Reviewerly/exHarmony huggingface dataset:
Automatic Classification of Low-Resolution Chromosomal Images

Chromosome karyotyping is a two-staged process consisting of segmentation followed by pairing and ordering of 23 pairs of human chromosomes obtained from cell spread images during metaphase stage of cell division. It is carried out by cytogeneticists in clinical labs on the basis of length, centromere position, and banding pattern of chromosomes for the diagnosis of various health and genetic disorders. The entire process demands high domain expertise and considerable amount of manual effort. This motivates us to automate or partially automate karyotyping process which would benefit and aid doctors in the analysis of chromosome images. However, the non-availability of high resolution chromosome images required for classification purpose creates a hindrance in achieving high classification accuracy. To address this issue, we propose a Super-Xception network which takes the low-resolution chromosome images as input and classifies them to one of the 24 chromosome class labels after conversion into high resolution images. In this network, we integrate super-resolution deep models with standard classification networks e.g., Xception network in our case. The network is trained in an end-to-end manner in which the super-resolution layers help in conversion of low-resolution images to high-resolution images which are subsequently passed through deep classification layers for label assigning. We evaluate our proposed network's efficacy on a publicly available online Bioimage chromosome classification dataset of healthy chromosomes and benchmark it against the baseline models created using traditional deep convolutional neural network, ResNet-50 and Xception network.
Innovational approach to diversification of activities of a modern university on the basis of remote education

Purpose The working hypothesis of the paper is that modern universities are peculiar for low sustainability to the changes of external environment due to low effectiveness of applied approaches to the diversification of their activities. The purpose of this paper is to verify the offered hypothesis by the example of modern Russia and develop an innovational approach to the diversification of modern university’s activities on the basis of remote education, which has to expand the possibilities of modern universities in the sphere of diversification of their activities. Design/methodology/approach This research is based on the hypothetical and deductive principles with application of the method of trend analysis, which allows studying dynamics of the number of universities in modern Russia; method of classification, which allows distinguishing the main approaches to the diversification of modern university’s activities; method of comparative analysis, which allows comparing these approaches and determining their advantages and drawbacks; method of statistical analysis, which allows evaluating the effectiveness of these approaches; method of analysis of causal connections, which allows determining the logic of this innovational approach to the diversification of modern university’s activities and determining its advantages as compared to existing approaches; method of modeling the state and development of socio-economic systems. The information and evidential basis of the research are statistical materials of the Federal State Statistics Service and the Ministry of Education of the Russian Federation. The research covers the time interval of 2005/2006–2016/2017. Findings It is substantiated that remote education opens additional possibilities in the sphere of diversification of modern university’s activities. The developed and presented innovational approach to the diversification of modern university’s activities on the basis of remote education is peculiar for higher effectiveness – as compared to the existing approaches – due to such advantages of remote education as minimum expenditures of labor and financial resources, potential large volume of market and obtaining advantages from “scale effect.” Originality/value The unique peculiarity and originality of the offered approach consist in the fact that remote education, which is a direction of diversification of university’s activities, is not an alternative but addition to the main educational services that are provided in the traditional form. Due to this, university enters a new market segment and strengthens its positions in the current market segment, which doubles its effect by increasing the sustainability of market positions of university.
Automatic Brain Tumor Segmentation with Contour Aware Residual Network and Adversarial Training

Localizing brain tumor and identifying different subtypes of tissues play a crucial role in treatment assessment and management of gliomas. In this paper, we present a contour-aware 3D convolution neural network (CNN) with adversarial training for segmenting gliomas and an ensemble of models for overall survival prediction. For the segmentation task, contour loss and adversarial loss are added as an auxiliary information in addition to the pixel-wise classification loss to ensure the segmentation results mimic the contours of the ground truth annotation. We employed both random-forest-based and neural-network-based regression scores for predicting overall survival time. Hand-crafted imaging feature incorporated with the non-imaging feature is employed. The proposed method was evaluated on the BraTS 2018 dataset and achieved competitive results for both segmentation and survival prediction tasks. We demonstrate that raw segmentation results can be improved by incorporating extra constraints in contours and adversarial training.
Novel Multi-Keyword Search on Encrypted Data in the Cloud

Searching on encrypted data has become a very important technique in cloud computing.Such searches enable the data owner to search on the encrypted data stored on the cloud without leaking any information.To obtain a better search experience, researchers have proposed many schemes which mainly focus on conjunctive and disjunctive keyword searches.However, conjunction of all the keywords may result in very few results, whereas a disjunction will return too many results.With the current schemes, customizing the relevancy of the keywords to obtain the desired results is difficult.To solve these problems, we propose a novel scheme that supports the search with the user-specified number of keywords contained in the search result.This number n can be used to customize the keyword relevancy.As a result, the data owner could obtain the desired search results containing any n keywords of a keyword set.The proposed scheme also supports the traditional disjunctive and conjunctive keyword searches when n equals 1 or the size of the keyword set, respectively.The keyword could be positive or negative.We first formally define its security and then prove that the proposed scheme is secure against the adaptively chosen keyword attack in the standard model and can defend against the offline keyword guessing attack to some extent.Furthermore, we present a theoretical performance comparison with other schemes as well as the experimental performance evaluations on our implemented scheme.
Curvature-based sparse rule base generation for fuzzy rule interpolation

Fuzzy inference systems have been successfully applied to many real-world applications. Traditional fuzzy inference systems are only applicable to problems with dense rule bases covering the entire problem domains, whilst fuzzy rule interpolation (FRI) works with sparse rule bases that do not cover certain inputs. Thanks to its ability to work with a rule base with less number of rules, FRI approaches have been utilised as a means to reduce system complexity for complex fuzzy models. This is implemented by removing the rules that can be approximated by their neighbours. Most of the existing fuzzy rule base generation and simplification approaches only target dense rule bases for traditional fuzzy inference systems. This paper proposes a new sparse fuzzy rule base generation method to support FRI. In particular, this approach uses curvature values to identify important rules that cannot be accurately approximated by their neighbouring ones for initialising a compact rule base. The initialised rule base is then optimised using an optimisation algorithm by fine-tuning the membership functions of the involved fuzzy sets. Experiments with a simulation model and a real-world application demonstrate the working principle and the actual performance of the proposed system, with results comparable to the traditional methods using rule bases with more rules.
TOSCA-based Intent modelling: goal-modelling for infrastructure-as-code

DevOps entails a set of practices that speed up the time needed to rollout software product changes. One such practice is automating deployment and delivery with infrastructure-as-code, i.e., automated scripts that ideally carry out 1-click deployment. Providing effective infrastructure-as-code poses the tricky issue in determining the modelling and information representation paradigm (e.g., Imperative, Declarative, etc.) most compatible with specifying infrastructural code. The OASIS TOSCA standard (“Topology and Orchestration Specification for Cloud Applications”) is the de-facto and de-iure standard language for infrastructure-as-code, and adopts an innovative take called “intent modelling”. This paper articulates the foundations of this modelling approach incorporating the most related modelling paradigm, that is, goal-modelling. We elaborate on it with a real but simple industrial sample featuring the TOSCA language.
Hybrid Resource Provisioning for Cloud Workflows with Malleable and Rigid Tasks

In cloud computing, reserved and on-demand instances are generally provided by service providers. Hybridization of the two alternatives can considerably save costs when renting resources from the cloud. However, it is a big challenge to determine the appropriate amount of reserved and on-demand resources in terms of users’ requirements. In this paper, the workflow scheduling problem with both reserved and on-demand instances is considered. The objective is to minimize the total rental cost under deadline constrains. The considered problem is mathematically modeled. A multiple sequence-based earliest finish time method is proposed to construct schedules for the workflows. Four different rules are used to generate initial task allocation sequences. Types and quantities of resources are determined by a free time block-based schedule construction mechanism. New sequences are generated by a variable neighborhood search method. Experimental and statistical analyses and results demonstrate that the proposed algorithm algorithm generates considerable cost savings when compared to the algorithms with only on-demand or reserved instances.
Three-tangle of a general three-qubit state in the representation of Majorana stars

Majorana stars, the $2j$ spin coherent states that are orthogonal to a spin-$j$ state, offer a visualization of general quantum states and may disclose deep structures in quantum states and their evolutions. In particular, the genuine tripartite entanglement - the three-tangle of a symmetric three-qubit state, which can be mapped to a spin-3/2 state, is measured by the normalized product of the distance between the Majorana stars. However, the Majorana representation cannot applied to general non-symmetric $n$-qubit states. We show that after a series of SL$(2,\mathbb{C})$ transformations, non-symmetric three-qubit states can be transformed to symmetric three-qubit states, while at the same time the three-tangle is unchanged. Thus the genuine tripartite entanglement of general three-qubit states has the geometric representation of the associated Majorana stars. The symmetrization and hence the Majorana star representation of certain genuine high-order entanglement for more qubits are possible for some special states. In general cases, however, the constraints on the symmetrization may prevent the Majorana star representation of the genuine entanglement.
Dual Authentication-Based Encryption with a Delegation System to Protect Medical Data in Cloud Computing

The increasing use of cloud computing, especially in commercial, government and healthcare institutions, started with the use of computerized clouds. Clouds store important data, which reduces the cost of management and ensures easy access. To protect this data, cryptographic methods are used to ensure confidentiality of the data, as well as to secure access to user data and increase trust in cloud technology. In our paper, we suggest a new scheme to support an attribute-based encryption system (ABE) that involves multiple parties such as data owners, data users, cloud servers and authority. A verified and authenticated decryption process for the cloud environment is the imperative feature of our proposed architecture. The data owner encrypts their data and sends it to the cloud. The cloud server performs partial decryption and the final decrypted data are shared for users as per their privileges. Thus, the data owner reduces complexity of productivity by delegating the decryption process to the cloud server. Analysis of the experimental results confirms that data access in the electronic cloud atmosphere is safer due to a controlled multiple-users-rights scheme. Our performance evaluation results show that the proposed model condensed the communication overhead and made Digital Imaging and Communications in Medicine (DICOM) more secure.
3-Uniform states and orthogonal arrays of strength 3

A pure quantum state of [Formula: see text] subsystems with [Formula: see text] levels is called [Formula: see text]-uniform state if all its reductions to [Formula: see text] qudits are maximally mixed. We construct 3-uniform states for an arbitrary number of [Formula: see text] via orthogonal arrays and the method of adding minus signs mathematically.
Typicalities and probabilities of exceptions in nonmotonic Description Logics

We introduce a nonmonotonic procedure for preferential Description Logics in order to reason about typicality by taking probabilities of exceptions into account. We consider an extension, called ALC+TRP, of the logic of typicality ALC+TR by inclusions of the form T(C)⊑pD with probability p, whose intuitive meaning is that “all the typical Cs are Ds, and the probability that a C is not a D is 1−p”. We consider a notion of extension of an ABox containing only some typicality assertions, then we equip each extension with a probability. We then restrict entailment of a query F to those extensions whose probabilities belong to a given and fixed range. We propose a decision procedure for reasoning in ALC+TRP and we exploit it to show that entailment is ExpTime-complete as for the underlying ALC.
Exploratory Search for Scientific Articles

It is intuitively clear that search for scientific publications often has many characteristics of exploratory search. The purpose of this paper is to formalize this intuitive understanding, explore which scientific search tasks can be classified as research search ones, what general approaches to the research search problem exist, and how they are implemented in specialized search engines for scientists. We overview the existing works that address the information-seeking behavior of scientists and a special variant of search called exploratory search. There are several types of search behavior typical for scientists; we show that most of them are exploratory ones. Exploratory search differs from information retrieval and requires special support from the search systems. We analyze seventeen search systems for academicians (from Google Scholar, Scopus, and Web of Science to ResearchGate) from the perspective of exploratory search support. We find that most of them do not go far beyond simple information retrieval, and there is room for further improvements, especially in collaborative search support.
Material-Oriented Musical Interactions

This chapter explores different perspectives on the role of musical tools in musical interactions, with a particular focus on entanglements of agency. These perspectives can run the full gamut from musicians claiming to be “played by” their instruments and essentially at the mercy of the inner workings of the instruments, to musicians feeling as though the instrument is transparent, and that their inner impulses are communicated as sounds with no resistance from the instrument. Viewpoints are presented from contemporary musical practices and from instrument designers and makers, and are connected with wider theoretical accounts of agency in technology. These discussions are then brought back to the context of the design and development of digital musical instruments, and to human-computer interaction more broadly, reflecting on the relationships between designers and their technologies, and on how the design and development process can be viewed as nested inside its own chain of technological and social influences.
Co-Designing the Kits of IoT Devices for Inquiry-Based Learning in STEM

The rapidly developing technological landscape challenges require educational institutions to constantly renew the school's digital infrastructure in order to keep students engaged in learning difficult subjects such as Science, Technology, Engineering, and Mathematics (STEM). The Internet of Things (IoT) is one of such new technology platforms that could help the schools enhance learning processes with innovative resources, and to increase students' motivation to learn. This paper summarizes the first stage of a design-based research focusing on introducing IoT technologies to secondary education. Five kits of IoT devices were co-designed by researchers, teachers, and students, to optimize their match with the curricular objectives, cost, learning curve, and re-usability in various educational contexts. The study included three steps: (1) mapping out the IoT devices on the basis of the desk research, (2) literature review on STEM education practices, and (3) two focus group interviews with teachers and students from different schools. As a result of the study, five different kits of IoT devices were purchased for schools and pilot-tested in real-life settings.
Dynamic multiple-image encryption based on chirp z-transform

A dynamic encryption scheme is demonstrated based on chirp z-transform (CZT). The novel design of this setup allows us to protect multiple images in a multiplexed format and fully recover them without the influence of crosstalk and sample noise. The encoding stage is implemented using the 2D CZT and a Fourier lens. The CZT works with proper frequency parameters to control the position and size of the input frequency spectrum. The accurate positioning leads to an efficient packing of the multiplexed images and a remarkable improvement of the signal-to-noise ratio of the decrypted pictures. The introduction of the CZT in the field of encryption is significant, since it can expand the functionality to a dynamic regime of optical and digital architectures for static encoding. We validated the feasibility of our proposal with the encryption and decryption of a color image sequence and a set of binary images.
Intelligent navigation of multiple coordinated robots

Navigation of multiple robots is a challenging task, particularly for many robots, since individual gains may more often than not adversely affect global gain. This paper investigates the problem of multiple robots moving towards individual goals within a common workspace without colliding amongst themselves. Two solutions for coordination namely Fuzzy Logic Controller (FLC) and Genetic Algorithm based FLC (GA-FLC) have been employed and the efficacy of cooperation strategies have been compared with their non-cooperative counterparts as well as with the fundamental potential field method (PFM). Proposed coordination schemes are verified through simulations. A total of 100 scenarios are considered varying the number of robots (8, 12, 16 and 20). The obtained results show the efficacy of the proposed schemes.
Multiple Sequential Regularized Extreme Learning Machines for Single Image Super Resolution

In this letter, we propose an example-based super-resolution algorithm based on multiple neural networks trained sequentially using the method online sequential regularized extreme learning machine. In order to train multiple networks, we divide the training samples into clusters using local gradient information, and a distinct network is trained for each cluster. We add another layer of learning by training linear kernels to reshape the network output patches into full images. Also, we employ a fast reconstruction scheme before and after the learning stage. Experiments show that the proposed method generates comparable or better results when compared to important works of the literature, without the need for specialized hardware, large training datasets, and extensive training time.
Continuous convolutional object tracking in developmental robot scenarios

Tracking arbitrary objects in natural environments is a challenging task in visual computing. A central problem is the need to adapt to changing appearances under strong transformation and occlusion. We propose a tracking framework that utilises the strength of Convolutional Neural Networks to create a robust and adaptive model of the object from training data produced during tracking. An incremental update mechanism provides increased performance and reduces the computational costs for training during tracking, allowing for robust real-time tracking with state-of-the-art performance. Together with optimisations for deploying the framework on humanoid robots and distributed devices, this shows its viability for research in developmental robotics on questions around infant cognition or active exploration.
Model-Based Digital Image Correlation for Noncontact Deformation Measurement of Strain Field and Mechanical Property

This paper presents a new method that combines a physics-based model and finite-element (FE) based digital image correlation (DIC) with an interfacial continuity condition for simultaneous deformation measurements of strain field and material mechanical properties. The dual-domain global DIC method incorporates a model-based kernel that offers useful physical insights into the complex deformation and constrains the solutions to the iterative minimization of the DIC criterion, which leads to a faster convergence. Unlike local subset-based and global FE-based methods where the DIC is formulated to solve for the nodal displacements, the model-based DIC that accounts for complex deformation in the specific region is formulated to identify system parameters. Both domains, along with their interface and the continuity condition enforced on the global displacement field, are discretized and solved simultaneously in a single calculation. The dual-domain global DIC method has been investigated by comparing reconstructed strain fields and estimated parameters against targets simulated using the commercial FEA software COMSOL and published experimental data. Unlike FE-DIC which is noise sensitive, the model-based DIC is robust against the Gaussian noise effects.