Reviewerly/exHarmony huggingface dataset:
Digital Transformation of Education, Science and Innovations

The article is devoted to the study of digital transformation of the «knowledge triangle» - science, education and innovations. Problems facing the Russian society with respect to interaction of these components are considered, available applied solutions are analyzed. Importance of the platform approach is substantiated: it is shown how the project "Creation and launching of the digital platform of knowledge exchange and copyrights management" will be implemented on the basis of the digital platforms available in the Russian Federation, the result of which will be the IPUniversity platform.
A systematic model parameter extraction using differential evolution searching

An evolutionary searching method for compact model parameter extraction is proposed. Optimum scale factor (SF) and crossover probability (CP) of the algorithm corresponded with the given task are investigated. The proposed method exhibits stable convergence behavior and superior accuracy compare to conventional methods.
Deep Semi-Supervised Person Re-Identification with External Memory

To overcome the scalability problem of supervised person re-identification (Re-ID), we consider the semi-supervised person Re-ID problem of learning from a limited number of labeled images of a few identities and a large number of unlabeled images. To this end, we propose an external-memory-based deep semi-supervised person Re-ID model (EDS). Based on the external memory, two loss functions are designed so as to effectively cope with the relation between labeled and unlabeled data for overcoming the limitation of batch size in each epoch in deep learning. Therefore, an effective deep semi-supervised learning method can be performed. Extensive experiments validate the superiority of the proposed method for semi-supervised person Re-ID.
Efficient Implementation of Convolutional Neural Networks with End to End Integer-Only Dataflow

Linear INT8 quantization is presented to construct an end to end integer-only dataflow for efficient inference of modern CNNs. The INT8 method is implemented with unified layer representation, thus quantized CNNs can be partitioned into computation subgraphs consisting of stacked unified layers with simplified integer-only arithmetic flow and scaling back mechanism, indicating high effectiveness for specific hardware realization. Experimental results show that both the classification and object detection models quantized by proposed INT8 method suffer approximate 1% accuracy loss, exhibiting comparable results with TensorRT. As a result, the deep learning accelerator (DLA) with integer-only dataflow and efficient memory hierarchy is designed for CNN applications.
Automated Synthesis of Secure Platform Mappings

System development often involves decisions about how a high-level design is to be implemented using primitives from a low-level platform. Certain decisions, however, may introduce undesirable behavior into the resulting implementation, possibly leading to a violation of a desired property that has already been established at the design level. In this paper, we introduce the problem of synthesizing a property-preserving platform mapping: synthesize a set of implementation decisions ensuring that a desired property is preserved from a high-level design into a low-level platform implementation. We formalize this synthesis problem and propose a technique for generating a mapping based on symbolic constraint search. We describe our prototype implementation, and two real-world case studies demonstrating the applicability of our technique to the synthesis of secure mappings for the popular web authorization protocols OAuth 1.0 and 2.0.
The Neural Network Method for Detection and Recognition of Moving Objects in Trajectory Tracking Tasks according to the Video Stream

The neural network method for detecting and recognizing moving objects in the tasks of trajectory tracking according to the data from surveillance cameras is offered. The new method is effective in terms of accuracy and high-speed performance. The adapted architectures of neural networks are offered to identify vehicles, people and situations.
Path Spaces of Higher Inductive Types in Homotopy Type Theory

The study of equality types is central to homotopy type theory. Characterizing these types is often tricky, and various strategies, such as the encode-decode method, have been developed. We prove a theorem about equality types of coequalizers and pushouts, reminiscent of an induction principle and without any restrictions on the truncation levels. This result makes it possible to reason directly about certain equality types and to streamline existing proofs by eliminating the necessity of auxiliary constructions. To demonstrate this, we give a very short argument for the calculation of the fundamental group of the circle (Licata and Shulman [1]), and for the fact that pushouts preserve embeddings. Further, our development suggests a higher version of the Seifert-van Kampen theorem, and the set-truncation operator maps it to the standard Seifert-van Kampen theorem (due to Favonia and Shulman [2]). We provide a formalization of the main technical results in the proof assistant Lean.
Critical success factors in library reengineering: a case of academic libraries in Kenya

Purpose Academic libraries are reengineering their services to address the gap brought about by the convergence of physical and virtual information landscape. Despite the reengineering efforts, as many as 70 per cent of the libraries do not achieve the expected results. Certain critical success factors need to be in place for successful implementation. This paper aims to investigate the key success factors to service reengineering in an effort to establish their extent of application in university libraries in Kenya. Design/methodology/approach A multiple case study approach of six purposively selected private and public university libraries in Kenya based on Webometric ranking as an indicator of reengineering was used. Data were collected through face-to-face interviews with 30 librarians. The qualitative data collected were analyzed through content analysis. Findings The following key success factors were found to have been considered during reengineering: top management commitment, planning, provision of required resources, appropriate IT infrastructure, presence of skilled and competent staff, value of the library to the university and teamwork. Despite the central role of training, change management program and having a clear vision and objectives, university libraries in Kenya did not pay any attention to them as deserved. The authors conclude that university libraries in Kenya have not optimally achieved the desired reengineering results as a result of overlooking training and change management as critical success factors. The paper recommends that the identified critical success factors should be considered wholly not in isolation. Research limitations/implications The study was carried out among the top-ranked universities according to the January 2017 Webometric ranking; and therefore the findings may give a general indication of the critical success factors considered by university libraries in Kenya. However, this pre-condition may have locked out other libraries that may have made some recommendable strides in re-engineering their services. Originality/value This study has practical implications in identifying the critical success factors for library service reengineering to provide insights on the factors to consider as librarians reengineer library services.
The Parallel CMAC Authenticated Encryption Algorithm for Satellite Communication

The evolution in digital communication technology has provoked the need for providing efficient security services for communication. The data security services majorly includes; confidentiality, authenticity, and integrity services. The Authenticated Encryption (AE) algorithm provides all these three services. Initially, AE algorithms were proposed with serial architecture. These AE algorithms are low throughput as compared to parallel architecture algorithms such as Galois Counter Mode (GCM) algorithm. In this work, we have presented a new AE algorithm with parallel architecture named as Parallel Cipher-based Message Authentication Code (PCMAC). The PCMAC AE algorithm is consists of Advanced Encryption Standard (AES) algorithm in Counter mode (CTR) and parallel implementation of the CMAC authentication algorithm. The proposed PCMAC algorithm is implemented on FPGA for showing its utility for high throughput applications. In this work, we compared the PCMAC AE algorithm implementation with the GCM AE algorithm implementation. The experimental results show that the throughput of PCMAC algorithm for pipelined implementation is 41.45 Gbps.
Deep cycle autoencoder for unsupervised domain adaptation with generative adversarial networks

Deep learning is a powerful tool for domain adaptation by learning robust high‐level domain invariant representations. Recently, adversarial domain adaptation models are applied to learn representations with adversarial training manners in feature space. However, existing models often ignore the generation process for domain adaptation. To tackle this problem, deep cycle autoencoder (DCA) is proposed that integrates a generation procedure into the adversarial adaptation methods. The proposed DCA consists of four parts, a shared encoder, two separated decoders, a discriminator and a linear classifier. With the labelled source images and unlabelled target images as inputs, the encoder extracts high‐level representations for both source and target domains, and the two decoders reconstruct the inputs with the latent representations separately. The shared encoder is pitted against the discriminator; the encoder tries to confuse the discriminator while discriminator aims at distinguishing which domain the latent representations come from. DCA adopts both adversarial loss and maximum mean discrepancy loss in the latent space for distribution alignment. The classifier is trained with both the source original and reconstructed image representations. Extensive experimental results have demonstrated the effectiveness and the reliability of the proposed methods.
Performance Evaluation of Real-Time System for Vision-Based Navigation of Small Autonomous Mobile Robots

Real-time operation systems (RTOS) have become very important to the development of autonomous mobile robots. The choice of RTOS has tremendous sway with processor utilization, response time, and real-time jitter. In this paper, we present experimental trials and analyze the feasibility of RTOS on a single-board computer for image recognition and vision-based navigation of small autonomous robot. Several real-time (RT) patches Linux frequently used not only in robotics are implemented and tested on the Raspberry Pi2 equipped with a native camera board. To study the speed of image recognition (classification) OpenCV library was used. Test results show that the RT Patch Linux can produce higher throughput compared to Xenomai, but it can be seen that RT systems almost did not affect the speed of static images recognition systems almost did not affect the speed of image recognition.
Towards the Development of a Smart Wearable Device Based on Electrically Conductive Yarns

This paper proposes smart wearable device based on electrically conductive yarns. The proposed platform collects data from two independent accelerators and gyroscopes and sends it via Wi-Fi to a server for processing and storage. All electrical connections between a microcontroller, gyroscopes and others parts of the smart wearable device are created using electrically conductive yarns. These conductive connections are sewn with embroidery machine Barudan BEXT-S1501CII. Different pattern designs and stitches are presented and a functional prototype is shown. The prototype’s main computing unit is based on Arduino platform because of its simplicity and ease of implementation.
Adaptive representation of dynamic 3D meshes for low-latency applications

Recently, 3D visual representations of highly deformable 3D models, such as dynamic 3D meshes, are becoming popular due to their capability to represent realistically the motion of real-world objects/humans, paving the road for new and more advanced immersive virtual, augmented and mixed reality experiences. However, the real-time streaming of such models introduces increasing challenges related to low cost, low-latency and scalable coding of the acquired information. In view of this, this article proposes an efficient scalable coding mechanism, that decomposes a mesh sequence into spatial and temporal layers that remove a single vertex at each layer. The removed vertices are predicted by performing Laplacian interpolation of the motion vectors. The artifacts that are introduced in low-resolution representations are mitigated using a subspace based normal-vector denoising procedure, that is optimized to support low-latency streaming scenarios using incremental SVD. A novel initialization strategy offers robustness to outliers generated due to local deformations. An extensive evaluation study using several synthetic and scanned dynamic 3D meshes highlights the benefits of the proposed approach in terms of both execution time and reconstruction quality even in very low throughput scenarios of bit-per-vertex-per-frame (bpvf).
The Design of Lightweight and Multi Parallel CNN Accelerator Based on FPGA

In this paper, a lightweight, parallel computing and storage, high energy efficiency CNN accelerator based on FPGA was designed. According to the characteristics of forward inference structure of deep convolution neural network, the parallel characteristics of convolution operation are analyzed from two aspects of computational efficiency and data reuse rate, and the full parallel pipeline implementation of full connection layer is studied. The accelerator has adopted full parallel pipeline structure to improve the computational efficiency, and making full use of various convolution parallel computing architectures to balance the requirements of computing efficiency, parameters and data load bandwidth, a lightweight and tightly coupled accelerator architecture is designed based on the characteristics of hardware resources such as internal computing, logic and storage in FPGA. In the validation test of open data set based on ImageNet, the accelerator is deployed on Xilinx Virtex7-690T FPGA with 28nm technology, under the working frequency of 200MHz, the maximum number of multiplication and addition operation is 1728 times per cycle, the peak operation rate is 691.2Gops, the energy ratio is 138.24Gops/w. In the end, the classification results are consistent with the processing results using double-precision floating-point data format.
Model Comparison Games for Horn Description Logics

Horn description logics are syntactically defined fragments of standard description logics that fall within the Horn fragment of first-order logic and for which ontology-mediated query answering is in PTime for data complexity. They were independently introduced in modal logic to capture the intersection of Horn first-order logic with modal logic. In this paper, we introduce model comparison games for the basic Horn description logic hornALC (corresponding to the basic Horn modal logic) and use them to obtain an Ehrenfeucht-Fraïssé type definability result and a van Benthem style expressive completeness result for hornALC. We also establish a finite model theory version of the latter. The Ehrenfeucht-Fraïssé type definability result is used to show that checking hornALC indistinguishability of models is ExpTime-complete, which is in sharp contrast to ALC indistinguishability (i.e., bisimulation equivalence) checkable in PTime. In addition, we explore the behavior of Horn fragments of more expressive description and modal logics by defining a Horn guarded fragment of first-order logic and introducing model comparison games for it.
Sentiment Analysis Of English Tweets: A Comparative Study of Supervised and Unsupervised Approaches

Currently, social networks have become the core of internet daily usage. Their popularity is increasing among the public users every day. Therefore, they can be considered as the main resource for gathering people's opinions and sentiments towards different topics. In this study, a comparison of three different machine learning algorithms used for sentiment analysis: Support Victor Machine, Random Forest Classification and K-mean Clustering is conducted. Unigrams and bigrams are used as features for all approaches. The results show that Support Vector Machine outperforms the other approaches.
Three Approaches to Design Engineering in the Health Domain: A Systemic Perspective

Abstract This paper proposes a categorization of existing approaches to healthcare-related engineering design based on systems thinking principles. Three existing approaches to healthcare-related engineering design are isolated which contribute differently to health systems fundamental purposes and interconnections. The three approaches are labeled as 'silent', 'overt', and 'convergent' health design. Each approach is defined and illustrated through an example. Following, practical advantages and disadvantages of each approach are discussed. A reflection is offered on the expected relevance of the convergent health design approach for present and future societal challenges in the health domain, and specifically on the recently growing field of e-health. Finally, open methodological challenges related to convergent health design are outlined and characterized as opportunities for future research.
Ether

Multi-tenant datacenters and cloud networks must provide both isolation and interactive service to tenant applications, many of which are sensitive to tail flow completion times. Network operators must also ensure high utilization of network capacity to reduce cost. Existing approaches that statically partition network capacity, in either time or space, provide good isolation but suffer from under-utilization. Existing schemes that dynamically allocate capacity to tenants incur either decreased fairness or high tail flow completion times. To overcome these limitations, we propose Ether. Ether is able to overcome these limitations because it can prioritize bursty flows during short congestion episodes while still ensuring fairness at long timescales. In this paper, we present a preliminary design of Ether and discuss its feasibility in today's programmable switches. Our evaluations show that, at high loads, Ether achieves 23% improvement in tail flow completion times (FCT) when compared with idealized fair queueing (FQ) while still providing similar fairness as FQ. In contrast, pFabric, which optimizes FCT, worsens fairness by a factor of 1.8 when compared with Ether.
Dependently typed Haskell in industry (experience report)

Recent versions of the Haskell compiler GHC have a number of advanced features that allow many idioms from dependently typed programming to be encoded. We describe our experiences using this "dependently typed Haskell" to construct a performance-critical library that is a key component in a number of verification tools. We have discovered that it can be done, and it brings significant value, but also at a high cost. In this experience report, we describe the ways in which programming at the edge of what is expressible in Haskell's type system has brought us value, the difficulties that it has imposed, and some of the ways we coped with the difficulties.
AIM: An Interactive Ashtray to Support Behavior Change through Gamification

Abstract Littering is a highly diffused anti-environmental and anti-social behavior, especially among young people. Furthermore, cigarette butts are one of the most littered items and are responsible for both severe environmental damages and high clean up expenses. The aim of this project is to design an interactive ashtray for the campus environment to limit the cigarette butts littering behavior in an engaging and effective way. Qualitative and quantitative data are collected. Coded observations were implemented through the research process, including the 2 pre (without the prototype) and 2 pros (with the prototype) sessions. Also, user experience test and one to one interview were conducted for deepening the understanding of the littering phenomenon and the reasons behind in the behavior among young people. The prototype indeed reduced the number of cigarette butts littering among observed behaviors of 156 students, especially in male sample. Final results indicate the behavior change of disposers is moderated by other factors, as the environmental cleanliness. Future development is also discussed.
Revelio: A MimbleWimble Proof of Reserves Protocol

We reveal Revelio, a new privacy-preserving proof of reserves protocol for Grin exchanges. By design, Revelio allows the detection of collusion between exchanges while hiding the identities of the outputs owned by the exchange in a larger anonymity set of outputs.
Digital tool marks (DTMs): a forensic analysis of file wiping software

Whilst difficult to ascertain the full extent to which so called anti-forensic software applications are in use by the public, their threat to an investigation of digital content is tangible, where of particular interest is the use of file wiping tools, which remains the focus of this work. This work presents the examination of eight freely available wiping tools in order to identify the existence of ‘digital tool marks’ (DMTs) left on a system following their use. Further attempts are made to ascertain whether such DTMs can be attributable to a particular wiping tool. Analysis is focused on the impact each tool has on system at a file system level, where in this work both FAT32 and NTFS are the subject of investigation. DMTs relating to each wiping tool are provided and recoverable file system metadata post-wipe is described.
Improving the Adaptive Properties of LSHADE Algorithm for Global Optimization

Differential Evolution is a really challenging algorithm in the field of computational intelligence and has proved its worth in solving various real-world optimization problems. The algorithm since its inception has been enhanced to improve its competitiveness and various new versions have been designed. In present work, the properties of an enhanced version of DE namely LSHADE algorithm are enhanced and new version namely SALSHADE is proposed. The newly proposed version consists of three major modifications that is, i) exponentially decreasing crossover rate, ii) linearly decreasing scaling factor and iii) frequency component is enhanced by using L/evy distributed step size. These three modifications have been added and experimental analysis is done on CEC2017 benchmark problems to prove its worth. The new proposed SALSHADE algorithm is compared with SaDE, JADE, SHADE, MVMO, CVsin, CV1.0, LSHADEcnEpSin and other algorithms. Further, experimental results show that SALSHADE is highly competitive and is a potential candidate for becoming state-of-the-art.