Reviewerly/exHarmony huggingface dataset:
Exploring The Whale Optimization Algorithm To Enhance Software Project Effort Estimation

The Software effort estimation is one of the most significant effects in software engineering community with considerable critical role. Moreover, it has an important impact on project management and decision making. This is due to the fact that the estimate correct enhances the project success within the planned resources because inaccurate prediction causes problems and failure of the project. Constructive Cost Model COCOMO II is the mostly used paradigm of software prediction and used in various environments and it covers a different set of factors when finding optimal coefficients in COCOMO II which will get optimal paradigm to predicate effort. Several models have been suggested to find the optimal coefficients but they have not come close to the required effort. In this paper, the humpback whale algorithm is applied on COCOMO II model to contribute solving the problem of ideal transactions (finding coefficients values), the features of this algorithm are easy concept by simulating the hunting manner of humpback whales to modify the position of populations for discovering the search space the Optimum, the whale optimization algorithm excellence on Particle Swarm Optimization (PSO) and another optimization techniques because they have the advantages of which less amenable modifying parameters, simple execution, and fast convergence. This algorithm has been applied to NASA 93 dataset and the results have been discussed with other experimental results and shows the proposed algorithm better based on Mean Magnitude of Relative Error(MMRE) metric is the most far used assessment for prediction estimate the efficiency of competing software estimate models.
Impact of Cuckoo Algorithm in Speech Processing

Speech processing has a lot of aspects to look after such as speech recognition, emotion recognition, language identification, etc. Processing a speech signal for various prospects is a very challenging task. The main goal of speech processing is to increase the accuracy and reduce the error percentage of error rate, as well as the cost of the whole process. To increase the accuracy level, it is a must that we use a better optimization algorithm. One of the best algorithms is the Cuckoo Search algorithm. This algorithm has been vastly used in image recognition as well as in other research areas but has little exposure in speech processing. This chapter contains the review and analysis of several works related to various speech processing areas where the Cuckoo Search algorithm has been used to optimize the performance of the proposed models. The advantages and limitations of the Cuckoo Search algorithm with other variants are also reviewed for future work.
Formalization of Forcing in Isabelle/ZF

We formalize the theory of forcing in the set theory framework of Isabelle/ZF. Under the assumption of the existence of a countable transitive model of $$ ZFC $$ , we construct a proper generic extension and show that the latter also satisfies $$ ZFC $$ . In doing so, we remodularized Paulson’s ZF-Constructibility library.
Toward Self-stabilizing Blockchain, Reconstructing Totally Erased Blockchain (Preliminary Version)

Blockchains, that are essentially distributed public ledgers, are extremely popular nowadays and are being used for many applications. One of the more common uses is for crypto-currencies, where they serve as a structure to store all the transactions publicly, securely, and hopefully irreversibly. Blockchains can be permissionless, where everyone can join and potentially contribute the blockchain, and permissioned, where only a few members (usually, much less than a permissionless blockchain) can push new transactions to the chain. While both approaches have their advantages and disadvantages, we will focus on a weakness of permissioned blockchains. The known boundary on the number of faulty participants − up to f for $$3f+1$$ participants − may be surpassed, causing the BFT algorithm to fail. A situation where a malicious adversary compromises/corrupts enough nodes to harm the blockchain may lead to the complete corruption of the ledger and even to the destruction of ledger copies the nodes hold. We will suggest a solution for the reconstruction of the blockchain in the event of such an attack. Our solution will include a mandatory publication of additional information by the private users when submitting transactions and will require them to store their transaction history. We will present a technique, using verifiable secret sharing (VSS), that will make our solution trust-less, immediate and per-user independent. Our technique will prevent the private user from lying, by making such an act enable the possible exposure of the user's secret key.
Application of the New FAAO Metaheuristics in Modeling and Simulation of the Search for the Optimum of a Function with Many Extremes

The article presents a new multi-criteria optimization method called Fuzzy Artificial Acari Optimization (FAAO). The AAO method was tested using ten commonly known benchmarks functionssuch as; Sphere - with a uniform surface, having only one minimum, Ackley - with a relatively uniform surface, having several dozen local minima and one global maximum with a much smaller value than most local minima, Eggholder -with an uneven surface, several dozen local minima, values similar to its global minimum global and Easom - with a flat surface in the vast majority of the domain, with global minimum of small area relative to the search space. The results were compared with other representatives of the Swarm Intelligence trend, such as ABC, PSO. It is very important to observe that FAAO is almost always the fastest, which can be treated as a good prognosis in the future applications of this metaheuristic.
Deep Learning Architectures for Medical Diagnosis

Following an introduction to the artificial intelligence and decision support systems in the Chap. 1, it is possible to focus on deep learning and exact architectures of deep learning used for medical diagnosis.
Protecting Smartphone Screen Notification Privacy by Verifying the Gripping Hand

As the most common personal devices, smartphones contain the user's private information. While people use mobile devices anytime and anywhere, the sensitive contents might be leaked from the screens. The smartphone notifications cause such privacy leakages even on a lock screen. With the aim to alert the user of an event (e.g., text messages, phone calls and calendar reminders), these onscreen notifications usually contain the sender's name and even a clip of the contents for preview. Such information, if not displayed appropriately, may cause the leakages of the user's social relations, personal hobbies and private message contents. This work focuses on wisely displaying the notifications to avoid leaking the user's privacy. We develop an unobtrusive user authentication system to confirm the user identity via their gripping-hands before displaying notifications. In particular, we carefully design an inaudible acoustic signal and emit it from the smartphone speaker to sense the gripping hand, when there is a need to push notifications. The signal propagating to the smartphone's microphones carries the user's biometric information related to the gripping hand (e.g., palm size and gripping strength). We further derive the Mel Frequency Cepstral Coefficient time series and develop a machine learning-based algorithm to identify the user. The experimental results show that our system can identify 8 users with 92% accuracy.
Computing Skeletons for Rectilinearly Convex Obstacles in the Rectilinear Plane

We introduce the concept of an obstacle skeleton, which is a set of line segments inside a polygonal obstacle $$\omega $$ that can be used in place of $$\omega $$ when performing intersection tests for obstacle-avoiding network problems in the plane. A skeleton can have significantly fewer line segments compared to the number of line segments in the boundary of the original obstacle, and therefore performing intersection tests on a skeleton (rather than the original obstacle) can significantly reduce the CPU time required by algorithms for computing solutions to obstacle-avoidance problems. A minimum skeleton is a skeleton with the smallest possible number of line segments. We provide an exact $$O(n^2)$$ algorithm for computing minimum skeletons for rectilinear obstacles in the rectilinear plane that are rectilinearly convex.
With Great Complexity Comes Great Vulnerability: From Stand-Alone Fixes to Reconfigurable Security

The increasing complexity of modern computing devices has rendered security architectures vulnerable to recent side-channel and transient-execution attacks. We discuss the most relevant defenses as well as their drawbacks and how to overcome them for next-generation secure processor design.
Peculiarities of the Digital Generation in the Context of Education and Management

The article takes a closer look at the most stable and widespread ideas about the socio-cultural peculiarities of the digital generation (the so-called “Y” and “Z” generations), which currently represents a significant population category with high standards and unique features. In terms of effective education and management, significant attention is drawn to issues related to the lifestyle and value orientations of the digital generation, formed in the era of the information revolution and the rapid development of computer and communication technologies. The purpose of the study is to analyze the ideas about the socio-cultural features of the digital generation and to show how these features should be considered in modern education and personnel management. The research methods used are the following: secondary analysis of data obtained from Russian and foreign studies, as well as the primary information collection methods: a survey of youth representatives (a questionnaire) and an expert interview. As a result of the research, the views and values typical for the digital generation are outlined, and the tools for using the socio-cultural peculiarities of the digital generation are methodically reasoned both for creating new digital educational process and for choosing the methods for personnel management in the company.
Smart embedded system based on demosaicking for enhancement of surveillance systems

Demosaicking and denoising are essential elements in digital photography pipelines. The use of convolutional neural networks (CNN)-based image demosaicking and denoising methods has been very successful. However, still there is a room for improvement in the network performance in terms of efficiency and accuracy. The main challenge that remains to be addressed is to guarantee the visual quality of reconstructed images, particularly in the presence of noise. To address these challenges, this paper introduces a novel demosaicking and denoising conjunct strategy using deep adaptive residual learning. The proposed framework has three stages. Initially, zero padding is performed to increase processing speed and preserve the edges of the image. In the second phase, we perform demosaicking using interpolation in order to find missing values using information about neighboring pixels. Finally, the reconstructed image is created using the original image. To evaluate the feasibility of the proposed scheme, we used Pytorch and Google Colab with 400 images for training and 100 images for validation The outcomes show that the proposed scheme beats cutting edge joint demosaicking and denoising schemes regarding both structural similarity index metrics (SSIM) and peak signal-to-noise ratio (PSNR) and basic similitude record measurements (SSIM).
Attention-gated LSTM for Image Captioning

Attention mechanism has achieved remarkable success in image captioning under the neural encoder-decoder frameworks. However, these methods are limited to introduce attention to the language model, e.g., LSTM (long short-term memory), straightforwardly: the attention is embedded into LSTM outside the core hidden layer, and the current attention is irrelevant to the previous one. In this paper, through exploring the inner relationship of attention mechanism and the gates of LSTM, we propose a new attention-gated LSTM model (AGL) that introduces dynamic attention to the language model. In this method, the visual attention is incorporated into the output gate of LSTM and propagates along with the sequential cell state. Thus the attention in AGL obtains dynamic characteristics, which means the current focused visual region can give remote guidance to the later state. Quantitative and qualitative experiments conducted on the MS COCO dataset demonstrate the the advantage of our proposed method.
Optimization of structural similarity in mathematical imaging

It is now generally accepted that Euclidean-based metrics may not always adequately represent the subjective judgement of a human observer. As a result, many image processing methodologies have been recently extended to take advantage of alternative visual quality measures, the most prominent of which is the Structural Similarity Index Measure (SSIM). The superiority of the latter over Euclidean-based metrics have been demonstrated in several studies. However, being focused on specific applications, the findings of such studies often lack generality which, if otherwise acknowledged, could have provided a useful guidance for further development of SSIM-based image processing algorithms. Accordingly, instead of focusing on a particular image processing task, in this paper, we introduce a general framework that encompasses a wide range of imaging applications in which the SSIM can be employed as a fidelity measure. Subsequently, we show how the framework can be used to cast some standard as well as original imaging tasks into optimization problems, followed by a discussion of a number of novel numerical strategies for their solution.
Adaptive Semantic Analysis of Radar Data Using Fuzzy Transform

The development of a new adaptive system of radar data semantic analysis with their non-stationarity, which is based on both numerical and logical methods of multiscanning processing of signals and methods of artificial intelligence using fuzzy transformations of the universe of signals and signal images, is proposed. The possibility of its hardware and software implementation is considered. The results of computer modeling, theoretical and experimental researches with processing of real radar signals are presented. The elements of logical analysis and algebra of finite predicates (AFP) are selected as mathematical apparatus. As experimental studies show, AFP is an appropriate tool for logical-mathematical constructions, with which it’s possible to describe the radar operator actions. The basic concepts of Boolean algebra and graph theory are also used. The practical value of the work is: a method for formalizing the processes of perception and transformation of signals and signal images, algorithms and software are intended for information radar systems with natural-language intellectual interface; also for support the design of information structures. Mathematical and software results can be used in the systems of automatic processing of radar information, particularly, in the intelligent radar and radio-electronic systems and complexes for monitoring of mobile air and ground objects.
Marginal Distribution Multi-Target Bayes Filter With Assignment of Measurements

This work proposes a marginal distribution multi-target Bayes filter with assignment of measurements to track multiple targets in the presence of an unknown and variable number of targets, clutter, and missed detections. Mathematically, the association of the measurements with either a target or clutter may be established by maximizing the joint likelihood function of the measurement partition, which leads to a two-dimensional assignment problem. By the introduction of detecting label, a handling approach for missed detections is also developed and is applied to the proposed filter. This filter greatly reduces the number of hypothesized targets or Gaussian terms by selecting the predicted probability density of a target or one of its multiple updated probability densities as its state distribution at each time step. Experimental results indicate that the proposed filter requires a less computational load than the existing filters and performs better than the efficient implementations of the δ-generalized labeled multi-Bernoulli filter for multi-target tracking at low and moderate clutter densities.
Extended Interval Valued Intuitionistic Fuzzy Index Matrices

Index Matrices (IMs) are extensions of the ordinary matrices. They are also an object of extensions and modifications, e.g., intuitionistic fuzzy IM and extended intuitionistic fuzzy IM. On the other hand, the intuitionistic fuzzy sets are extended to interval valued intuitionistic fuzzy sets. In the present research, we introduce interval valued intuitionistic fuzzy IMs and describe some of the basic operations, relations and operators over them.
Identification of Students’ Need Deficiency Through a Dialogue System

In the domain of moral education, students’ need deficiency refers to the unsatisfied need that would result in problem behaviors. Timely and accurate identification of students’ need deficiency is crucial to moral education and the students themselves. Previous psychology research focusing on distinct factors only provides scattered guidelines to identify such need deficiencies and meanwhile few teachers and parents have the related expertise, which makes the identification task difficult to accomplish. To address these issues, we develop a task-oriented dialogue system to help teachers and parents identify students’ need deficiency through multi-turn dialogues. Specifically, relevant factors of need deficiency are summarized based on psychology theories, which provides a theoretical foundation for the newly proposed system. In addition, reinforcement learning methodology is adopted to learn dialogue policy to serve the designed dialogue system. Experimental results demonstrate that the developed dialogue system achieves its design objectives.
Dental x-ray Image Segmentation using Maker Based Watershed Technique in Neutrosophic Domain

Dental image segmentation is a crucial task in dental image processing and analysis. Dental x-ray image is complex due to its different structures. This segmentation is important for many types of oral disease detection and to solve biometric purpose also. Dental x-ray image are of four types. These are panoramic, bitewing, periapical and Occlusal X-rays. Our proposed method is validated on bitewing, panoramic and periapical dental x-ray images. In this research work, we have proposed a dental teeth segmentation technique using marker based water based technique. The watershed method is a well known tool image segmentation technique. However, it does not result good for x-ray image, because the segmented individual region are almost same perceptually. In order to properly segment such regions the concept of the Marker based Watershed technique is introduced. A marker driven Watershed method is applied to segment the teeth regions. The proposed method is validated on bench marked dataset and achieved 89.78% accuracy in 324.45 mili-seconds. The execution time is quite faster than the popular existing methods
Intelligent interaction design research based on block chain communication technology and fuzzy system

With the increasing amount of information on the Internet, data storage management tends to be distributed. In distributed storage environment, users pay more and more attention to the timeliness of user interaction experience and the reliability of information interaction. However, the efficiency of users is often limited by the efficiency of data communication between distributed sites. One of the important goals of distributed data management is to improve the efficiency of data transmission and ensure the reliability of data transmission. Block chain technology is one of the emerging technologies supporting the development of management information system; it provides a solution for the storage, verification, transmission and communication of the distributed data. This paper focuses on solving the problem of block chain data transmission, and studies it from three aspects: improving the efficiency of data communication, ensuring the reliability of transmission, and improving the fairness of service, and different block chain data communication performance optimization strategies are proposed under the constraints of node communication capability, node trust, weight, priority of service request and other influencing factors.
Somehow They Are Never Horny!

With this provocation, we argue that existing HCI discourses on ageing risk rendering older adults as asexual individuals through research writing. We begin by articulating how discursive subjectivities such as users, consumers, participants, etc. are constructed, maintained, and propagated as representations of other individuals/groups through HCI research. We build our argument with examples highlighting tropes that have led to marginalization of certain groups of individuals through research writing. Through our provocation, we argue for re-framing and exploring sexual well-being of older adults as a fundamental humans rights issue and social justice concern while designing technologies. We point to problematic assumptions, amplify concerns, and call for further HCI research on sexual needs, desires, and experiences of older adults.
Intent Recognition from Speech and Plan Recognition

In multi-agent systems, the ability to infer intentions allows artificial agents to act proactively and with partial information. In this paper we propose an algorithm to infer a speakers intentions with natural language analysis combined with plan recognition. We define a Natural Language Understanding component to classify semantic roles from sentences into partially instantiated actions, that are interpreted as the intention of the speaker. These actions are grounded to arbitrary, hand-defined task domains. Intent recognition with partial actions is statistically evaluated with several planning domains. We then define a Human-Robot Interaction setting where both utterance classification and plan recognition are tested using a Pepper robot. We further address the issue of missing parameters in declared intentions and robot commands by leveraging the Principle of Rational Action, which is embedded in the plan recognition phase.
Health Data Analytics with an Opportunistic Big Data Algorithm

In data-driven society, health data can lead to profound impacts on public safety policies, epidemic modeling, and advancement of health science and medicine. This paper presents an approach to automatically elucidating useful information from "Big" health data. In particular, we analyze manufactured cosmetic products containing chemicals that are known or suspected to cause cancer, birth defects, or developmental and reproductive harm. Our analysis is based on the Apriori algorithm, the heart of the popular Association Rule Mining to discover associations among sets of influencing factors. However, with rapid growth of huge amount of data, including ours, existing data analytics algorithms designed for in-memory data are not adequate. Most Big data analytics algorithms are implemented on MapReduce framework for execution in parallel and distributed environments. Unlike traditional implementation, our approach employs an opportunistic MapReduce-based Apriori algorithm to fully exploit parallelism. The paper describes the algorithm and presents our findings, from 113, 179 data instances, both in terms of the execution times and the discovered associations among product profiles. For a support threshold of 10% (5%,), 20 (53) association rules are obtained with an improved execution time over that of the traditional MapReduce-based algorithm by 14.6% (40.3%) on the average over three machines.