Reviewerly/exHarmony huggingface dataset:
Automated Tracking of Red Blood Cells in Images

Computer simulations of processes inside microfluidic devices often require validation data from experiments with biological cells. Besides single cell experiments, data involving flow of many cells can be used to validate many-cell behaviour. Manual data gathering from such experiments, such as video sequence of cell flow, is inefficient and needs to be automated. Building on top of an automated detection of red blood cells, tracking of red blood cells is needed in order to provide physical data about each cell. In this work, we first describe our existing traditional algorithms for cell tracking. We assess the possible metrics for measuring their performance and iterate upon the flaws of our algorithm in order to design improvements and propose a neural network solution.
A method of mapping and nearest neighbor optimization for 2-D quantum circuits

In some practical quantum physical architectures, the qubits need to be distributed on 2-dimensional (2-D) grid structure to implement quantum computation. In order to map an 1-dimensional (1-D) quantum circuit into a 2-D grid structure and satisfy the nearest neighbor constraint of qubit interaction in the grid structure, a mapping method from 1-D quantum circuit to 2-D grid structure is proposed in this paper. This method firstly determines the order of placing qubits, and then presents the layout strategy of qubits in 2-D grid. We also proposed an algorithm for establishing interaction paths between non-adjacent qubits in 2-D grid structure, which can satisfy the physical constraints of the interaction of quantum bits in the grid in the process of mapping an 1-D quantum circuit to a 2-D grid structure. For some benchmark circuits, after using the method of this paper to place qubits, it is possible to make every 2-qubit gate in the circuit have a nearest neighbor, so that there is no need to use SWAP gate to establish channel routing. Compared with the latest available methods, the average optimization rate is 82.38%.
Classification of Rare Recipes Requires Linguistic Features as Special Ingredients

In this paper, we propose a joint model, composed of neural and linguistic sub-models, to address classification tasks in which the distribution of labels over samples is imbalanced. Different experiments are performed on tasks 1 and 2 of the DEFT 2013 shared task []. In one set of experiments, the joint model is used for both classification tasks, whereas the second set of experiments involves using the neural sub-model, independently. This allows us to measure the impact of using linguistic features in the joint model. The results for both tasks show that adding the linguistic sub-model improves classification performance on the rare classes. This improvement is more significant in the case of task 1, where state-of-the-art results are achieved in terms of micro and macro-average F1 scores.
Report on the third international workshop on narrative extraction from texts (Text2Story 2020)

The Third International Workshop on Narrative Extraction from Texts (Text2Story'20 [https://text2story20.inesctec.pt/]) was held on the 14th of April 2020, in conjunction with the 42 nd European Conference on Information Retrieval (ECIR 2020). This year due to the Covid-19 outbreak the Text2Story workshop was held online on Zoom platform. During the course of the day, an average of more than 60 attendees had the opportunity to follow-up and discuss the recent advances in extraction and formal representation of narratives. The workshop consisted of two invited keynotes and thirteen paper presentations. The proceedings of the workshop are available online at http://ceur-ws.org/Vol-2593/
An Optimal Data Representation to Store and Retrieve Data using Encoding

Smart and innovative trends towards digital India lead to data explosion, while many cloud and network applications require smart data management. Storing big data generated in volumes by smart devices and social networks is a challenge, though enhanced storage facilities and storage devices keep coming as each facility requires some overheads or other in maintaining, retrieving and transmitting data. Next generation computing involves vast data handling requirements for which effective storage representations is needed. In this paper an optimal data representation technique based on probability based encoding is proposed, which can be used for efficient storage and communication of large data. The technique has been tested on various data types and the effectiveness on storage and time are analyzed and presented.
Applying Faster R-CNN for Hematocytes Detection on Compound Microscope with Image Sensor Device and Multiple GPU Computation

In this paper, we demonstrate a machine learning algorithm with multiple GPU processing in hematocytes detection. Object detection in compound microscopy images presents a specific task. Microscopy image import directly to a specimen slide under the compound microscope by an image sensor device. We propose Faster R-CNN with a customizing model applying cutting-edge object detection systems. The MicrosisDCN (Microbes Diagnosis with Deep Convolutional Neural Network) deploys Faster R-CNN in python script configures to access multiple GPUs computation with 7168 CUDA cores of dual GPUs with Linux command options: worker_replicas and num_clones equal to several graphics processing units. The datasets consist of hematocytes extracted from raw slides under a microscope. These images separate the cells of interest into three groups: red blood cell (RBCs), white blood cell (WBCs), and platelets. The training dataset consists of 80 percent of the 40,000 images. And the testing dataset consists of 20 percent of the 40,000 images. Our algorithm also provides the result of mean average precision (mAP) and enables multiple GPU training models in Tensorflow and OpenCV. The mAP is the average of average precision (AP) with intersection-over-union (IoU) in measuring the score of object detection accuracy. If the mAP score approaches 1.0, it indicates well accuracy. Our 9,000 steps valuation algorithm model by the python script estimates the mAP in 3 groups: RBCs, WBCs, and platelets as about 0.9147, 0.9664, and 0.9548. The optimum would be at nearly 12,000 steps because the algorithm estimates the mAP model as 0.96 in all types. The experiment aims to verify the neural network model using a compound microscope.
Algorithm analysis of multi keyword rank searching using tree method upon encrypted cloud data

In these days enterprises are using cloud space to keep their data. This practice saves the cost of hardware purchasing and maintenance as well as infrastructure cost. Although privacy and security of the data are the important issues because the cloud server is either un-trusted or semi trusted. Various methods and schemes have been used to achieve the objective of data security and privacy. To keep data safe and secure, data is encrypted before uploading at cloud space. Therefore to search particular information at cloud space the plaintext keyword searching method is obsolete. The new method for searching the data upon cloud is searching upon encrypted data. The authors proposed here a multi keyword rank search scheme, which returns top most relevant documents as an output of search. The tf x idf model as well as vector space model is used to form the table that keeps the index and queries. For encryption of index and query kNN algorithm can be used. The authors have formed a tree known as M way balanced search (MBS) for search and indexing in a better way. The authors also proposed a dfsm (depth first searching method) scheme for multi key synonym rank search. The certainty and efficiency of dfsm is scalable but MBS tree is taking sub linear time complexity. For calculating the complexity computational methods were used.
A Study and Implementation of Virtual Reality and its Capabilities

Virtual Reality is a technology, which helps the user to interact with an environment of the simulation of the real world or an imaginary world. This paper details an application using the Unity game engine to showcase and demonstrate the capabilities of VR. It includes the capabilities such as: Movement of the player, to demonstrate the ability to move your player model around the play area using the controllers; Head tracking in game, to demonstrate the VR head tracking technology by moving the camera in the game to match your head position in real life; and using in game hands to mimic the positioning of your real hands in relation to your body, to interact with objects within the virtual space.
Modelling of Content Adaptation in 5G Networks: A Survey

Video streaming is attaining popularity across mobile users. The most popular video websites such as YouTube will be the major providers for mobile videos. In the recent years, with the ongoing exponential increase in the data, video traffic has become a major problem and is undergoing serious bottleneck issues. In this paper, surveys on various video streaming strategies are identified and its application to content adaptation is observed. Final results are analysed to adapt a better system for implementing the content adaptation problem.
Dynamic Path Planning Model for Intelligent Air Vehicle

With the gradual popularization of the application of intelligent air vehicle in various fields, the problems related to its application are becoming a hot research topic increasingly. Rapid path planning in complex environment is an important topic of intelligent air vehicle control. Because of the limitation of the system structure, the positioning system of this kind of air vehicle cannot locate itself accurately. Once the positioning error accumulates to a certain extent, it may lead to the failure of the mission. Therefore, it is an important task to correct the positioning error in the flight path planning of intelligent air vehicle. This paper makes a deep study on the problem of flight path planning. By simplifying the three-dimensional space problem into a two-dimensional plane geometry problem, the optimization mathematical model is established by using the dynamic decision optimization method, and the problem of how to plan the optimal flight path by using the known space correction points is analyzed in detail, which provides some enlightenment for optimizing the flight path, improving the flight success rate and improving the performance of the air vehicle.
Structure and Metrics of Emerging Computing

A metric and structure of computing 2020 is proposed in the form of Top 12 Technology Trends, which will influence on investment in science, education and industry in developing countries. The primary social and technological problem of the protection of society and critical facilities through the creation of Global Intelligent Cyber Security is formulated. The axioms of the constructive formation of developing countries on the basis of the adoption of moral relations are formulated. Models, methods and algorithms of cyber-social computing are proposed that are focused on processing big data, searching for keywords and test fragments. New characteristic equations of similarity - differences between the processes and phenomena are synthesized for the exact information retrieval by keywords in cyber-physical space. A computing model of the development of the Universe is formulated, where the binary interactions of entities and forms are harmonic functions of the phase state. A structure of interactive computing of the creative process based on a metric assessment of the development status with world achievements is proposed.
Maximum Distance Separable Codes

Two codewords of a block code of length n and minimum distance d must differ on any set of n − d + 1 coordinates, since they are at distance at least d from each other. This observation leads to the Singleton bound, Theorem 6.1. A code whose parameters give an equality in the Singleton bound is called a maximum distance separable code or simply an MDS code. Therefore, an MDS code is a block code in which every possible (n − d + 1)-tuple of elements of the alphabet occurs in a unique codeword for any set of n − d + 1 coordinates. The focus in this chapter will be on linear MDS codes, since not so much is known about non-linear MDS codes, and there are no known non-linear MDS codes which outperform linear MDS codes.
Personalized Citation Recommendation Using an Ensemble Model of DSSM and Bibliographic Information

With the tremendous proliferation of scientific literature and research papers published every year, fulfilling a comprehensive literature overview became a tedious and time-consuming task. Citation recommendation is considerably important to improve the efficiency and quality of literature search. It scales down the information overload in academia by using the content of the paper and citation information to automatically recommend papers relevant to the students or respectively researcher’s preferences. In this paper, we propose a novel personalized citation recommendation system comprised of a query-based recommendation module and a graph-based ranking module. The query-based recommendation module relies on Deep Semantic Similarity Model (DSSM) to rank papers based on their semantic similarity to a query text. The graph-based ranking module uses a heterogeneous graph that incorporates the citation and content information within papers, to rank the candidate papers based on their relevance to a query text and the corresponding author. The fusion of the results from both modules provides the final recommendation list. Our intensive experiments on the ACL Analogy dataset (AAN) prove that our model significantly outperforms other state-of-the-art techniques in terms of MAP and MRR. Also it shows a better Recall in the top ranked papers over the best performing baseline.
Computing the multilinear factors of lacunary polynomials without heights

We present a deterministic algorithm which computes the multilinear factors of multivariate lacunary polynomials over number fields. Its complexity is polynomial in ℓn where ℓ is the lacunary size of the input polynomial and n its number of variables, that is in particular polynomial in the logarithm of its degree. We also provide a randomized algorithm for the same problem of complexity polynomial in ℓ and n. Over other fields of characteristic zero and finite fields of large characteristic, our algorithms compute the multilinear factors having at least three monomials of multivariate polynomials. Lower bounds are provided to explain the limitations of our algorithm. As a by-product, we also design polynomial-time deterministic polynomial identity tests for families of polynomials which were not known to admit any. Our results are based on so-called Gap Theorem which reduce high-degree factorization to repeated low-degree factorizations. While previous algorithms used Gap Theorems expressed in terms of the heights of the coefficients, our Gap Theorems only depend on the exponents of the polynomials. This makes our algorithms more elementary and general, and faster in most cases.
The Most Likely Object to be Seen Through a Window

We study data structures to answer window queries using stochastic input sequences. The first problem is the most likely maximal point in a query window: Let [Formula: see text] be constants, with [Formula: see text]. Let [Formula: see text] be a set of [Formula: see text] points in [Formula: see text], for some fixed [Formula: see text]. For [Formula: see text], each point in [Formula: see text] is associated with a probability [Formula: see text] of existence. A point [Formula: see text] in [Formula: see text] is on the maximal layer of [Formula: see text] if there is no other point [Formula: see text] in [Formula: see text] such that [Formula: see text]. Consider a random subset of [Formula: see text] obtained by including, for [Formula: see text], each point of [Formula: see text] independently with probability [Formula: see text]. For a query interval [Formula: see text], with [Formula: see text], we report the point in [Formula: see text] that has the highest probability to be on the maximal layer of [Formula: see text] in [Formula: see text] time using [Formula: see text] space. We solve a special problem as follows. A sequence [Formula: see text] of [Formula: see text] points in [Formula: see text] is given ([Formula: see text]), where each point [Formula: see text] has a probability [Formula: see text] of existence associated with it. Given a query interval [Formula: see text] and an integer [Formula: see text] with [Formula: see text], we report the probability of [Formula: see text] to be on the maximal layer of [Formula: see text] in [Formula: see text] time using [Formula: see text] space. The second problem we consider is the most likely common element problem. Let [Formula: see text] be the universe. Let [Formula: see text] be a sequence of random subsets of [Formula: see text] such that for [Formula: see text] and [Formula: see text], element [Formula: see text] is added to [Formula: see text] with probability [Formula: see text] (independently of other choices). Let [Formula: see text] be a fixed real number with [Formula: see text]. For query indices [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text], with [Formula: see text] and [Formula: see text], we decide whether there exists an element [Formula: see text] with [Formula: see text] such that [Formula: see text] in [Formula: see text] time using [Formula: see text] space and report these elements in [Formula: see text] time, where [Formula: see text] is the size of the output.
Description method of Illumination invariant image features

Image feature extraction technology is one of main topics in the field of computer vision, which has been widely applied in biological recognition, image retrieval, target detection and other fields. To overcome the drawbacks of WLD under complex illumination condition, we propose a novel illumination–insensitive feature descriptor named as anisotropic Weber synergy gradient descriptor (AWSGD). The proposed algorithm contains two parts:differential excitation component and gradient direction component. Firstly, by introducing the differential synergy excitation pattern (DSEP) and anisotropic LOG operator with variable scales and angles, we propose the anisotropic differential synergy excitation pattern (ADSEP) as the differential excitation component. Next, focused on the shortage that local gradient pattern (LGP) lacks detailed description of local features with single-layer model, we propose weighted local synergy gradient pattern (WLSGP) as the gradient direction component based on two-layer structure model and weight coefficient distribution model. Finally, ADSEP and WLSGP are fused to form AWSGD histogram. Meanwhile, we adopt XGBoost classifier to conduct related experiments on face databases CMUPIE, Yale B and texture databases PhoTex, RawFooT. The experimental results indicate that the proposed algorithm has stronger robustness to illumination variation and achieves the best performance compared with state-of-the-art methods, which has a certain theoretical significance and practical value in image recognition field under complex illumination condition.
Italian priest conducts online mass with dumbbells after accidentally turning on filter

An Italian priest mistakenly switched on Facebook Live filters when he began live-streaming mass amid the country's lockdown due to the novel coronavirus.
Operational Electricity Dispatch Based on Direct Normal Irradiance (DNI) and Load Forecasting: Case Study: STTP with TES system

The rise in the use of concentrated solar power (CSP) systems has drawn attention to the fluctuations that affect the grid due to the variability nature of solar resources especially Direct Normal Irradiance (DNI), the main component of these systems. These variations can cause serious damages to the grid causing sometimes black-outs. In terms of technical operations, the chaotic behavior of DNI makes the dispatch in many cases impossible to provide electricity for the right place at the right time when the need is there. This study proposes an operational method to dispatch electricity based on DNI and load forecasting using statistical and machine learning techniques combined with the dispatch tool provided in SAM (System Advisor Model) software. The case study explored is the Solar Tower with the molten salt combined with Thermal Energy Storage (TES) system used in NOOR 3 Ouarzazate, Morocco. The aim of the proposed method is to reduce the effect of fluctuations on the electrical grid by anticipating them and providing an easier and more accurate operational way of scheduling and making dispatch decisions. The results show a considerable increase in the performance of the simulated grid due to the new proposed dispatch method and the machine learning techniques used.
An Efficient One-Way Authenticated Key Exchange Protocol for Anonymity Networks

Authenticated key exchange (AKE) is a crucial cryptographic primitive used for establishing an authenticated and confidential communication channel between parties, which has been widely used in our daily life when we surf on the Internet. However, in past decades mutual AKE protocols have been studied extensively in theory, while in many practical environments such as in the Tor anonymity network the mutual authentication property is undesirable. Meanwhile, considering privacy protection is getting more and more attention and one-way AKE protocols play an important role in privacy protection, so no matter in theory or in practice, it is quite meaningful to conduct research on the one-way AKE protocols. In this article, we present a new efficient anonymous one-way AKE protocol, which has strong anonymity and security properties guarantee, and efficiency advantages. Moreover, our new scheme has tight security reduction and is formally proved secure in the random oracle model under the Gap-DH and XCR signature security assumptions.
Formula for Success

The article is devoted to the 90th anniversary of the Volgograd State Technical University (VSTU). The author views the components of the success formula of the University as a flagship University of the Volgograd region, and dwells on the historical facts of the creation of the Stalingrad tractor-building Institute at the beginning of the country’s industrialization and its development to a flagship University, which is a modern multi-campus University with a promising educational, scientific and industrial infrastructure. The article also presents the scientific schools of well-known scientists of the University that carry out fundamental and applied scientific research as well as the international activities of the University, including international cooperation in research, creation and modernization of educational programs, and export of educational services. In its multi-faceted activities, the flagship University focuses on solving urgent problems of socio-economic development of the region. The hybrid model of the flagship University is focused mainly on two typical models: «multi-disciplinary regional University» and «technology leader in the region». The article considers a number of systemic and structural transformations of the University in the framework of a hybrid model: an effective management system for supporting innovation, including socio-psychological attitudes and infrastructure changes; the development of additional education and the creation of conditions for business development.