Reviewerly/exHarmony huggingface dataset:
Teaching Academic English to Russian-speaking Academic Staff: The Case of the Office of Academic Writing

The paper discusses the preliminary results of teaching academic writing to scientific and academic staff at South Ural State University (national research university) within the framework of advanced training courses. The 36 and 72-hour training programs were developed by the academic and methodological department of the Office of Academic Writing. The Office was opened at the university in 2016 in order to improve the academic literacy of the university’s academic staff, develop professional communication skills in English, and assist authors who aspire to get their papers published in top rated scientific journals indexed in the scientific citation databases Scopus and Web of Science. Upon completion of these programs, authors of scientific papers gain knowledge about the basics of academic writing, the structure of a scientific paper, the English scientific style and its lexical and grammatical features, as well as editing a paper, working with scientific citation databases, and elaborating a publishing strategy. We conducted a study from October 2016 to December 2018 and calculated data on the attendees’ affiliation (an institute or higher school), the contingent (position), and the importance of the offered training programs (the number of attendees in each course). The obtained data indicates that representatives of social-humanitarian and technical fields show a special interest in the courses. The courses of academic writing are most popular among associate professors who have experience in publishing papers in Russian. Authors are especially interested in intensive practice-oriented programs. Upon completion of the courses and with the support of the Office of Academic Writing, attendees prepared scientific texts in English and submitted them to top rated journals and conference proceedings. The educational activity of the Office of Academic Writing can be developed through elaboration and implementation of new advanced training programs, including the distance ones. Their effectiveness will be evaluated by an increase in the publication activity of the university’s employees.
A passive sensing device for a cloud on the skyline detection

There is a set of sensing devices for a cloud on sky detection. The common passive examinations of cloud presence are realized with digital post processing of picture that was taken with fish eye lenses. Therefore, results of examinations are delayed and information at skyline is degraded. The common examinations include also thermal image analysis or the active laser distant sensors adaptations. The common feature of these sensors is the high cost of equipment. Thus, sensing device constructions for real-time measurements of clouds taking place on the skyline have been examined. Examinations were performed at the same time with two sensing devices that include two different optical channels. The first sensing device, used as a laboratory reference, consists of two spectrometers Maya 2000 pro while mechanic module consists of a plate with large core optical fiber adapter and fiber optic collimator adapter. These adapters are used to fix collimator and an optical fiber that is connected to a proper spectrometer. The examination results enable to point relation of variation of cloud cover factor as variation in semi-stable band 440 nm and variation in an unstable band that extends to longer than 550 nm wavelengths. Therefore, the second device, aimed as a cost effective consist of two optoelectronic sensors type BH1750 and OPT101 that are connected by a mechanical adapter that includes optical filters to binoculars. These optical filters are 440 nm band pass and 550 nm long pass. The data obtained of examinations show that the second construction may be a base for further development of data processing algorithms for passive sensing device for a cloud on the skyline detection.
Memory-Optimized Voronoi Cell-based Parallel Kernels for the Shortest Vector Problem on Lattices

In this paper we propose a parallel implementation of a Voronoi cell-based algorithm for the Shortest Vector Problem for both CPU and GPU architectures. Additionally, we present an algorithmic simplification with particular emphasis on significantly reducing the memory usage of the implementation. According to our tests, the parallel multi-core CPU implementation scales linearly with the number of cores used, and also benefits from simultaneous multi-threading, achieving a maximum speedup of 5.56× for 8 threads. The parallel GPU implementation obtains speedups of 13.08×, compared with the sequential CPU implementation. The acceleration of this class of signal processing algorithms is a fundamental step in the evolution of post-quantum cryptanalysis. Currently, the best algorithms can take months to process for moderately low dimensions.
A Novel ALU Circuit based on Reversible Logic

We propose a new ALU circuit based on reversible logic. The ALU circuit implements two addition methodologies. The outputs are generated at some fixed lines for each arithmetic or logic function. A satisfactory tradeoff is achieved between the line count and the quantum cost. Reduction in ancillary inputs and garbage outputs causes a decrease in fabrication cost. The proposed designs outperform the earlier designs with respect to delay, line count and number of operations. The libraries NOT–CNOT–V–[Formula: see text] are used to optimize the quantum cost of the proposed designs.
Depth Augmented Semantic Segmentation Networks for Automated Driving

In this paper, we explore the augmentation of depth maps to improve the performance of semantic segmentation motivated by the geometric structure in automotive scenes. Typically depth is already computed in an automotive system to localize objects and path planning and thus can be leveraged for semantic segmentation. We construct two networks that serve as a baseline for comparison which are “RGB only” and “Depth only”, and we investigate the impact of fusion of both cues using another two networks which are “RGBD concat”, and “Two Stream RGB+D”. We evaluate these networks on two automotive datasets namely Virtual KITTI using synthetic depth and Cityscapes using a standard stereo depth estimation algorithm. Additionally, we evaluate our approach using monoDepth unsupervised estimator [10]. Two-stream architecture achieves the best results with an improvement of 5.7% IoU in Virtual KITTI and 1% IoU in Cityscapes. There is a large improvement for certain classes like trucks, building, van and cars which have an increase of 29%, 11%, 9% and 8% respectively in Virtual KITTI. Surprisingly, CNN model is able to produce good semantic segmentation from depth images only. The proposed network runs at 4 fps on TitanX GPU, Maxwell architecture.
An Augmented Reality (AR) Experience for Lorenzo Lotto

Virtual interactive experience created for the Picture gallery of Jesi (Italy). Namely, three interactive works realized with Unreal Engine 4 to give the spectator a greater immersiveness on the immortal pictures of Lorenzo Lotto. The goal was achieved by creating three choreographies with audio supervised by a historian, recreation of the works with three-dimensional graphics and a specially composed soundtrack by Tecla Zorzi. The augmented reality (AR) application was realized specifically for Android tablets.
Predictive analysis by ensemble classifier with machine learning models

Classification of machine learning models had an ultimate achievement by means of supervised learning, but the 'state-of-art models' have not yet extensively applied the 'biological image data.' To order the erythrocytes as jungle fever contaminated or not, we sort erythrocytes through an outfit arrangement strategy. With this respect, the content strived to delay our previous 'choice tree based paired classifier' to achieve the outfit grouping. Preparing information which is given bunched into assorted gatherings depend on the assortment saw in every single likely component's projection. Each bunch is locked in to a more prominent degree to prepare a single classifier. In this regard, morphological highlights and entropy-based highlights are incorporated. From authentic pathology research facilities, the examination carried on the constant information sources and blend of benchmark datasets were created as mysterious information. In contrast to the current techniques, the proposition of this archive executed trials on voluminous information that multiply in size when contrasted with present benchmark datasets. With the measurable appraisal the execution of proposition is assessed by near investigation in the midst of the two existing models 'Intestinal sickness contaminated erythrocyte arrangement dependent on a half and half classifier utilizing minute pictures of flimsy blood smear (Hybrid Classification Approach)' and 'Scale to Estimate Premature Malaria Parasites Scope (SEMPS)' and the proposed model 'Cuckoo Search Based Ensemble Classifier (CSEC).' As delineated by the exploratory examination the recommended model is outperforming the two existing models.
Research on Weibo user behavior system for subjective perception and big data mining technology

Firstly, the paper analyzes the information propagation mode of Sina Weibo, and proposes a new MURank algorithm based on PageRank algorithm, which fully considers the relationship between users, the transmission relationship between users and microblogs, and the forwarding relationship between micr oblogs. At the same time build the user-microblog mode diagram. The design of the thesis is also more in line with the information dissemination mode of the actual Weibo platform. Experiments prove that MURank can effectively solve the impact of “zombie powder” and “level sinking” problems, and more realistically reflect the level of influence of users. Secondly, the paper combines BP neural network algorithm to improve the QoE hierarchical index system, and proposes a five-level index model. The mobile video service is taken as an example to illustrate the establishment process of the indicator system and the QoE quantitative evaluation method. By verifying on the open source distributed platform Hadoop platform, it is found that the proposed method is a solution to deal with massive data analysis.
Interactive 360° Video and Storytelling Tool

Existing 360° video players on the market playback only one type of media in their timeline. This paper introduces a new tool that allows creating interactive 360° videos with storytelling elements that can switch between flat and 360° videos in one timeline. The tool also integrates other media types like images, audio, and web resources that can be interlinked with each other to create one or multiple branched stories via non-linear video technology. The benefits of this approach are far-reaching, i.e., it allows broadcasters to create genres of programming that combine both traditional footage and immersive 360° video segments in one seamless experience. It enables a highly effective and attractive lean-back and lean-in experience for the audience. Moments of immersion can then be coupled with traditional storytelling, which is an attractive option for content creators who can enhance existing material with immersive moments in a way that is cost effective and allows them to fall back on their pre-existing skills as story and program creators. It will pave the way for much broader adoption and production of spherical content among content providers of the first wave, as it lowers the barrier of entry. The tool will enable content producers and editors to create stories in 360° videos and in combination with non-360° media content by providing an easy-to-use web-based editor that targets devices with various characteristics and input capabilities such as TVs, tablets and head-mounted displays (HMDs).
Analysis of Measurement System Accuracy Based on 2D Laser Triangulation Scanner When Measuring Soiled Pipe Thread Pitch and Height

An experimental setup was assembled for precision measuring of soiled pipe nipple thread parameters. The experimental setup error was evaluated in accordance with ISO 10360-8 standard. Law of distribution was defined for thread pitch and height values measured by setup. Special composite material casts were used to verify laser scanner accuracy during thread pitch and height measurement. Casts from the thread surface were measured using a precise optical microscope to find pitch and height values with the accuracy ten times greater, than the accuracy of the assembled experimental setup. Fifteen independent measurements were made to evaluate the relative error of the pitch and height values. Comparison of the microscope and setup measurement results was made to evaluate the absolute error of the pitch and height values. The possibility of achieving by 2D laser triangulation scanner of regulatory requirements to the measurement accuracy of API 5CT standard for the soiled threaded pipe was confirmed experimentally.
Investigating Users' Perceptions of Light Behaviors in Smart-Speakers

Light expressions can communicate and convey information in an unobtrusive manner. Smart-speakers employ light behaviors to indicate a wide range of device states as well as notifying users. However, no prior work has looked into the efficacy of these light behaviors in smart-speakers. That is, can users distinguish and understand information states associated with different light behaviors in smart-speakers? In this work, we aim to address this gap by investigating whether users can accurately identify light behaviors in Amazon Echo and Google Home devices. For this, we conducted an MTurk survey with 243 smart-speaker owners. Our findings reveal that only 34% of the light behaviors are correctly recognized by users on average. Moreover, we found that users find it easier to recognize light behaviors in Amazon Echo than in Google Home devices. These findings show a clear need for rethinking the design of light behaviors in smart-speakers. We also explored novel light behaviors that users might find useful but are not supported by current devices including expressing sentiment and privacy notifications.
Comparison Between RSA Algorithm and Modified RSA Algorithm Used in Cloud Computing

The primary operation performed by cloud computing is to provide dynamic storage capacity and security to its users. The main benefit from using the cloud computing is the reduction in the economic expenditure and ease in the accessibility of data. This whole mechanism involves various security algorithms; still there are some security issues which needs to be solved. In this paper, various security algorithms have been discussed in order to analyze the performance of the algorithms and to find out that which encryption algorithm is better for the data protection in the cloud computing platform. The mainly concerned algorithms in this paper are RSA Algorithm and Modification in RSA Encryption Algorithm (MREA). One of the modifications in RSA encryption algorithm is named as Homomorphic Encryption Algorithm.
Within-Class Scatter Constraint Based Randomized Autoencoder for One-Class Classification

One-class classification (OCC) has attracted a lot of attentions for its strong applicability of outlier/anomaly detection in real-world applications. Among them, the mixed model based on the autoencoder (AE) is the commonly used OCC architecture. The recent random neural network based extreme learning machine (ELM) AE (ELM-AE) has been extended to OCC. But no constraints have been applied to the encoded feature in ELM-AE and the random parameters used in ELM-AE may result in meaningless features. In this paper, we propose a novel within-class scatter constraint based randomized AE (WSC-RAE) by introducing a constraint on the distribution of the encoded feature to restrict the solution space and thus avoid the meaningless features. The proposed WSC-RAE is then embedded into a multilayer one-class ELM (ML-OCELM) framework for OCC. Experiments on 17 benchmark datasets are carried out to demonstrate the superiority of WSC-RAE.
New Image Encryption using Chaotic Map in Wavelet Domain

Multiple image encryption is the prime concern of data security technology due to prevalence of capacity in storage and data transmission. In our paper we have introduced three-level multiple image encryption technique which uses Robust chaotic map. Firstly, the input images are concatenated and undergo 2D discrete wavelet transform and the image that is obtained is composed of low frequency components. Then using Arnold map image is being scrambled. Following to which encryption key generated from RCM is diffused with scrambled image. Since each key is unique it becomes difficult to decrypt and get original data. Again, using Henon map the scrambling is performed to acquire encrypted image. Lastly the performance analysis is carried out to know the security level of the system using histogram, correlation coefficients and NPCR and UACI values.
Purification of the atom-field interaction Hamiltonian

We study the Jaynes-Cummings model with initial mixed states, for the atom or the field, are considered. The evolved mixed field density matrix is purified to a wavefunction that describes the interaction between a quantised field and an artificial four-level atom. This allows us to use the Araki-Lieb inequality to calculate the field entropy from the atomic entropy. We then suggest Hamiltonians that reproduce the field entropy dynamics. We finally show that more realistic Hamiltonians may be considered by using two entangled two-level atoms.
Entrepreneurial Potential and Opportunities for Its Activization by Students of HR-Managers in the Educational Program of the University

The main goal of the modern strategy of professional education is the formation of a competent specialist, competitive and ready for potential development and self-realization in society. Entrepreneurial potential is a set of diff erent abilities (updated and reserve), knowledge, skills and abilities of a person, allowing to adequately interact with the ever-changing organizational and economic environment. The article reveals the essence of entrepreneurial potential, analyzes its constituent elements, presents the structure. Ways of formation of professionally important qualities and competences for development of entrepreneurial potential of students within improvement of educational programs and educational process at University are indicated.
Substitution Box Design Based from Symmetric Group Composition

Abstract This work shows a new design of substitution box (S-Box) construction for the implementation in the block cipher. The S-Box is the only component in block cipher such as advanced encryption standard (AES) that possess the nonlinearity characteristics. Hence, it is crucial to properly design so that it able resist the cipher against known attack such as linear and differential attacks. Thee construction is based on composition of permutation within the symmetric group. This paper focus on the AES S-Box class which received 8-bit input and produced 8-bit output. This bijective S-Box consist of 256 elements which later involve in the process of composition. Initially, a set of 30 S-Box with high nonlinearity is generated using 30 irreducible polynomials under the finite field GF (2 8 ). These S-Box is then undergoing two rounds of composition which finally yield about 1.62 million S-Box. More than half of the generated S-Box achieve the nonlinearity of at least 100 with the maximum recorded nonlinearity of 110. This method also guarantee that the generated S-Box is bijective. To show the security level of our construction method, a comparison to other constriction methods is conducted. The methods introduced in this paper have slightly higher nonlinearity compared to several construction with the value of differential uniformity not on the par as AES however comparable to other similar heuristic construction.
Collaborative Wireframing for Model-Driven Web Engineering

Today's Model-Driven Web Engineering (MDWE) approaches automatically generate Web applications from conceptual, domain-specific models. This enhances productivity by simplifying the design process through a higher degree of abstraction. Due to this raised level of abstraction, the collaboration based on conceptual models also opens up new use cases, such as the end user's tighter involvement into Web development. However, especially in the early design stages of Web applications, common practices for requirement elicitation mostly rely on paper prototypes or wireframes instead of MDWE, created usually in analog settings. The digitization of this process, combined with the benefits of model-driven development, bears a lot of potential for improving MDWE practices. In this contribution, we enhance an existing MDWE approach with wireframing capabilities, realized through real-time synchronization of models, wireframes and code. We present the conceptual considerations of our approach, the realization of the synchronous wireframing tool and the synchronization between wireframe and model. Our evaluation results for collaborative Web development tasks are promising and open the gate towards novel, collaborative and agile MDWE techniques.
TCP‐based traceroute: An evaluation of different probing methods

Traceroute is the most commonly used tool, not only for network diagnostics, but also for discovering the topology of the Internet. We evaluated the discovery capability of three variations of TCP‐based traceroute: the first uses SYN segments as probes, the other two operate on an existing connection and use DATA and ACK segments as probes. Experimental results show that using different types of probes is useful to obtain a richer view of the Internet, both in terms of IP interfaces and autonomous systems.
Online Schemaless Querying of Heterogeneous Open Knowledge Bases

Applications that depend on a deep understanding of natural language text have led to a renaissance of large knowledge bases (KBs). Some of these are curated manually and conform to an ontology. Many others, called open KBs, are derived automatically from unstructured text without any pre-specified ontology. These open KBs offer broad coverage of information but are far more heterogeneous than curated KBs, which themselves are more heterogeneous than traditional databases with a fixed schema. Due to the heterogeneity of information representation, querying KBs is a challenging task. Traditionally, query expansion is performed to cover all possible transformations and semantically equivalent structures. Such query expansion can be impractical for heterogeneous open KBs, particularly when complex queries lead to a combinatorial explosion of expansion possibilities. Furthermore, learning a query expansion model requires training examples, which is difficult to scale to diverse representations of facts in the KB. In this paper, we introduce an online schemaless querying method that does not require the query to exactly match the facts. Instead of exactly matching a query, it finds matches for individual query components and then identifies an answer by reasoning over the collective evidence. We devise an alignment-based algorithm for extracting answers based on textual and semantic similarity of query components and evidence fields. Thus, any representational mismatches between the query and evidence are handled online at query-time. Experiments show our approach is effective in handling multi-constraint queries.