Reviewerly/exHarmony huggingface dataset:
International Cooperation At the University Level. Obstacles and Challenges for Incoming Students and How To Eliminate Them

This paper is about the cross-border cooperation between universities at the tertiary education level. The main issues presented in this work is related to students and the obstacles that they face with during applying, preparing and coming to any of the overseas education programs. The questions of how responsible people / entities from different universities can make things easier for incoming students and how universities can attract more participants to their international study programs, short-term cross-border events, etc. to support the internationalization and cross-border cooperation between universities / countries are described, additionally, the involved and responsible entities are identified. The research was done on the example of cooperation programs and activities of Dortmund University of Applied Sciences and Arts, Germany. The paper shows the main barriers, which were identified during the interviews and conducted questionnaires among the Ukrainian students, who were/are interested in the study programs of Dortmund International University of Applied Sciences and Arts (FH Dortmund) and current / former students of university's international Master programs as European Master in Project Management and Master Program of Embedded Systems.
LiveCore

Liveness is an important factor in live coding but frequently liveness focuses on high-level, textual environments. While these environments offer manifold abstraction capabilities, users of low-level dataflow programming environments could also benefit from increased liveness. In this work we introduce LiveCore: a macro library for the low-level dataflow environment Reaktor Core enabling live coding. LiveCore manages program state at audio rates and provides a suite of modules for musical pattern generation, sequencing and synthesis. LiveCore increases liveness in Reaktor Core from an editable dataflow program, to one with continuous audio suitable for musical performance. We reflect on the design process to discuss the qualitative differences of liveness in low-level dataflow programming, compared with other forms of live coding. We suggest that live coding in a low-level dataflow environment provides a uniquely immediate experience for the performer.
Convergence to pure steady states of linear quantum systems

We study the convergence of density operators of linear quantum systems towards their steady states in trace norm. We give compact and intuitive necessary and sufficient conditions for such linear quantum systems to have pure Gaussian steady states in terms of the Hamiltonian and coupling operators. Furthermore, using the nullifiers concept in Gu et al (2009 Phys. Rev. A 79), we show that set of the nullifiers of these states are spanned by the coupling operators and other operators obtained via the commutation of Hamiltonian and coupling operators.
HDGS: A Hybrid Dialogue Generation System using Adversarial Learning

Open domain dialogue generation has attracted much attention in the field of natural language processing (NLP). The previous work is basically based on retrieval methods or generative methods. Retrieval methods searches the pre-built database for the most appropriate human-generated sentences. The sentence generated by the retrieval methods are always grammatically accurate and fluent, but it can not generate sentences that have not been seen in the database. Generative methods can generate new sentences, but tend to produce dull, short sentences. In this paper, we propose a hybrid approach for open-domain dialogue generation. Our model combines the advantages of retrieval methods and generative methods. The system aims to produce sequences that are indistinguishable from human-generated dialogue. Therefore, we use adversarial to train our generative model, and we use reinforcement learning to solve the problem of not fully-differentiable in NLP. In addition, we cast the GAN-training as a in-filling task which can effectively reduce mode collapse and help with training stability. Experimental results show that our method can generate quality samples evaluated by human.
"Types of the Students’ Attitude to Self-Educational Activity"

The article presents the main results of the research of types of university students' attitude towards self-educational activities.The following types of students' attitudes toward self-educational activity are singled out.Harmonious type means that students are engaged in self-educational activities (related to the future profession that is got at the university, or unconnected directly with it), they are motivated to self-educational activities, realize it by own initiative, and realize its personal significance.Insufficiently conscious attitude means that students are not clearly aware of their personal significance doing self-education on their own initiative.Non-initiative attitude means that realizing the personal importance of self-educational activity, students require an external incentive
Online Local Boosting: Improving Performance in Online Decision Trees

As more data are produced each day, and faster, data stream mining is growing in importance, making clear the need for algorithms able to fast process these data. Data stream mining algorithms are meant to be solutions to extract knowledge online, specially tailored from continuous data problem. Many of the current algorithms for data stream mining have high processing and memory costs. Often, the higher the predictive performance, the higher these costs. To increase predictive performance without largely increasing memory and time costs, this paper introduces a novel algorithm, named Online Local Boosting (OLBoost), which can be combined into online decision tree algorithms to improve their predictive performance without modifying the structure of the induced decision trees. For such, OLBoost applies a boosting to small separate regions of the instances space. Experimental results presented in this paper show that by using OLBoost the online learning decision tree algorithms can significantly improve their predictive performance. Additionally, it can make smaller trees perform as good or better than larger trees.
Odeporica fantastica e lingue immaginarie

Viaggio in Drimonia is a collection of fantastic tales by Lia Wainstein (1919-2001), a journalist and translator born to a Russian Jewish family in Finland, raised in Italy and educated in Switzerland. One of the main point of interests of Wainstein’s tales is the imitation of travel literature and the description of imaginary countries, populations, and languages. The invention of linguistic otherness is often a narrative expedient for the Soviet dissident Wainstein to express her ideas on language policy and the fragile relationship between semantics, pragmatics and communication in contemporary societies.
Accelerating Video Processing Inside Embedded Devices to Count Mobility Actors

The actual number of surveillance cameras and the different methods for counting vehicles originate the question: What is the best place to process video flows? This work analyze techniques to accelerate a counting system for mobility actors like cars, pedestrians, motorcycles, bicycles, buses, and trucks in the context of an Edge computing application using deep learning. To solve this problem this study presents the analysis and implementation of different techniques based on the use of an additional hardware element as is the case of a Vision Processing Unit (VPU) in combination with methods that affect the resolution, bit rate, and time of video processing. For this purpose we consider the Mobilenet-SSD model with two approaches: a pre-trained model with known data sets and a trained model with images from our specific scenarios. Additionally, we compare an optimized model using OpenVINO toolkit and overclock of hardware. The use of SSD-Mobilenet’s model generates different results in terms of accuracy and time of video processing in the system. Results show that the use of an embedded device in combination with a VPU and video processing techniques reach 18.62 Frames per Second (FPS). Thus, video processing time is slightly superior (5.63 min) for a video of 5 min. Optimized model and overclock show improvements too. Recall and precision values of 91% and 97% are reported in the best case (class car) for the vehicle counting system.
Tracking Histogram of Attributes over Private Social Data in Data Markets

We propose new inference attacks to predict sensitive attributes of individuals in social attribute networks. Our attacks assume powerful adversaries with wide background knowledge leverage innocent individual information which is publicly available to predict unobserved attributes of targeted users. Given the growing availability of individual information online, our results have significant implications for data privacy preservation – the latent correlation between innocent data and sensitive data might be learned unless we sanitize data prior to publishing to protect against inference attacks. Existing inference attacks has high computational complexity by leveraging either the publicly available social friends or individuals’ observed social attributes, but not both. To mitigate the gaps, we develop an approach to integrate social structures and attributes into a probabilistic graphical model and predict unobserved attributes with linear complexity. Furthermore, we propose a differentially private approach to track histogram of attributes from social-attribute networks.
Multi-spectral planar imaging using a tuneable Lyot-Ehman filter

Abstract The paper provides results of the assessment of a multi-spectral 2D imaging by using a tuneable band-pass optical filter. The filter is based on the Lyot-Ehman physical principles and uses liquid crystal cells as polarization rotators. The spectral properties of the filter are tested by imaging fluorescence of a Rhodamine dye solution. Full width at half maximum of the band of the filter was found to be 9-10 nm in the tested range of 520-620 nm. The filter is applied for 2D imaging of local spontaneous Raman scattering in a laminar premixed Bunsen flame. It is shown that the local intensity of Stokes and anti-Stokes components of rovibrational transitions during the light scattering by nitrogen molecules can be captured by using this filter. It is also concluded that the filter can be used for 2D imaging of local fluorescence in gases or liquids.
ON THE ESSENCE AND DIFFERENT FORMS OF INTRODUCTION THE PRACTICAL-ORIENTATED TRAINING TO THE TEACHING-LEARNING PROCESS OF THE FUTURE MATHEMATICS’ TEACHERS AT INSTITUTIONS OF HIGHER EDUCATION

Practical-orientated form of organization of teaching-learning process is one of the modern educational trends for all levels and all directions of education. It is obvious, by this, that such teaching-learning process must have different content and different forms of implementation on various levels and directions of education. The fact is bound up both with the general purpose of education in every separate direction and with age and psychological peculiarities of those who are going through the educational process. The authors consider practical-orientated training on the level of higher education a training directed onto the deliberate application of its results during the subsequent stages of training and during the subsequent professional activities. The authors’ concept of the essence of practical-orientated teaching-learning process for future teachers of mathematics at institutions of higher education is represented in the paper. The concept has been created on the base of analyzing the possible directions of organization of practical-orientated teaching-learning process in mathematics for secondary education institutions students. At the present situation the authors suggest to redistribute credits and training material between subjects of higher mathematics and subjects of elementary mathematics and methods of teaching mathematics at general secondary education institutions in the way that eliminates the incontestable duplication in training material existing at present. By the authors’ opinion, the proposed redistribution will create the necessary pre-conditions for organizing the integrity system of practical-orientated training at the corresponding direction of higher education and will essentially improve the quality of the process of training as a whole. For the suggested concept the possible ways of its implementation to the real teaching-learning process are introduced. For the future teachers of mathematics at institutions of general secondary education it is advisable to begin the process of working out the corresponding experimental Educational Professional Program of training with the purpose of its subsequent approbation in the educational process.
An Approach to Design Trust-Aware Goal Model and Provenance Model for Intelligent Adaptive Systems

Intelligent Adaptive System is a proactive and flexible system that provides appropriate services to a user based on current situation. For the adaptation, a single system may not have enough capability to collect all required information, and as a result it needs to cooperate with other systems. One of the most important things in the cooperation is determining a reliable partner, because an unreliable cooperation may cause critical problems such as information hijacking or personal information extrusion. Trust can be one of the key solutions as the emerging quality attribute which is appropriate to the cooperation in the connected environments. In this research, we propose trust-aware requirements engineering framework with prove -nance model as new trust evidence. Based on the understanding about trust, we propose a method to define the trust-requiring situation and derive the trust-aware goal model in the requirements engineering process. Comparing to the traditional goal model, the trust-aware goal model can represent the trust-related requirements. It helps system engineer to analyze and develop the trust-aware system. In addition, we also need to analyze which information can represent and evaluate trust. Because the existing works cannot represent the various facets about trust, we suggest provenance model as the new trust evidence. Provenance model is the information about the history, derivation, origin, or context of a specific artifact. It can be used as the trust evidence, because the trust can be determined by the reputations from the historical contexts of the system.
A Semantic Representation of the Citation Structure

A scientific citation is usually represented as a relation between two publications without any precise meaning and inner structure. In fact, the structure of a citation, which is usually not represented explicitly, can be quite complicated. In our previous papers, we have proposed so-called expanded citations which allow representing the structure of a citation in a machine-readable way. In this short paper, we present and discuss selected structures of citations. In particular, we consider their meaning and possible application.
Exploring Double Cross Cyclic Interpolation in Unpaired Image-to-Image Translation

The unpaired image-to-image translation consists of transferring a sample a in the domain A to an analog sample b in the domain B without intensive pixel-to-pixel supervision. The current vision focuses on learning a generative function that maps both domains but ignoring the latent information, although its exploration is not explicit supervision. This paper proposes a cross-domain GAN-based model to achieve a bi-directional translation guided by latent space supervision. The proposed architecture provides a double-loop cyclic reconstruction loss in an exchangeable training adopted to reduce mode collapse and enhance local details. Our proposal has outstanding results in visual quality, stability, and pixel-level segmentation metrics over different public datasets.
Formants and Prosody-Based Automatic Tonal and Non-tonal Language Classification of North East Indian Languages

This paper proposes an automatic tonal and non-tonal language classification system for North East (NE) Indian languages using formants and prosodic features. The state-of-the-art system for tonal/non-tonal classification uses mostly prosodic features and considers the utterance-level analysis unit during feature extraction. To this end, the present work explores formants and studies if it has complimentary information with respect to prosody. It also analyzes different analysis units for feature extraction, namely syllable, di-syllable, word, and utterance. Classification techniques based on Gaussian mixture model—universal background model (GMM-UBM), neural network and i-vector have been explored in this work. The paper presents NIT Silchar language database (NITS-LD) prepared in-house to carry out experimental validation. It covers seven NE Indian languages and uses data from All India radiobroadcast news archives. Experimental analysis suggests that artificial neural network (ANN) based on syllable level features provides the lowest EERs of 31.8, 36 and 37.8% for test data of durations, 30, 10, and 3 s, respectively, when the combination of prosodic features and formants are used. The addition of formants helps to improve the system performance by up to 6.8, 7.8 and 9.2% for test data of the three different durations with respect to that of prosodic features.
Swarm Intelligence Based Ensemble Learning of Deep Neural Networks

Ensemble learning is a powerful tool in machine learning and it is very significant to utilize the mechanism of ensemble learning to improve the performance of deep learning for recognition and classification tasks. In this paper, we propose a general ensemble learning framework of deep neural networks based on swarm intelligence algorithms for solving the combination coefficients to the outputs of these component deep neural networks. We formulate the weights assigning problem for the deep neural networks as an optimization problem whose objective function is highly complicated and use swarm intelligence algorithms to solve it. We evaluate this ensemble learning framework on two real-world datasets, Market-1501 for person re-identification and CIFAR for image classification, and show that it outperforms a single deep neural network remarkably.
Design and Implementation of Engineering Standard Database System Based on Data Mining

In order to establish a public service platform for construction drawing intelligent review, an engineering standard database system based on Web crawler was proposed by building an engineering standard database system to improve the efficiency of querying engineering standards. The system mainly obtains the unstructured engineering standard information on the Web site through the crawler module, and extracts the information in the image on the Web site through the image recognition module, and then removes the non-critical information into the structured information through the data cleaning module, and finally the data update module ensures real-time data updates. When the user uses the system, the information in the database can be queried through the web page. Experimental research shows that the engineering standard database system can meet the daily needs of users.
Presentaciones con TIC versus preferencias de los estudiantes universitarios

Qualitative study of cases is carried out in order to know the opinion of university students on different models of presentation with TIC tools, considering that adapting them to their preferences can satisfactorily influence their motivation and performance. From the results obtained we can say that advices derived from the business sector can be considered valid for university classrooms presentations, , but improving its design in order to make them more visual and harmonious, with meaningful and interactive content, omitting formulas, data tables and spellings without a training purpose, and limiting exposure times and interspersing tasks of verification and consolidation of knowledge. It can be concluded by recommending new (multi-center) studies in order to adapt the ICT presentations to the preferences of current students, limiting their use in order to avoid the rejection and invalidity of this teaching-learning technology.
Development of an information system for storing digitized works of the Almaty Academgorodok research institutes

The present article describes the architecture of the integrated distributed information system created for storing digitized works of employees of Almaty Akademgorodok research institutes (Kazakhstan) and providing access to them using Web technology.Comparative analysis of two data storage systems for storing digitized works, Ceph and GlusterFS, is provided.The description of the software part of the information system is provided which consists of four subsystems: repository of digital objects, subsystem for managing current research information, subsystem of integration of distributed information resources, subsystem of access to distributed information resources based on Web technologies.The relation between the subsystems and their integration is described.The work defines the requirements to the repository of digital objects.The requirements for the repository of digital objects are defined; a comparative analysis of open source software used for these purposes is made.
A Variable Precision Approach for Deep Neural Networks

Deep Neural Network (DNN) architectures have been recently considered as the big breakthrough for a variety of applications. Because of the high computing capabilities required, DNN has been unsuitable for various embedded applications. Many works have been trying to optimize the key operations, which are multiply-and-add, in hardware for a smaller area, higher throughput, and lower power consumption. One way to optimize these factors is to use the reduced bit accuracy; for examples, Google's TPU used only 8-bit integer operations for DNN inference. Based on the characteristics of different layers in DNN, further bit accuracy can be changed to preserve the hardware area, power consumption, and throughput. In this work, the thesis investigates a hardware implementation of multiply-and-add with variable bit precision which can be adjusted at the computation time. The proposed design can calculate the sum of several products with the bit precision ranging from 1 to 16 bits. The hardware implementation results on Xilinx FPGA Virtex 707 development kit show that our design occupies smaller hardware and can run at a higher frequency of 310 MHz, while the same functionality implemented with and without DSP48 blocks can only run at a frequency of 102 MHz. In addition, to demonstrate that the proposed design is applicable effectively for deep neural network architecture, the paper also integrated the new design in the MNIST network. The simulation and verification results show that the proposed system can achieve the accuracy up of to 88%.