how the two data set one of which is base data and the other is target data and by varying the sizes of these two data sets and using the transfer learning in neural networks, labeling of data is costly and time consuming, and the series of labeled instance are generally very small. It is a proposed variety of steps on how the source dataset and target dataset mapping play a crucial role in transfer learning scenarios where each scenario has its own effects. By either training, a target of classes from start or keeping the layers whichever is available within the network, for the Places 205 dataset and ImageNet and their subsequent scale down version of the datasets. By indicating how each scenario is differently supported the info size for Similar and Different datasets or Small and bigger categories where we freeze the weights for the first few layers of the network provide a good fit on the target set classes.
Explaining Neural Network Output Using Evolutionary Calculations

In the paper we tried to describe our research, connected with the attempt to explain the output, made by the artificial neural networks. In our previous research we have proposed the method of combining genetic algorithms and ANN. The basic idea of the apporach was training ANN with application of GA instead of 'classic' back propagation method. In this paper we depict a reverse task which is concluded in revealing the hidden unknown links between NN inputs and outputs while solving it. The results are implemented in the neuronetwork decision support system
Candidate region correlation for video action detection

The rapid development of deep learning has prompted the development of video action detection technology. However, the accuracy of current video action detection algorithms can be improved further. Previous work has improved feature extraction by optimizing the network structure. In addition, the features of the candidate regions have been optimized by changing the representation of the regions. Although these methods have achieved promising results, they fail to consider the correlation among different candidate regions, generating uninformative (even redundant) candidate regions, and thus usually decrease the detection performance in practice. To address this problem, in this paper we propose a self-attention mechanism for candidate regions, which can help pursue the most informative regions. We obtain the region correlation by simultaneously determining the spatial and temporal correlation among different candidate regions. In addition, we focus on how to apply the correlation to optimize the original candidate region features and improve video action detection accuracy. The experimental results show the promising improvement achieved by our method over the state-of-the-art solutions.
Конструкция я/ты + ж (же) + имя существительное как средство манипулирования

This paper presents the nominal semantic classes involved in the construction “I/you + particle + noun.” The I-type is usually associated with self-justification and self-praise, while the You-type is associated with reproach, persuasion, order, and request. The usage of such words as jažemat′ or tyžvrač on the Internet suggests that the determination of the addressee’s and sender’s identity may be seen as a form of struggle for leadership. I/you constructions can be divided into two gropus: the one with nouns that denote kinship or gender and and the other that collocates with nouns that denote occupation. The former group is characterized by symmetry of the I/you constructions and commonest words these constructions collocate with (a mother, a son, a man, a woman etc.), while for the latter group the use of constructions with I and constructions with you is asymmetrical. When the speaker nominates the interlocutor using one of these constructions he/she gets the upper hand in the conversation and thus limits the actions of the addressee, who cannot argue because such statement of the obvious deprives the latter of grounds for disagreement: tyžvrač, you’re a doctor here, aren’t you? and you cannot beat it. We show that the relationship between the speakers in the constructions we study is mobile, and the possibility of completing the dialogue with an open conflict is minimal, since the identification of the addressee makes it clear that he/she must behave in accordance with the sender’s presupposition and cannot go beyond the line of restrictions set by him/her.
A Sampling-Based Tool for Scaling Graph Datasets

Graph processing has become a topic of interest in many domains. However, we still observe a lack of representative datasets for in-depth performance and scalability analysis. Neither data collections, nor graph generators provide enough diversity and control for thorough analysis. To address this problem, we proposea heuristic method for scaling existing graphs. Our approach, based onsampling andinterconnection, can provide a scaled "version" of a given graph. Moreover, we provide analytical models to predict the topological properties of the scaled graphs (such as the diameter, degree distribution, density, or the clustering coefficient), and further enable the user to tweak these properties. Property control is achieved through a portfolio of graph interconnection methods (e.g., star, ring, chain, fully connected) applied for combining the graph samples. We further implement our method as an open-source tool which can be used to quickly provide families of datasets for in-depth benchmarking of graph processing algorithms. Our empirical evaluation demonstrates our tool provides scaled graphs of a wide range of sizes, whose properties match well with model predictions and/or user requirements. Finally, we also illustrate, through a case-study, how scaled graphs can be used for in-depth performance analysis of graph processing algorithms.
RANDM: Random Access Depth Map Compression Using Range-Partitioning and Global Dictionary

We present a novel random-access depth map compression algorithm (RANDM) for interactive rendering. Our compressed representation provides random access to the depth values and enables real-time parallel decompression on commodity hardware. Our method partitions the depth range captured in a given scene into equal-sized intervals and uses this partition to generate three separate components that exhibit higher coherence. Each of these components is processed independently to generate the compressed stream. Our decompression algorithm is simple and performs prefix-sum computations while also decoding the entropy compressed blocks. We have evaluated the performance on large databases on depth maps and obtain a compression ratio of 20 − 100 × with a root-means-square (RMS) error of 0.05 − 2 in the disparity values of the depth map. The decompression algorithm is fast and takes about 1 microsecond per block on a single thread on an Intel Xeon CPU. To the best of our knowledge, RANDM is the first depth map compression algorithm that provides random access capability for interactive applications.
A study on Shift towards Digitization of Approval of Permissions in a University

Getting permissions approved is one of the biggest problems faced by college students. Even till date, many universities use manual and traditional techniques for getting approvals. This causes not only wastage of time and energy but also various resources. The aim of this research work is to develop an application for the clubs, chapters and teams of a university. So that it can be used for online approval of permissions from faculties and also keep a record of same. This application will have several modules which include room permissions, event permissions, guest visits, event participation, material purchase requests and on-duty permissions.
Association Rule Modeling using UML and Apriori Algorithm

The categorical relationships play a crucial role for choosing of computer database. Based on it, association rules display the link among the 2 categories. Because the object oriented elegance for the computer code cultivates, the relationships among the categories. Additionally rise near the complicated computer code style. However, one will develop improved object-oriented style through right association rules among groups or categories. The present paper deals to style the proper association rules for the object-oriented databases taken from the categories. A true scenario of Electricity Bill Deposit System (EBDS) and user interested items by analyzing user behavior history in social network environment. Both applications are taken into account in Indian situation and Apriori Algorithmic rule is employed for locating the employment of recurrent information sets through the proper association rules. The association rules area unit deliberate through famous Unified Modeling Language. The current paper work is to the associate degree carrying out of Apriori algorithmic rule towards the information of EBDS.
Label Likelihood Maximisation: Adapting iris segmentation models using domain adaptation

We propose to use unlabelled eye image data for domain adaptation of an iris segmentation network. Adaptation allows the model to be less reliant on its initial generality. This is beneficial due to the large variance exhibited by eye image data which makes training of robust models difficult. The method uses a label prior in conjunction with network predictions to produce pseudo-labels. These are used in place of ground-truth data to adapt a base model. A fully connected neural network performs the pixel-wise iris segmentation. The base model is trained on synthetic data and adapted to several existing datasets with real-world eye images. The adapted models improve the average pupil centre detection rates by 24% at a distance of 25 pixels. We argue that the proposed method, and domain adaptation in general, is an interesting direction for increasing robustness of eye feature detectors.
Development of General Cultural Competences of Higher Education Students on the Basis of Regional Content Communicative Training

This article is devoted to the issues of developing general cultural competences of undergraduate students. Special attention is paid to the foreign-language communicative competence that determines the ability and commitment of Russian specialists to establish and develop professional communication in a foreign language. In order to develop the above-mentioned competence, the authors suggest implementing communicative training technology, which presupposes the creation of special conditions for intensive interpersonal interaction in a foreign language. The assignments of the communicative training include regional content that is connected to the field of nuclear energy and technical physics. The regional content itself reflects the comparison of functioning of nuclear power plants in Nizhny Novgorod region, Russia, and the province of Ontario, Canada. The concept of the regional content is studied at the micro-level and allows creating special communicative training tasks that are based on real professional communicative situations that are significant for the representatives of both regions. Nizhny Novgorod region, which is known to be a major research center of nuclear energy, is of great interest to specialists in this field around the world. Regional content communicative training presented in the article will prepare Nizhny Novgorod nuclear energy and technical physics undergraduate students for successful interaction with their foreign colleagues in the sphere of operation of nuclear power plants.
BioFaceNet: Deep Biophysical Face Image Interpretation.

In this paper we present BioFaceNet, a deep CNN that learns to decompose a single face image into biophysical parameters maps, diffuse and specular shading maps as well as estimating the spectral power distribution of the scene illuminant and the spectral sensitivity of the camera. The network comprises a fully convolutional encoder for estimating the spatial maps with a fully connected branch for estimating the vector quantities. The network is trained using a self-supervised appearance loss computed via a model-based decoder. The task is highly underconstrained so we impose a number of model-based priors. Skin spectral reflectance is restricted to a biophysical model, we impose a statistical prior on camera spectral sensitivities, a physical constraint on illumination spectra, a sparsity prior on specular reflections and direct supervision on diffuse shading using a rough shape proxy. We show convincing qualitative results on in-the-wild data and introduce a benchmark for quantitative evaluation on this new task.
Recognition of White-Cane Users from Surveillance Camera Images Based on Detection of Line Segments

Accidents caused by visually impaired individuals in public spaces are becoming social problems. In this study, we proposed a recognition method of white-cane users walking in public spaces on the basis of the video images obtained by surveillance cameras. The proposed method was applied to actual camera images, and experimental results were shown.
Asymptotic Analysis of the Convergence Time of Autoregressive Kalman Filters

In recent years, the Kalman filter has become the prime approach for estimating parameters that evolve following some dynamic model and prior statistics. In addition, recent contributions are introducing the use of autoregressive models in the state-space formulation to deal with correlated Gaussian-distributed magnitudes. However, the derivation of closed-form expressions for predicting their performance during the design stage is still an open problem. In that regard, in this letter we derive novel approximate closed-form upper bounds to characterize the convergence time of autoregressive Kalman filters. To this end, we extend a batch mode-based approach previously proposed in the literature that reveals the need for a dedicated dual-asymptotic analysis for this kind of techniques. Simulations are provided to show the goodness of the derived results.
Architectures for Autonomy: Towards an Equitable Web of Data in the Age of AI

Today, the Web connects over half the world's population, many of whom use it to stay connected to a multiplicity of vital digital public and private services, impacting every aspect of their lives. Access to the Web and underlying Internet is seen as essential for all—even a fundamental human right [7]. However, many contend that the power structure on large swaths of the Web has become inverted; they argue that instead of being run for and by users, it has been made to serve the platforms themselves, and the powerful actors that sponsor such platforms to run targeted advertising on their behalf. In such an ad-driven platform ecosystem, users, including their beliefs, data, and attention, have become traded commodities [13].
Adapting Ensemble Neural Networks to Clinical Prediction in High-Dimensional Settings

Neural networks have been investigated as models for survival data using a training criterion similar to that of the Cox proportional hazards model, a criterion not designed for clinical prediction. In this paper, we develop a new survival learning algorithm where a neural network ensemble minimizes the integrated Brier score. We compare the results obtained with this method to a standard implementation of random survival forests in R and to an ensemble of linear units.
Anomalous Action Recognition Research for Few-shot Learning

The research of action recognition has achieved excellent progress and results, but the common datasets in action recognition research usually have a large number of samples and are suitable for the training of neural network, such as UCF-101 and Kinetics. For anomalous action research, it is difficult to collect and make datasets due to the rare occurrence of anomalous actions. Therefore, the research on few-shot learning of action recognition has certain significance. Based on the above, this paper studies the few-shot learning of anomalous action recognition. We present an anomalous action recognition network based on few-shot learning. We clip the videos in UCF-Crime datasets and other videos collected on the network to make a new dataset named UCF-Crime+. The action recognition network pre-trained by kinetics named I3D is used to extract the feature of the anomalous action video. We use a few-shot learning network, named prototypical network to classify the video feature samples, so as to realize the few-shot learning and classification of the anomalous video. In order to increase the difference between clusters, we use the triplet loss function. At the same time, the feature fusion of I3D network is carried out, which increases the utilization and fusion of deep and shallow features. The experimental results show that the improved few-shot learning model has higher accuracy than the base network.