Reviewerly/exHarmony huggingface dataset:
An improved Evaporation Rate-Water Cycle Algorithm based Genetic Algorithm for solving generalized ratio problems

This paper presents an efficient metaheuristic approach for optimizing the generalized ratio problems such as the sum and multiplicative of linear or nonlinear ratio objective function with affine constraints. This paper focuses on the significance of hybrid techniques, which are implemented by using GA and ER-WCA to increase efficiency and robustness for solving linear and nonlinear generalized ratio problems. Initially, GA starts with an initial random population and it is processed by genetic operators. ER-WCA will observe and preserve the GAs fittest chromosome in each cycle and every generation. This Genetic ER-WCA algorithm is provided with better optimal solutions while solving constrained ratio optimization problems. Also, the effectiveness of the proposed genetic ER-WCA algorithm is analyzed while solving the large scale ratio problems. The results and performance of the proposed algorithm ensures a strong optimization and improves the exploitative process when compared to the other existing metaheuristic techniques. Numerical problems and applications are used to test the performance of the convergence and the accuracy of the approached method. The behavior of this Genetic ER-WCA algorithm is compared with those of evolutionary algorithms namely Neural Network Algorithm, Grey Wolf Optimization, Evaporation Rate - Water Cycle Algorithm, Water Cycle Algorithm, Firefly algorithm, Cuckoo search algorithm. The evaluated results show that the proposed algorithm increases the convergence and accuracy more than other existing algorithms.
Governing Blocks: Building Interagency Consensus to Coordinate Humanitarian Aid

The conflict in Syria has led to one of the biggest refugee crises in history. An estimated 660,000 Syrian refugees have moved to neighbouring Jordan, many of whom are highly vulnerable to monetary poverty and food shortages. To reduce the daily inequalities faced by Syrian refugees, humanitarian agencies are progressively shifting to programmes that encourage financial inclusion and self-reliance. Operating since 2016, Building Blocks, a cash-based assistance programme created by the United Nations World Food Programme (WFP), uses blockchain technology rather than traditional financial service providers to supply monetary assistance for food purchases by Syrian refugees in Jordan. Beneficiaries have their identities confirmed though iris scanning when purchasing goods at supermarkets within the camps. Following authentication, monetary assistance is provided to complete the transaction. This system benefits over 100,000 Syrian refugees registered on the UNHCR’s PRIMES database and WFP has plans to scale up the programme to include more beneficiaries. This technology assessment focuses on understanding the governance of blockchain technology in Building Blocks (if any), and on finding opportunities for WFP operating partner agencies consisting of UNHCR, UNICEF, UN Women, Oxfam and Mercy Corps, to coordinate with and join the programme. Scaling up Building Blocks will benefit refugees and displaced people by giving these individuals a semblance of normalcy in a situation of vulnerability and crises. We advise WFP to collaborate with its partners to form a blockchain humanitarian consortia governing Building Blocks to avoid duplicating efforts and to achieve their shared objectives of delivering humanitarian aid in a sustainable manner. Other recommendations include to have UNOCHA and UNHCR as data aggregator and coordinator, respectively, to grant joint access to PRIMES for non-UN operating partners, to coordinate efforts with UNHCR’s Common Cash Facility programme, to consider renewable energy sources and to build local technical capacity for women in refugee camps. The involvement of operating partners in governing technology used in such processes will ensure equity of aid delivery, resulting in a broader governance, thus reducing inequality.
Designing Convolutional Neural Network Architectures Using Cartesian Genetic Programming

Convolutional neural networks (CNNs), among the deep learning models, are making remarkable progress in a variety of computer vision tasks, such as image recognition, restoration, and generation. The network architecture in CNNs should be manually designed in advance. Researchers and practitioners have developed various neural network structures to improve performance. Despite the fact that the network architecture considerably affects the performance, the selection and design of architectures are tedious and require trial-and-error because the best architecture depends on the target task and amount of data. Evolutionary algorithms have been successfully applied to automate the design process of CNN architectures. This chapter aims to explain how evolutionary algorithms can support the automatic design of CNN architectures. We introduce a method based on Cartesian genetic programming (CGP) for the design of CNN architectures. CGP is a form of genetic programming and searches the network-structured program. We represent the CNN architecture via a combination of pre-defined modules and search for the high-performing architecture based on CGP. The method attempts to find better architectures by repeating the architecture generation, training, and evaluation. The effectiveness of the CGP-based CNN architecture search is demonstrated through two types of computer vision tasks: image classification and image restoration. The experimental result for image classification shows that the method can find a well-performing CNN architecture. For the experiment on image restoration tasks, we show that the method can find a simple yet high-performing architecture of a convolutional autoencoder that is a type of CNN.
Modelling an Automotive Software-Intensive System with Adaptive Features Using ASMETA

In the context of automotive domain, modern control systems are software-intensive and have adaptive features to provide safety and comfort. These software-based features demand software engineering approaches and formal methods that are able to guarantee correct operation, since malfunctions may cause harm/damage. Adaptive Exterior Light and the Speed Control Systems are examples of software-intensive systems that equip modern cars. We have used the Abstract State Machines to model the behaviour of both control systems. Each model has been developed through model refinement, following the incremental way in which functional requirements are given. We used the ASMETA tool-set to support the simulation of the abstract models, their validation against the informal requirements, and the verification of behavioural properties. In this paper, we discuss our modelling, validation and verification strategies, and the results (in terms of features addressed and not) of our activities. In particular, we provide insights on how we addressed the adaptive features (the adaptive high beam headlights and the adaptive cruise control) by explicitly modelling their software control loops according to the MAPE-K (Monitor-Analyse-Plan-Execute over a shared Knowledge) reference control model for self-adaptive systems.
Design and Development of Single-Qubit Ion Trap on Glass and Si Substrates With RF Analysis and Performance Benchmarking

This article presents the design and development of surface electrode ion traps on glass and Si substrates and their radio frequency (RF) characterizations and performance benchmarking. In this case, the ion trap on glass shows superior performances in all necessary criteria. In terms of RF characterizations, ion traps on glass have a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> factor of greater than 900. This is significantly higher than the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> factor of its silicon counterparts, which are around 20–300. Such a high <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> factor results in power spectral density (PSD) of greater than 10 W/MHz. On the other hand, ion traps on silicon produce PSD values of lower than 3 W/MHz. In terms of RF performance, the ion trap on glass shows insertion loss lower than 0.2 dB at 60 MHz. This is more superior to insertion loss values of ion traps on silicon, which are around 1–4 dB. The ion-traps metallization is developed using three metallization layers (0.1- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> Ti barrier layer, 2.5–3.7- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> Cu, and 0.3- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> Au) on top of the dielectric. The on-chip resonance condition can be maintained upon packaging integration. The laser optical setup for ion trapping is verified to capture a single <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">88</sup> Sr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ion.
Semantic Web-Based Information Retrieval Models: A Systematic Survey

Effective representation of semantics in information sources has been central to Semantic Web (SW), since its inception. An information retrieval (IR) system must exploit the semantic knowledge embodied in web resources. Several attempts were made by researchers to make retrieval systems capable of utilizing web semantics. As a result, IR systems exploiting Semantic Web technologies were proposed in literature. In this paper, we have presented various intelligent models for information retrieval on SW. Our work mainly focuses on systems based on multi-agent, ontology, soft computing and concept-based paradigm employed for information retrieval on SW. Some existing surveys tried to comprehend various intelligent information retrieval models on SW, but their scope is limited. In this paper, we are providing a systematic and comprehensive elucidation of various intelligent information retrieval models with their basic approaches, key features and limitations in the context of SW. We have also provided a comparison of reviewed IR models for critical analysis.
A systematic literature review of the factors affecting smartphone user threat avoidance behaviour

Purpose There is a need for behavioural research within the smartphone context to better understand users’ behaviour, as it is one of the reasons for the proliferation of mobile threats. This study aims to identify the human factors that affect smartphone users’ threat avoidance behaviour. Design/methodology/approach A structured literature review (SLR) was applied to answer the research question. A total of 27 sources were analysed, from which 16 codes emerged. After synthesis, six themes transpired. Findings Six factors were identified as drivers and/or challenges of smartphone users’ threat avoidance behaviour, namely, knowledge and awareness, misconceptions and trust, cost and benefit considerations, carelessness, perceived measure effectiveness and the user’s perceived skills and efficacy. Research limitations/implications The results can encourage and provide a starting point for further research on human behaviour to improve smartphone user behaviour. Practical implications The mobile industry should focus on eradicating common misconceptions and undue trust in mobile security that is prevalent among smartphone users and make cost effective and usable interventions available. Training and awareness programs should be updated to include the factors that were identified in this study to affect smartphone users’ threat avoidance behaviour. In addition to improving users’ declarative knowledge concerning available smartphone measures and tools, procedural knowledge should also be improved to ensure proper use of available protective measures. Users should realise the importance of staying updated with evolving smartphone technology and associated threats. Originality/value This study acknowledges and supports the notion that addressing human behaviour is crucial in the fight against mobile threats. It addresses the need for behavioural research to analyse the factors that drive smartphone user behaviour. Furthermore, it uses and documents the use of a SLR, a research technique often unfamiliar among information security researchers.
A demonstration of quantum key distribution with entangled photons for the undergraduate laboratory

Now that fundamental quantum principles of indeterminacy and measurement have become the basis of new technologies that provide secrecy between two communicating parties, there is a need to provide teaching laboratories that illustrate how these technologies work. In this article, we describe a laboratory exercise in which students perform quantum key distribution with single photons, and see how the secrecy of the communication is ensured by the principles of quantum superposition and state projection. We used a table-top apparatus, similar to those used in correlated-photon undergraduate laboratories, to implement the Bennett-Brassard-84 protocol with polarization-entangled photons. Our experiment shows how the communication between two parties is disrupted by an eavesdropper. We use a simple quartz plate to mimic how an eavesdropper intercepts, measures, and resends the photons used in the communication, and we analyze the state of the light to show how the eavesdropper changes it.
How Many Weights Can a Quasi-Cyclic Code Have?

We investigate the largest number of nonzero weights of quasi-cyclic codes. In particular, we focus on the function Γ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</sub> (n, ℓ, k, q), that is defined to be the largest number of nonzero weights a quasi-cyclic code of index gcd(ℓ, n), length n and dimension k over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> can have, and connect it to similar functions related to linear and cyclic codes. We provide several upper and lower bounds on this function, using different techniques and studying its asymptotic behavior. Moreover, we determine the smallest index for which a q-ary Reed-Muller code is quasi-cyclic, a result of independent interest.
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning

Learning novel concepts while preserving prior knowledge is a long-standing challenge in machine learning. The challenge gets greater when a novel task is given with only a few labeled examples, a problem known as incremental few-shot learning. We propose XtarNet, which learns to extract task-adaptive representation (TAR) for facilitating incremental few-shot learning. The method utilizes a backbone network pretrained on a set of base categories while also employing additional modules that are meta-trained across episodes. Given a new task, the novel feature extracted from the meta-trained modules is mixed with the base feature obtained from the pretrained model. The process of combining two different features provides TAR and is also controlled by meta-trained modules. The TAR contains effective information for classifying both novel and base categories. The base and novel classifiers quickly adapt to a given task by utilizing the TAR. Experiments on standard image datasets indicate that XtarNet achieves state-of-the-art incremental few-shot learning performance. The concept of TAR can also be used in conjunction with existing incremental few-shot learning methods; extensive simulation results in fact show that applying TAR enhances the known methods significantly.
Improving Energy Efficiency in Internet of Things using Artificial Bee Colony Algorithm

Background: With the advent of IoT, the deployment of batteries with a limited lifetime in remote areas is a major concern. In certain conditions, the network lifetime gets restricted due to limited battery constraints. Subsequently, the collaborative approaches for key facilities to reduce the constraint demands of the current security protocols. Objective: This work covers and combines a wide range of concepts linked by IoT based on security and energy efficiency. Specifically, this study examines the WSN energy efficiency problem among IoT devices and security for the management of threats in IoT through collaborative approaches and finally outlines the future. The concept of energy-efficient key protocols which clearly cover heterogeneous IoT communications among peers with different resources has been developed. Because of the low capacity of sensor nodes, the energy efficiency in WSNs has been an important concern. Methods: In this paper, we present an algorithm for Artificial Bee Colony (ABC) which reviews security and energy consumption to discuss their constraints in the IoT scenarios. Results: The results of a detailed experimental assessment are analyzed in terms of communication cost, energy consumption and security, which prove the relevance of a proposed ABC approach and a key establishment. Conclusion: The validation of DTLS-ABC consists of designing an inter-node cooperation trust model for the creation of a trusted community of elements that are mutually supportive. Initial attempts to design the key methods for management are appropriate individual IoT devices. This gives the system designers, an option that considers the question of scalability.
Generalized Subspace Learning by Roweis Discriminant Analysis

We present a new method which generalizes subspace learning based on eigenvalue and generalized eigenvalue problems. This method, Roweis Discriminant Analysis (RDA), is named after Sam Roweis to whom the field of subspace learning owes significantly. RDA is a family of infinite number of algorithms where Principal Component Analysis (PCA), Supervised PCA (SPCA), and Fisher Discriminant Analysis (FDA) are special cases. One of the extreme special cases, which we name Double Supervised Discriminant Analysis (DSDA), uses the labels twice; it is novel and has not appeared elsewhere. We propose a dual for RDA for some special cases. We also propose kernel RDA, generalizing kernel PCA, kernel SPCA, and kernel FDA, using both dual RDA and representation theory. Our theoretical analysis explains previously known facts such as why SPCA can use regression but FDA cannot, why PCA and SPCA have duals but FDA does not, why kernel PCA and kernel SPCA use kernel trick but kernel FDA does not, and why PCA is the best linear method for reconstruction. Roweisfaces and kernel Roweisfaces are also proposed generalizing eigenfaces, Fisherfaces, supervised eigenfaces, and their kernel variants. We also report experiments showing the effectiveness of RDA and kernel RDA on some benchmark datasets.
Gathering garbage or going green?

Digital technologies are increasingly used with individuals with special needs for skill-building, social inclusion, and empowerment. However, different stakeholders involved in raising an individual with special needs have different and sometimes conflicting understanding of empowerment and motivations towards it. Using an empowerment framework, we collaboratively analyzed and critically reflected on the design and outcomes of three user studies conducted at a special needs school in New Delhi, India. The studies focused on learning how to make compost out of everyday kitchen waste, to assemble a solar lantern, and to buy groceries from a local store. Findings from the analysis provide insights into the complex socio-cultural conditions that enable, and in some cases limit, empowerment of individuals in an underserved and special needs context. We contribute to discussions on empowerment of children through design and use of digital technologies.
Clustered nuclei splitting based on recurrent distance transform in digital pathology images

Abstract The accuracy of the applied technique for automated nuclei segmentation is critical in obtaining high-quality and efficient diagnostic results. Unfortunately, multiple objects in histopathological images are connected (clustered) and frequently counted as one. In this study, we present a new method for cluster splitting based on distance transform binarized with the recurrently increased threshold value and modified watershed algorithm. The proposed method treats clusters separately, splitting them into smaller sub-clusters and conclusively into separate objects, based solely on the shape feature, making it independent of the pixel intensity. The efficiency of these algorithms is validated based on the labeled set of images from two datasets: BBBC004v1 and breast cancer tissue microarrays. Results of initial nuclei detection were significantly improved by applying the proposed algorithms. Our approach outperformed the state-of-the-art techniques based on recall, precision, F1-score, and Jaccard index. The proposed method achieves very low amount of under-segmented, as well as over-segmented objects. In summary, we provide novel and efficient method for dividing the clustered nuclei in digital images of histopathological slides.
Professional soft competencies of future software engineers: key concepts

The conceptual apparatus was analyzed in the article and the interpretation of the basic concepts of different authors was considered. The analysis of scientific sources allowed to generalize the concept of “professional soft competencies” as follows: it is a set of non-specialized competencies that in one way or another relate to problem solving, interaction between people and are responsible for successful participation in the work process, high productivity and , in contrast to specialized competencies, not related to a specific area, equipment control and technical skills. They cover the following aspects of behavior: situational awareness, decision making, error management, interpersonal communication, and so on. In general, these competencies are important for software developers, as software development projects are organized on the basis of teams or groups, where software engineers make up the majority of the team.
Computational power of matchgates with supplementary resources

The authors study the classical simulability and computational power of so-called matchgate circuits in the presence of additional resources, and find striking parallels to the known results for Clifford circuits. They also clarify under which conditions universal quantum computation is achieved with this gate set.
Error-Tolerance and Error Management in Lightweight Description Logics

Abstract The construction and maintenance of ontologies is an error-prone task. As such, it is not uncommon to detect unwanted or erroneous consequences in large-scale ontologies which are already deployed in production. While waiting for a corrected version, these ontologies should still be available for use in a “safe” manner, which avoids the known errors. At the same time, the knowledge engineer in charge of producing the new version requires support to explore only the potentially problematic axioms, and reduce the number of exploration steps. In this paper, we explore the problem of deriving meaningful consequences from ontologies which contain known errors. Our work extends the ideas from inconsistency-tolerant reasoning to allow for arbitrary entailments as errors, and allows for any part of the ontology (be it the terminological elements or the facts) to be the causes of the error. Our study shows that, with a few exceptions, tasks related to this kind of reasoning are intractable in general, even for very inexpressive description logics.