Reviewerly/exHarmony huggingface dataset:
Cooperation Between University and Business Community: Experience and Development Priorities

The modern universities play a key role in the socio-economic development of a region of their location. The long-term partnerships between educational organizations and employers contribute to a stronger competitive position of the region as a center of intellectual resources and human capital, foster innovative development of entrepreneurship forms and strengthening of the regional economy. The article focuses on the practical implementation of the main forms of cooperation between higher education system and business community on the example of Orenburg State University. The authors have systematized and grouped the organizational and conceptual forms and methods of interaction. The conclusion formulates the need to introduce an integrated practiceoriented approach in the field of interaction between universities and enterprises-employers in order to train competitive specialists for the regional economy in accordance with the project “Young professionals” within the national project “Education”. ки региона.
Challenges and Requirements of Immersive Media in Autonomous Car: Exploring the Feasibility of Virtual Entertainment Applications

Abstract This paper investigated the influence of VR-entertainment systems on passenger and entertainment experience in vehicles with smooth movements. To simulate an autonomous driving scenario, a tablet and a mobile VR-HMD were evaluated in a dynamic driving simulator. Passenger, user and entertainment experience were measured through questionnaires based on comfort/discomfort, application perception, presence, and simulator sickness. In two experiments, two film sequences with varying formats (2D versus 3D) were presented. In Experiment 1, the established entertainment system (tablet + 2D) was tested against a possible future one (HMD + 3D). The results indicated a significantly more favorable experience for the VR-HMD application in the dimensions of user experience (UX) and presence, as well as low simulator sickness values. In Experiment 2, the film format was held constant (2D), and only the device (tablet versus HMD) was varied. There was a significant difference in all constructs, which points to a positive reception of the HMD. Additional analyses of the HMD device data for both experiments showed that the device and not the film format contributed to the favorable experience with the HMD. Additionally, the framework to evaluate the new application context of VR as an entertainment system in autonomous vehicles was discussed.
Identifying Semantics in Clinical Reports Using Neural Machine Translation

Clinical documents are vital resources for radiologists when they have to consult or refer while studying similar cases. In large healthcare facilities where millions of reports are generated, searching for relevant documents is quite challenging. With abundant interchangeable words in clinical domain, understanding the semantics of the words in the clinical documents is vital to improve the search results. This paper details an end to end semantic search application to address the large scale information retrieval problem of clinical reports. The paper specifically focuses on the challenge of identifying semantics in the clinical reports to facilitate search at semantic level. The semantic search works by mapping the documents into the concept space and the search is performed in the concept space. A unique approach of framing the concept mapping problem as a language translation problem is proposed in this paper. The concept mapper is modelled using the Neural machine translation model (NMT) based on encoder-decoder with attention architecture. The regular expression based concept mapper takes approximately 3 seconds to extract UMLS concepts from a single document, where as the trained NMT does the same in approximately 30 milliseconds. NMT based model further enables incorporation of negation detection to identify whether a concept is negated or not, facilitating search for negated queries.
A 3D Mobility Model for Autonomous Swarms of Collaborative UAVs

Collaboration between several Unmanned Aerial Vehicles (UAVs) can produce high-quality results in numerous missions, including surveillance, search and rescue, tracking or identification. Such a combination of collaborative UAVs is referred to as a swarm. These several platforms enhance the global system capabilities by supporting some form of resilience and by increasing the number and/or the variety of the embedded sensors. Furthermore, several UAVs organized in a swarm can (should the ground control station support this) be considered as a single entity from an operator point-of-view. We aim at using such swarms in complex and unknown environments, and in the long term, allow compact flights. Dynamic path planning computation for each UAV is a major task to perform their mission. To define this path planning, we have implemented a three-dimensional (3D) mobility model for swarms of UAVs using both the Artificial Potential Fields (APF) principle and a global path planning method. In our model, the collaboration between the platforms is made by sharing information about the detected obstacles. To provide a significant validation of our mobility model, we have simulated real-world environments and real-world sensors characteristics, using the OMNeT ++ network simulator.
Validation of surface solar irradiances estimates and forecast under clear-sky conditions from the CAMS McClear model in Benguerir, Morocco

McClear is a fast model of surface solar irradiance under cloudless condition (clear-sky), based on the radiative transfer model libRadtran that exploits the clear sky atmospheric optical properties (notably partial and total aerosol optical depths —AOD— at different wavelengths, total contents of water vapor and ozone) provided by the EU-funded Copernicus Atmospheric Monitoring Service (CAMS). This model provides global, diffuse horizontal irradiances and direct normal irradiance at any place and any time since 2004 onwards, with 2 days of delay, with temporal resolution up to 1 min (interpolation). In this paper, we present a first validation of the McClear clear sky irradiance model for the specific climate of Benguerir, Morocco. McClear’s validation was performed by comparing the 1 min average clear sky irradiance data (GHI: Global Horizontal Irradiance and DNI: Direct Normal Irradiance) from a high precision thermopile-based pyranometric station installed in Benguerir at Green Energy Park, and the values estimated by McClear for the same instants. The bias for global horizontal irradiance is 5 W m-2 (0.6 % of the mean observed irradiance). The root means square error (RMSE) is 17 W m-2 (2.2 %) and the correlation coefficient (CC) is 0.996. The bias for the direct normal irradiance is -17 W m-2 (-1.9 %). The RMSE is 58 W m-2 (6.7 %) and the correlation coefficient (CC) is 0.845. The same types of comparison have been done to assess the ability of McClear to forecast clear-sky GHI and DNI one day-ahead, using the provided forecasts of the state of the clear atmosphere by CAMS. The Bias, RMSE, STDE and correlation coefficient of such comparisons on the average 1-min profile of GHI and DNI from McClear are respectively for GHI - 0.8%, 3.3%, 3.3% and 0.991. For DNI -2.8%, 9.9%, 9.5% and 0.779.
The Validity, Generalizability and Feasibility of Summative Evaluation Methods in Visual Analytics

Many evaluation methods have been used to assess the usefulness of Visual Analytics (VA) solutions. These methods stem from a variety of origins with different assumptions and goals, which cause confusion about their proofing capabilities. Moreover, the lack of discussion about the evaluation processes may limit our potential to develop new evaluation methods specialized for VA. In this paper, we present an analysis of evaluation methods that have been used to summatively evaluate VA solutions. We provide a survey and taxonomy of the evaluation methods that have appeared in the VAST literature in the past two years. We then analyze these methods in terms of validity and generalizability of their findings, as well as the feasibility of using them. We propose a new metric called summative quality to compare evaluation methods according to their ability to prove usefulness, and make recommendations for selecting evaluation methods based on their summative quality in the VA domain.
A Fractal Based Image Cipher Using Knuth Shuffle Method and Dynamic Diffusion

This paper proposes a fractal-based image encryption algorithm which follows permutation-substitution structure to maintain confusion and diffusion properties.The scheme consists of three phases: key generation process; pixel permutation using the Knuth shuffle method; and the dynamic diffusion of scrambled image.A burning ship fractal function is employed to generate a secret key sequence which is further scanned using the Hilbert transformation method to increase the randomness.The chaotic behavior of the fractal strengthens the key sensitivity towards its initial condition.In the permutation phase, the Knuth shuffle method is applied to a noisy plain image to change the index value of each pixel.To substitute the pixel values, a dynamic diffusion is suggested in which each scrambled pixel change its value by using the current key pixel and the previously ciphered image pixel.To enhance the security of the cryptosystem, the secret key is also modified at each encryption step by performing algebraic transformations.The visual and numerical analysis demonstrates that the proposed scheme is reliable to secure transmission of gray as well as color images.
CameraNet: A Two-Stage Framework for Effective Camera ISP Learning

Traditional image signal processing (ISP) pipeline consists of a set of individual image processing components onboard a camera to reconstruct a high-quality sRGB image from the sensor raw data. Due to the hand-crafted nature of the ISP components, traditional ISP pipeline has limited reconstruction quality under challenging scenes. Recently, the convolutional neural networks (CNNs) have demonstrated their competitiveness in solving many individual image processing problems, such as image denoising, demosaicking, white balance and contrast enhancement. However, it remains a question whether a CNN model can address the multiple tasks inside an ISP pipeline simultaneously. We make a good attempt along this line and propose a novel framework, which we call CameraNet, for effective and general ISP pipeline learning. The CameraNet is composed of two CNN modules to account for two sets of relatively uncorrelated subtasks in an ISP pipeline: restoration and enhancement. To train the two-stage CameraNet model, we specify two groundtruths that can be easily created in the common workflow of photography. CameraNet is trained to progressively address the restoration and the enhancement subtasks with its two modules. Experiments show that the proposed CameraNet achieves consistently compelling reconstruction quality on three benchmark datasets and outperforms traditional ISP pipelines.
Barriers for fast matrix multiplication from irreversibility

Determining the asymptotic algebraic complexity of matrix multiplication, succinctly represented by the matrix multiplication exponent ω, is a central problem in algebraic complexity theory. The best upper bounds on ω, leading to the state-of-the-art ω ≤ 2.37.., have been obtained via the laser method of Strassen and its generalization by Coppersmith and Winograd. Recent barrier results show limitations for these and related approaches to improve the upper bound on ω.We introduce a new and more general barrier, providing stronger limitations than in previous work. Concretely, we introduce the notion of of a tensor and we prove (in some precise sense) that any approach that uses an irreversible tensor in an intermediate step (e.g., as a starting tensor in the laser method) cannot give ω = 2. In quantitative terms, we prove that the best upper bound achievable is lower bounded by two times the irreversibility of the intermediate tensor. The quantum functionals and Strassen support functionals give (so far, the best) lower bounds on irreversibility. We provide lower bounds on the irreversibility of key intermediate tensors, including the small and big Coppersmith-Winograd tensors, that improve limitations shown in previous work. Finally, we discuss barriers on the group-theoretic approach in terms of monomial irreversibility.
Flower Pollination Neural Network For Heart Disease Classification

Abstract Heart Disease are among the leading cause of death worldwide. The application of artificial neural network as decision support tool for heart disease detection have been previously proposed. However, artificial neural network using conventional back propagation algorithm for error minimization and these algorithm tend to stuck at local minima. This paper proposed the use of flower pollination algorithm as a substitute to conventional back propagation algorithm for error minimization. Heart disease dataset obtain from UCI machine learning repository is used to evaluate the performance of the proposed framework. The results show that the proposed flower pollination neural network able to produce higher classification accuracy compared to the conventional back propagation neural network algorithm.
Quantization of deep neural networks for accumulator-constrained processors

We introduce an Artificial Neural Network (ANN) quantization methodology for platforms without wide accumulation registers. This enables fixed-point model deployment on embedded compute platforms that are not specifically designed for large kernel computations (i.e. accumulator-constrained processors). We formulate the quantization problem as a function of accumulator size, and aim to maximize the model accuracy by maximizing bit width of input data and weights. To reduce the number of configurations to consider, only solutions that fully utilize the available accumulator bits are being tested. We demonstrate that 16-bit accumulators are able to obtain a classification accuracy within 1\% of the floating-point baselines on the CIFAR-10 and ILSVRC2012 image classification benchmarks. Additionally, a near-optimal $2\times$ speedup is obtained on an ARM processor, by exploiting 16-bit accumulators for image classification on the All-CNN-C and AlexNet networks.
Blockchain in Agriculture

Agriculture is the production of food, fibre and data. The data attests to the qualities and properties of the food and fibre, and is therefore economically valuable. Yet while data is cheap to add, it is often costly to verify. In consequence, a significant percentage of the final cost of agricultural produce goes to costs of establishing provenance, proving compliance with standards and regulations, undergoing inspections, audits, and process monitoring, as well as costs of intermediation, quality assurance, and branding. Blockchain, a new technology that enables different parties along a supply chain to trust digital data (it is sometimes called a ‘trustless’ technology), has the potential to lower transaction costs and improve the efficiency of agricultural supply chains by reducing the need for monitoring and verification of data. Yet while hugely promising, the technology is still new and experimental, and faces a number of significant barriers to adoption.
AI vs AI: fraudsters turn defensive technology into an attack tool

The first rule of managing online fraud and mitigating risk is to remember that fraudsters are entrepreneurs. While it's tempting to think of those committing digital fraud as hoody-wearing lone wolves spending hours in their bedroom working to weasel their way into someone's online account, in reality professional fraud operations look more like the JP Morgan trading floor. Cyber criminals are not simple, hoodie-wearing lone wolves. Many are sophisticated fraud operations using the most advanced technology, including artificial intelligence (AI). The energy and ingenuity with which fraud rings and cyber criminals have deployed AI-based solutions has matched that of the businesses and organisations that work to protect themselves from bad actors. Machines have been put to malicious use in ways ranging from click farms to complex model extraction schemes, explains Swami Vaithianathasamy of Signifyd.
A Survey of Multi-Space Techniques in Spatio-Temporal Simulation Data Visualization

The widespread use of numerical simulations in different scientific domains provides a variety of research opportunities. They often output a great deal of spatio-temporal simulation data, which are traditionally characterized as single-run, multi-run, multi-variate, multi-modal and multi-dimensional. From the perspective of data exploration and analysis, we noticed that many works focusing on spatio-temporal simulation data often share similar exploration techniques, for example, the exploration schemes designed in simulation space, parameter space, feature space and combinations of them. However, it lacks a survey to have a systematic overview of the essential commonalities shared by those works. In this survey, we take a novel multi-space perspective to categorize the state-of-the-art works into three major categories. Specifically, the works are characterized as using similar techniques such as visual designs in simulation space (e.g, visual mapping, boxplot-based visual summarization, etc.), parameter space analysis (e.g, visual steering, parameter space projection, etc.) and data processing in feature space (e.g, feature definition and extraction, sampling, reduction and clustering of simulation data, etc.).
Смыслотехники, обращенные к субъектному опыту обучающегося как основе его ценностно-смыслового выбора на примере математики

Introduction. The use of various psychological techniques in learning, which are capable of actualizing students’ unique subjective experience, may impart a personal and meaningful orientation to the learning process. Subjective experience is a basis for value-meaning choices, which are considered as a point of contact between students’ meanings and the meanings of culture. This contact can help students transform their meaning propensities. This paper proposes a new classification for meaning techniques that are oriented towards students’ experience and presents the results for their testing in order to increase students’ motivation and the development of their meaning sphere in mathematics classes.&#x0D; Methods. This study used (a) the questionnaire developed by the authors to identify students’ value attitude towards mathematics, their subjective position, and possibility of choosing in learning and (b) the revised Life Orientation Test (LOT-R, Russian version; D.A. Leontiev). The sample was comprised of 34 first-year students of Platov South-Russian State Polytechnic University (Novocherkassk).&#x0D; Results. The findings from a questionnaire survey at the initial and final stages of the pedagogical experiment demonstrated the effectiveness of meaning techniques for the educational process. The Life Orientation Test findings speak in favor of students’ subjective position at the forming experimental stage.&#x0D; Discussion. As an integration of modern psychological and pedagogical technologies, the classification of psychotechnics proposed in this study makes it possible to control students’ emotions/feelings and values/motivation in choice situations and reveals individuals’ personal and meaning potential. The use of meaning techniques oriented towards students’ subjective experience as the basis for their value-meaning choices in mathematics classes is possible if teachers construct the educational content on a meaning basis.
Content-based Image Retrieval of Color, Shape and Texture by Using Novel multi-SVM Classifier

Background: the considerable time consumption, query retrieval difficulty and reduced retrieval rate.Still remaining challenges in Content-based image retrieval.Methods: in this work, we propose a pre-processing method that uses a Gaussian filter to improve quality by reducing image noise.An effective feature extraction method for in presented to extracted texture help color co-occurrence feature (CCF), color and shape features such as area and diameters.The colors features are extracted by means of a grey-level co-occurrence matrix and bit pattern.Extracting these features will enhance the image retrieval accuracy.With the use of a novel multi-SVM classifier, classification is performed and image retrieval is completed effectively.Results: performance measures, namely, precision, recall, error rate, correct rate, and retrieval rate, are computed.The proposed methodology produces superior results on these measures and exhibits an effective retrieval rate of approximately 94.92%; therefore, our technique is more efficient than existing MRED and MALP methods.
Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT

Liver segmentation from CT is regarded as a prerequisite for computer-assisted clinical applications. However, automatic liver segmentation technology still faces challenges due to the variable shapes and low contrast. In this paper, a patient-specific probabilistic atlas (PA)-based method combing modified distance regularized level set for liver segmentation is proposed. Firstly, the similarities between training atlases and testing patient image are calculated, resulting in a series of weighted atlas, which are used to generate the patient-specific PA. Then, a most likely liver region (MLLR) can be determined based on the patient-specific PA. Finally, the refinement is performed by the modified distance regularized level set model, which takes advantage of both edge and region information as balloon force. We evaluated our proposed scheme based on 35 public datasets, and experimental result shows that the proposed method can be deployed for robust and precise liver segmentation, to replace the tedious and time-consuming manual method.
Small Face Detection Using Deep Learning on Surveillance Videos

Face detection is one of the essential tasks widely studied in the field of Computer Vision.Several authors have developed different techniques to improve the face detection in images, but these are limited in their application on videos and more if they present low resolution.In this study, we propose a new model for face detection in low-resolution videos based on the morphology of the upper body of people, and the use of Deep Learning (CNN).Our results show an average of 39% accuracy over the Caviar dataset and 32% in the UCSP dataset.Compared with other techniques, our results are greater due they only reach 1% of accuracy.
Old or Heavy? Decaying Gracefully with Age/Weight Shapes

Modern saturation theorem provers are based on the given-clause algorithm, which iteratively selects new clauses to process. This clause selection has a large impact on the performance of proof search and has been the subject of much folklore. The standard approach is to alternate between selecting the oldest clause and the lightest clause with a fixed, but configurable age/weight ratio (AWR). An optimal fixed value of this ratio is shown to produce proofs significantly more quickly on a given problem, and further that varying AWR during proof search can improve upon a fixed ratio. Several new modes for the Vampire prover which vary AWR according to a “shape” during proof search are developed based on these observations. The modes solve a number of new problems in the TPTP benchmark set.