Reviewerly/exHarmony huggingface dataset:
Expanding Your Geographic Horizons

Throughout their careers, English-language educators who expand their geographic horizons through travel to foreign countries acquire skills and knowledge unavailable without such efforts. Becoming familiar with new colleagues, supervisors and students at work enhances our professional effectiveness in teaching, research and service, as well as administration, materials development, and advocacy. Making the effort to expand our horizons allows us to contribute more effectively to our home communities through the impact we make there. In this chapter, we seek to contribute practical perspectives that provide information on opportunities for expanding our cultural horizons beyond our familiar geographical locations.
A Comparative VHDL Implementation of Advanced Encryption Standard Algorithm on FPGA

The advanced encryption standard (AES) offers more flexibility in terms of implementing it in software and on hardware. Since field programmable gate arrays (FPGA’s) provide excellent performance in terms of parallel processing and simpler design cycles. This research intends to examine the required setup time, delay from clock to external outputs, logic and route utilization at input and output stage. The performance of AES algorithm is evaluated by simulating this symmetric algorithm on two different FPGAs using hardware description language (HDL). Xilinx ISE Design Suite is the software which helps in simulating, optimizing a synthesizable HDL code. Xilinx Spartan Family and Virtex Family devices are used for software evaluation, and the results of Xilinx Virtex are superior and efficient because of less delays.
Decision Making and Control of a Cognitive Agent’s Knowledge Process Under Time Constraints

Considered are the questions of designing a system for modeling the reasoning of a cognitive agent, capable of making conclusions based on its knowledge and observations of the external environment, solving problems in a hard enough real-time mode. To work in this mode, the existence of a critical time threshold is established, which is set to solve the problem facing the agent. Exceeding the threshold is fraught with grave, sometimes catastrophic consequences and for the agent is unacceptable. The formal basis of the modeling system (cognitive process control) is a logical system - extended step theory, that combines the concepts of active temporal logic and logical programming. Among the original methods proposed by the authors in the work, an approach to combining the concepts of active logic and logical programming in one logical system should be noted; an approach to constructing a consistent declarative semantics for extended step theory of active logic; a method of formalizing temporal, nonmonotonic reasoning of an agent using extended step theory of active temporal logic; a method of granulating time in a logical system to formalize meta-reasoning. A subclass of temporal logic is considered, oriented to application in real-time systems. Additionally, the issues of managing the agent’s cognitive process in hard real-time, eliminating anomalies (unforeseen situations), and applying the temporal logic of branching time are investigated in more detail.
Application of the “World Café” methodology in pedagogical research

Recent transformations in pedagogical theory and practice arouse interest in innovative methods. The developed in the late XX century methodology of conducting an informal focused discourse &amp;ldquo;World Caf&amp;eacute;&amp;rdquo; can be applied for solution of complex issues, integration of opinions of the experts in different fields, arrangement of group work, recapitulation, exchange of experience, etc. Currently, this technique enjoys popularity worldwide. The subject of this research is the history of creation, functional capabilities and peculiarities of application of the &amp;ldquo;World Caf&amp;eacute;&amp;rdquo; methodology in pedagogical research. The article presents a brief overview of on the history of its emergence. The fundamental principles and rules of its implementation are outlined. The article reveals the peculiarities of application of &amp;ldquo;World Caf&amp;eacute;&amp;rdquo; in scientific-pedagogical purposes as a qualitative method of research of socio-constructivist direction, which facilitates the dialogue, opens access to knowledge and opinions of broad range of participants, advances mutual learning, as well as serves as the source of valuable information. The author analyzed Russian and foreign examples of application of &amp;ldquo;World Caf&amp;eacute;&amp;rdquo; methodology for resolving the indicated issues. The article describes the characteristics of this method in comparison with such methods of obtaining quality information as individual interviews and focus groups. Comparative merits and flaws are identified.
On the Generation of Disassembly Ground Truth and the Evaluation of Disassemblers

When a software transformation or software security task needs to analyze a given program binary, the first step is often disassembly. Since many modern disassemblers have become highly accurate on many binaries, we believe reliable disassembler benchmarking requires standardizing the set of binaries used and the disassembly ground truth about these binaries. This paper presents (i) a first version of our work-in-progress disassembly benchmark suite, which comprises $879$ binaries from diverse projects compiled with multiple compilers and optimization settings, and (ii) a novel disassembly ground truth generator leveraging the notion of "listing files'', which has broad support by clang, gcc, icc, and msvc. In additional, it presents our evaluation of four prominent open-source disassemblers using this benchmark suite and a custom evaluation system. Our entire system and all generated data are maintained openly on GitHub to encourage community adoption.
ICE-Talk: An Interface for a Controllable Expressive Talking Machine.

ICE-Talk is an open source web-based GUI that allows the use of a TTS system with controllable parameters via a text field and a clickable 2D plot. It enables the study of latent spaces for controllable TTS. Moreover it is implemented as a module that can be used as part of a Human-Agent interaction.
Twitter Sentiments Analysis Using Machine Learning

Twitter sentiment analysis is the method of Natural Language Processing (NLP). In this project named Twitter sentiment Analysis we analyze the sentiments behind the twitter’s tweet. We have three type of sentiment: Positive, Neutral and Negative. Analyzing the sentiments behind every tweet is the biggest problem in the early days but now it can be solved with the help of Machine Learning. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters and through the Twitter Sentimental Analysis we can analysis the mood of the person who tweet which can helps in the industries to analyze the market and their product reviews or we can know the sentiments behind the opinion on any topic on which the group of people tweet and through this we can find the final result that the people point on view on the particular topic, product and any other tweets suggestions.
Induction of the transformation rules of the natural language problem statement into a semantic model for generating solver

The author considers a problem of automatic synthesis (induction) of the rules for transforming the natural language formulation of the problem into a semantic model of the problem. According to this model a program that solves this problem can be generated. The&amp;nbsp; problem is considered in relation to the system of generation, recognition and transformation of programs PGEN ++. Based on the analysis of literary sources, a combined approach was chosen to solve this problem, within which the rules for transforming the natural language formulation into a semantic model of the problem are generated automatically, and the specifications of the generating classes and the rules for generating a program from the model are written manually by a specialist in a specific subject area. Within the framework of object-event models, for the first time, a mechanism for the automatic generation of recognizing scripts and related entities (CSV tables, XPath functions) was proposed. Generation is based on the analysis of the training sample, which includes sentences describing objects in the subject area, in combination with instances of such objects. The analysis is performed by searching for unique keywords and characteristic grammatical relationships, followed by the application of simple eliminative-inducing schemes. A mechanism for the automatic generation of rules for replenishing / completing the primary recognized models to full meaning ones is also proposed. Such generation is performed by analyzing the relations between the objects of the training sample, taking into account information from the specifications of the classes of the subject area. The proposed schemes have been tested on the subject area "Simple vector data processing", the successful transformation of natural language statements (both included in the training set and modified) into semantic models with the subsequent generation of programs solving the assigned tasks is shown.
Utilizing Graphical Elements for Concept Map Analysis to Design Teaching and Learning Assessment

The graphical elements as parts of concept map construction are employed to assess both learning and teaching. Augmenting the use of concept maps, this study examines the graphical elements, such as, nodes, edges, cliques, diameters, travelling paths and structures of the graphs to relate to ones’ understanding to a topic, in this case, polynomials for middle school. In the aspect of teaching assessment, the teacher’ concept map drawn according to the lesson plan is served as the master map, which echoes the teacher’s expectation of students’ learning. On the other hand, students’ maps also reveal their understanding through the nodal relationship, which can be the definitions of terms, related examples, graph representation and algebraic manipulation. Data collection includes a focus group of 10 students and 1 teacher undergoing the concept map assessment task with restricted node terms. Graphically analyzed, students’ concept maps reveal some common elements as in the teacher’s map. In addition, the interview with the teacher also suggests that concept map as the assessment tool is an effective teaching reflection for which the teacher can see what to fulfill for future classes.
A symmetric protocol to establish service level agreements

We present a symmetrical protocol to repeatedly negotiate a desired service level between two parties, where the service levels are taken from some totally ordered finite domain. The agreed service level is selected from levels dynamically proposed by both parties and parties can only decrease the desired service level during a negotiation. The correctness of the protocol is stated using modal formulas and its behaviour is explained using behavioural reductions of the external behaviour modulo weak trace equivalence and divergence-preserving branching bisimulation. Our protocol originates from an industrial use case and it turned out to be remarkably tricky to design correctly.
Optimal support vector machine and hybrid tracking model for behaviour recognition in highly dense crowd videos

Purpose Object detection models have gained considerable popularity as they aid in lot of applications, like monitoring, video surveillance, etc. Object detection through the video tracking faces lot of challenges, as most of the videos obtained as the real time stream are affected due to the environmental factors. Design/methodology/approach This research develops a system for crowd tracking and crowd behaviour recognition using hybrid tracking model. The input for the proposed crowd tracking system is high density crowd videos containing hundreds of people. The first step is to detect human through visual recognition algorithms. Here, a priori knowledge of location point is given as input to visual recognition algorithm. The visual recognition algorithm identifies the human through the constraints defined within Minimum Bounding Rectangle (MBR). Then, the spatial tracking model based tracks the path of the human object movement in the video frame, and the tracking is carried out by extraction of color histogram and texture features. Also, the temporal tracking model is applied based on NARX neural network model, which is effectively utilized to detect the location of moving objects. Once the path of the person is tracked, the behaviour of every human object is identified using the Optimal Support Vector Machine which is newly developed by combing SVM and optimization algorithm, namely MBSO. The proposed MBSO algorithm is developed through the integration of the existing techniques, like BSA and MBO. Findings The dataset for the object tracking is utilized from Tracking in high crowd density dataset. The proposed OSVM classifier has attained improved performance with the values of 0.95 for accuracy. Originality/value This paper presents a hybrid high density video tracking model, and the behaviour recognition model. The proposed hybrid tracking model tracks the path of the object in the video through the temporal tracking and spatial tracking. The features train the proposed OSVM classifier based on the weights selected by the proposed MBSO algorithm. The proposed MBSO algorithm can be regarded as the modified version of the BSO algorithm.
Testing Homogeneity for Normal Mixture Models: Variational Bayes Approach

The test of homogeneity for normal mixtures has been conducted in diverse research areas, but constructing a theory of the test of homogeneity is challenging because the parameter set for the null hypothesis corresponds to singular points in the parameter space. In this paper, we examine this problem from a new perspective and offer a theory of hypothesis testing for homogeneity based on a variational Bayes framework. In the conventional theory, the constant order term of the free energy has remained unknown, however, we clarify its asymptotic behavior because it is necessary for constructing a hypothesis test. Numerical experiments shows the validity of our theoretical results.
Design and Implementation of Aerial Vehicle Remote Sensing and Surveillance System, Dehazing Technique Using Modified Dark Channel Prior

After studying the aviation problems in Iraq, on one hand, and because Iraq has various weather characteristics due to its different terrains (i.e.mountains, plain fields, and deserts).We discussed these problems with pilots and aviation specialists, and came to the conclusion that the main offset faced is vision deficiency is foggy and dusty weather.Thus, the Iraqi aviation reconnaissance system was developed to overcome this hurdle by implementing instant image rendering by developing this technology to remove fog and such hazy particles utilizing dark channel prior (DCP) image processing algorithm since this method is highly capable of resolving the fog/haze weather condition problems.The DCP algorithm suggested showed a reasonable enhancement of the images with foggy, hazy, and dusty conditions when the Unmanned Aerial Vehicle (UAV) was used to capture images at various altitudes correspondent to the same height most helicopters and reconnaissance UAVs fly at.Our main contribution is based on the evaluation of optimal values of DCP parameters/metrics to the hazy images and performs UAV image dehazing and enhancement.
REFINING THE SOLAR CLIMATE OF KRASNODAR FOR THE EFFECTIVE WORK OF SOLAR PANELS IN RESIDENTIAL BUILDINGS. RESEARCH RESULTS FOR 2014-2020

The article is a continuation of the article entitled "Reconciliation of the solar climate of Krasnodar for the effective operation of solar panels in residential buildings" published in the Bulletin of BSTU named after V.G. Shukhov in 2017. At present, the vast majority of stationary weather stations are located at a sufficiently large distance from the zone in which it is necessary to assess the potential of renewable energy resources. The article presents the results of field studies of the solar climate of Krasnodar conducted from 2014 to 2020. The results of the study are compared with normative documentation and a climate guide. The use of solar panels is important factor to reduce harmful emissions into the atmosphere. For the correct selection of the number of solar panels, it is necessary to know the exact amount of solar energy that will fall on this panel. This information allows to calculate the amount of energy that will be generated by the building’s solar energy system or solar power station. The correct selection of the number of solar panels affects the quality of life of people and allows to fully cover the energy consumption of buildings and structures using solar energy as the main source of energy.
Feed‐forward timing estimation for burst signals in non‐cooperative communication

Timing estimation is the most crucial part of the modulation classification and demodulation of burst signals. The traditional methods in classical cooperative communication fail to provide satisfying accuracy in non-cooperative communication. A novel timing estimation method was proposed in this study, which involves a carefully designed optimisation function constituted by piecewise over code period. Moreover, a closed-form analytical solution of the proposed optimisation problem has been provided, which significantly increases the estimation performance with affordable complexity. In detail, the proposed method demonstrates almost five times higher accuracy than the traditional methods in the low signal-to-noise ratio (SNR) scenario, while approaches the modified Cramer–Rao bound in the high SNR scenario.
VR as a Persuasive Technology “in the Wild”. The Effect of Immersive VR on Intent to Change Towards Water Conservation

The combination of VR with the correct psychological mechanism could become a powerful persuasion system to stimulate intent to change towards important environmental issues such as water conservation. However, very limited research has been reported on VR usage in this area. Therefore, we conducted a between-groups study to investigate whether the level of presence felt in a VR environment together with a trigger mechanism such as guilt could spark intent to change towards water conservation. Participants were exposed to a persuasive message about water conservation in one of three conditions: audio only, simple VR and visually rich VR. Forty participants completed the study “in the wild”. The results showed that while intent to change increased in all three groups, both VR groups indicated lower levels of change than the audio only group. Additionally, a positive correlation, albeit small, was found between presence and cued recall along with presence and intent to change. These results furthermore showed that presence could play a role in behavior modification and intent to change.
YouTube as a means of developing Chinese students’ listening skills when learning Russian language for business communication

The article deals with the possible options of using videos posted on YouTube when teaching Chinese students the Russian language for business communication. The purpose of the viewings is to improve and develop the listening skills of Chinese students. The subjects of the video clips are doing business in China, developing business relations between Russia and China. The article presents examples of tasks for the video stories.
Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation

Efficient tracking algorithms are a crucial part of particle tracking detectors. While a lot of work has been done in designing a plethora of algorithms, these usually require tedious tuning for each use case. (Weakly) supervised Machine Learning-based approaches can leverage the actual raw data for maximal performance. Yet in realistic scenarios, sufficient high-quality labeled data is not available. While training might be performed on simulated data, the reproduction of realistic signal and noise in the detector requires substantial effort, compromising this approach. Here we propose a novel, fully unsupervised, approach to track reconstruction. The introduced model for learning to disentangle the factors of variation in a geometrically meaningful way employs geometrical space invariances. We train it through constraints on the equivariance between the image space and the latent representation in a Deep Convolutional Autoencoder. Using experimental results on synthetic data we show that a combination of different space transformations is required for meaningful disentanglement of factors of variation. We also demonstrate the performance of our model on real data from tracking detectors.
Bringing Schrödinger’s cat to life with non-equilibrium respiration

In this study, we have proposed a method based on non-equilibrium effects to generate the superposition of macroscopically distinguishable quantum states, known as Schrodinger cat states, by using a Mach-Zehnder interferometry type experiment. Interaction of the input number state with a Kerr medium in the presence of a couple of heat baths in different temperatures in interaction picture and without imposing Markov assumption is considered. We have shown that the study of dynamics of the cat states under non-equilibrium condition open a way for the robustness of quantum features against the destructive role of the environment even at high temperature limit. It is verified that mutual influence of the environments, far from equilibrium, on the open system, makes it possible to revive quantum beats for longer time intervals. Moreover, we have probed how the traits of the environment, like its temperature and the Ohmic, super-Ohmic or sub-Ohmic functionality of the spectral density, may affect the pattern of the oscillation between alive or dead states of the cat.
Software Entity Recognition Method Based on BERT Embedding

The global open source software ecosystem contains rich information in the field of software engineering. The existing analysis methods for the text content of the knowledge community in this field are mainly focus on the structural relationship and rule-based association and mining. This paper proposes a software entity recognition method based on BERT word embedding. Firstly, the BiLSTM-CRF model is constructed, and the entity recognition model is constructed by combining the word vector embedding in software engineering field. Then, the word vector in the input layer of the model is improved by introducing the BERT pre-training language model. In the process of pre-training of BERT, the pre-training data should be constructed based on the discussion content of Stack Overflow software Q & A community. Then, we use these data to pre-training the BERT model, so as to obtain the word vector representation suitable for software engineering field, improving the effect of entity recognition in software engineering field, and solving the problem that the traditional word vector embedding is mostly based on the general domain data training, which is not fully suitable for software engineering field, and can’t well represent the context semantic information. At the same time, to solve the problem that there are few annotated data in the field of software, this paper tries to extends the data appropriately by the method of model prediction and dictionary matching, and carries out experimental test. Finally, this paper uses the method of deep learning to realize the entity recognition in the field of software engineering, so as to provide support for the extraction of software entities, the construction of software knowledge base, and the intelligent application of software engineering.