Reviewerly/exHarmony huggingface dataset:
Mobile Technologies Impact on Economic Development in Sub-Saharan Africa

Mobile technology today is increasingly being used to help improve underdeveloped and developing areas such as Sub-Saharan Africa. With the statistics showing the number of adults in Africa owning mobile devices steadily increasing, mobile technology has been a popular area of interest to use to help improve areas such as healthcare and education throughout African cities and rural areas. The purpose of this chapter was to examine the different ways mobile technology was being used to help the residents of Sub-Saharan Africa in the sectors of healthcare and education, and examine the possible ethical effects these technologies could be having. The study concludes that while the mobile technology can be implemented to help better the standard of health and education, it is mainly focused on urban areas and contributing to a poverty imbalance between urban and rural Africa.
Consciousness about Mobile App Permissions

This paper gives us the attentiveness on opening files from unknown sources as sometimes it may cause damage to our mobile phones. In almost all the mobile apps after downloading it will ask some of the permissions to allow clicking for the allow button only we can able to access anything in that application otherwise we are unable to use all the features of that application. Many popular android apps including Facebook messenger, WhatsApp, Skype, Twitter, Share it, Instagram and other party apps get user permission after the installation. By allowing these permissions they can recording with the phone audio and video at any time, they can see contacts and modifying the USB storage contents(files). Lack of knowledge and awareness about permissions to the people may cause significant negative consequences. This research evaluates effectiveness of a demo app with visual ques to increase permissions awareness and avoid negative consequences.
Solving the Quadratic Assignment Problem using the Swallow Swarm Optimization Problem

In recent years, there is a growing interest in swarm intelligent algorithms inspired by the observation of the natural behavior of swarm to define a computational method, which may resolve the hardest combinatorial optimization problems. The Quadratic Assignment Problem is one of the well-known combinatorial problems, which simulate with the assignment problem in several domains such as the industrial domain. This paper proposes an adaptation of a recent algorithm called the swallow swarm optimization to solve the Quadratic Assignment Problem; this algorithm is characterized by a hierarchy of search who allow it to search in a totality of research space. The obtained results in solving some benchmark instances from QAPLIB are compared with those obtained from other know metaheuristics in other to evaluate the performance of the proposed adaptation.
An integrated blockchain network with energy router-based trading strategy for optimal energy management

The new emerging idea of energy router and energy blockchain network (EBN) is gaining remarkable attention due to various applications and innovative solutions for efficient energy trading. However, the implementation of the EBN raises multiple challenges such as lack of power loss consideration and congestion management, insufficient computing, and higher transaction costs. This paper introduces a new energy trading approach to counter energy trading challenges effectively and perform optimal energy trading. The proposed systematic design was divided into four steps which execute (i) forming trading pairs through a two-stage Stackelberg game model, (ii) checking security by using the characteristics of directional power flow, (iii) congestion management through the minimum loss multi-path power transmission scheme, (iv) forming smart contracts, and settling transactions. The performance of the proposed framework is verified using 13 nodes EBN implementation, where the results obtained demonstrate accurate energy trading, balanced trading prices, even in congestion conditions.
Gambang: The gamelan wooden xylophone

This work investigated the ironwood used to construct a gambang, which is a traditional musical instrument. A gambang is constructed from a wooden bar with a similar thickness and width but a different length. The sound and established frequencies were compared with the equal tempered scale. The peak differed from the intended pitch and the partials were not always harmonic. This gambang only classified 4 octaves. The audio classification of the gambang was based on signal processing using a Picoscope oscilloscope. This article explains how wood is transformed into musical instruments. The findings revealed that the sound aspect and sound value of the wood keyboard instruments differed from the desired pitch. The third octave notes created C5, E5, E5, G5, and A5 instead of C5, D5, E5, G5, and A5, while the fourth octave pitch produced C6, D6, E6, G6, and A6# instead of C6, D6, E6, G6, and A6. Only the third and fourth octaves exhibited nearly flawless tuning for the gambang.
Emulation of quantum measurements with mixtures of coherent states

We propose a methodology to emulate quantum phenomena arising from any nonclassical quantum state using only a finite set of mixtures of coherent states. This allows us to successfully reproduce well-known quantum effects using resources that can be much more feasibly generated in the laboratory. We present a simple procedure to experimentally carry out quantum-state emulation with coherent states, illustrate it emulating multiphoton NOON states with few phase-averaged coherent states, and demonstrate its capabilities in observing fundamental quantum mechanical effects, such as the Hong-Ou-Mandel effect, violating Bell inequalities and witnessing quantum nonclassicality.
Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence

The logical negation property (LNP), which implies generating different predictions for semantically opposite inputs (p is true iff ¬p is false), is an important property that a trustworthy language model must satisfy.However, much recent evidence shows that large-size pre-trained language models (PLMs) do not satisfy this property.In this paper, we perform experiments using probing tasks to assess PLMs' LNP understanding.Unlike previous studies that only examined negation expressions, we expand the boundary of the investigation to lexical semantics.Through experiments, we observe that PLMs violate the LNP frequently.To alleviate the issue, we propose a novel intermediate training task, named meaning-matching, designed to directly learn a meaning-text correspondence, instead of relying on the distributional hypothesis.Through multiple experiments, we find that the task enables PLMs to learn lexical semantic information.Also, through fine-tuning experiments on 7 GLUE tasks, we confirm that it is a safe intermediate task that guarantees a similar or better performance of downstream tasks.Finally, we observe that our proposed approach 1 outperforms our previous counterparts despite its time and resource efficiency.
From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory

Understanding, modelling and predicting human risky decision-making is challenging due to intrinsic individual differences and irrationality. Fuzzy trace theory (FTT) is a powerful paradigm that explains human decision-making by incorporating gists, i.e., fuzzy representations of information which capture only its quintessential meaning. Inspired by Broniatowski and Reyna's FTT cognitive model, we propose a computational framework which combines the effects of the underlying semantics and sentiments on text-based decision-making. In particular, we introduce Category-2-Vector to learn categorical gists and categorical sentiments, and demonstrate how our computational model can be optimised to predict risky decision-making in groups and individuals.
Dynamics of single-mode nonclassicalities and quantum correlations in the Jaynes–Cummings model

Dynamics of atom–field correlations and single-mode nonclassicalities present in the resonant Jaynes–Cummings model are investigated using negativity and entanglement potential for a set of initial states. The study has revealed the interplay among three different types of nonclassicalities present in the model and established that the nonclassicality is continuously exchanged between the field and atom through atom–field correlations. Further, it is observed that the entanglement potential does not capture all single-mode nonclassicality, and there exists some residual nonclassicality in the reduced single-mode states at the output of the beam splitter that is not captured by the entanglement in which single-mode nonclassicality is quantitatively mapped in Asboth’s criterion. Additional layers of beam splitters are added to deplete all the nonclassicality and to reveal that almost all residual nonclassicality is captured with three layers of beam splitters. Further, the reduced states of the atom and field have zero (non-zero) quantum coherence in the Fock basis when the atom–field correlations are maximum if the field (or atom) has zero (non-zero) quantum coherence initially.
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs

The optimization with orthogonality has been shown useful in training deep neural networks (DNNs). To impose orthogonality on DNNs, both computational efficiency and stability are important. However, existing methods utilizing Riemannian optimization or hard constraints can only ensure stability while those using soft constraints can only improve efficiency. In this paper, we propose a novel method, named Feedback Gradient Descent (FGD), to our knowledge, the first work showing high efficiency and stability simultaneously. FGD induces orthogonality based on the simple yet indispensable Euler discretization of a continuous-time dynamical system on the tangent bundle of the Stiefel manifold. In particular, inspired by a numerical integration method on manifolds called Feedback Integrators, we propose to instantiate it on the tangent bundle of the Stiefel manifold for the first time. In our extensive image classification experiments, FGD comprehensively outperforms the existing state-of-the-art methods in terms of accuracy, efficiency, and stability.
Machine learning via relativity-inspired quantum dynamics

We present a machine-learning scheme based on the relativistic dynamics of a quantum system, namely a quantum detector inside a cavity resonator. An equivalent analog model can be realized for example in a circuit QED platform subject to properly modulated driving fields. We consider a reservoir-computing scheme where the input data are embedded in the modulation of the system (equivalent to the acceleration of the relativistic object) and the output data are obtained by linear combinations of measured observables. As an illustrative example, we have simulated such a relativistic quantum machine for a challenging classification task, showing a very large enhancement of the accuracy in the relativistic regime. Using kernel-machine theory, we show that in the relativistic regime the task-independent expressivity is dramatically magnified with respect to the Newtonian regime.
Diskurse – digital

The aim of this publication is to expand the program and methodological inventory of discourse linguistics in two ways: firstly, by systematizing the specific descriptive categories and analytical tools available for discourses in digital media (e.g., hashtags). Secondly, it seeks to evaluate and develop digital methods and the methods and tools of corpus linguistics to meet the requirements of discourse linguistics.
Building an i2b2-Based Population Repository for COVID-19 Research

Reuse of Electronic Health Records (EHRs) for specific diseases such as COVID-19 requires data to be recorded and persisted according to international standards. Since the beginning of the COVID-19 pandemic, Hospital Universitario 12 de Octubre (H12O) evolved its EHRs: it identified, modeled and standardized the concepts related to this new disease in an agile, flexible and staged way. Thus, data from more than 200,000 COVID-19 cases were extracted, transformed, and loaded into an i2b2 repository. This effort allowed H12O to share data with worldwide networks such as the TriNetX platform and the 4CE Consortium.
Complete conversion between one and two photons in nonlinear waveguides: theory of dispersion engineering

Abstract High-efficiency photon-pair production is a long-sought-after goal for many optical quantum technologies, and coherent photon conversion (CPC) processes are promising candidates for achieving this. We show theoretically how to control coherent conversion between a narrow-band pump photon and broadband photon pairs in nonlinear optical waveguides by tailoring frequency dispersion for broadband quantum frequency mixing. We reveal that complete deterministic conversion as well as pump-photon revival can be achieved at a finite propagation distance. We also find that high conversion efficiencies can be realised robustly over long propagation distances. These results demonstrate that dispersion engineering is a promising way to tune and optimise the CPC process.
Revisiting Software Metrology

Some topics never fade away but also never advance. Software metrology seems to be one of those. Let’s revisit it here.
Subgradient Sampling for Nonsmooth Nonconvex Minimization

Risk minimization for nonsmooth nonconvex problems naturally leads to first-order sampling or, by an abuse of terminology, to stochastic subgradient descent. We establish the convergence of this method in the path-differentiable case and describe more precise results under additional geometric assumptions. We recover and improve results from Ermoliev and Norkin [Cybern. Syst. Anal., 34 (1998), pp. 196–215] by using a different approach: conservative calculus and the ODE method. In the definable case, we show that first-order subgradient sampling avoids artificial critical points with probability one and applies moreover to a large range of risk minimization problems in deep learning, based on the backpropagation oracle. As byproducts of our approach, we obtain several results on integration of independent interest, such as an interchange result for conservative derivatives and integrals or the definability of set-valued parameterized integrals.
The Use of Convolutional Neural Networks in the Prediction of Invasive Ductal Carcinoma in Histological Images of Breast Cancer

This paper presents a deep learning approach for automatic detection and visual analysis of Invasive Ductal Carcinoma (IDC) tissue regions. The method proposed in this work is a convolutional neural network (CNN) for visual semantic analysis of tumor regions for diagnostic support. Detection of IDC is a time-consuming and challenging task, mainly because a pathologist needs to examine large tissue regions to identify areas of malignancy. Deep Learning approaches are particularly suitable for dealing with this type of problem, especially when many samples are available for training, ensuring high quality of the learned features by the classifier and, consequently, its generalization capacity. A 3-hidden-layer CNN with data balancing reached both accuracy and F1-Score of 0.85 and outperforming other approaches from the literature. Thus, the proposed method in this article can serve as a support tool for the identification of invasive breast cancer.
Introduction to Cyber Crime

Modern world gives us many facilities. We can visit anywhere in the world by the help of modern invention. Sometimes Modern Invention creates crime. Cyber Crime is one of them Cyber Crime is a common phenomenon in the world. Cyber Crime is that group of activities made by the people by creating disturbance in network, stealing others important and private data, documents, hack bank details and accounts and transferring money to their own. Cyber Crime, especially through the Internet, has grown in importance as the computer has become central to commerce, entertainment, and government. Cyber crime, also called computer crime, the use of a computer as an instrument to further illegal ends, such as committing fraud, Trafficking in child pornography and intellectual property, stealing identities, or violating privacy. The cyber crime and they its impacts over the society in the form of economical disrupt, psychological disorder, threat to National defense etc. Restriction of cyber crimes is dependent on proper analysis of their behavior and understanding of their impacts over various levels of society. Now a day’s Cyber crime is increasing day by day. People have been greatly suffering for it. It is not only creates human suffering but also put effect on it. So Cyber Crime is one of the major crimes done by computer expert. This paper gives the Introduction of cyber crime.
Interactive Expert System for Budgeting World Bank Consultancy Projects

Cost estimating is a key component of the budgeting of any project, conventional approaches may result in uncertainty and usually do not utilize the knowledge of past projects. Ideally, performing cost estimation under the best predictions of the relevant future conditions is the best approach of economic analysis to evaluate the optimum value for money. With the rise of Artificial Intelligence techniques, there are several Machine Learning (ML) methods that are being studied to capitalize on accuracy gains with regards to cost estimating techniques during the tendering phase. This research aims at developing an expert system that determines the rough order of magnitude for budgeting with an expected range of 30–35% leeway, to forecast the budget of consultancy services of World Bank projects through regression by classification using the Ensemble method. The expert system utilizes advanced ML methods to be able to generate accurate forecasts based on a rigorous database of past projects. The model was trained to identify the influential factors that affect the cost of the services in accordance with the published data related to the project and contract award. Among the studied variables are; sector, procurement method, environmental category, procurement type, region, and overall project budget. The dataset was used as inputs for over 80,000 consultancy contracts globally over the last 14 years. A web interface was then created where the cost estimates of consultancy services tendered by the World Bank are determined. The model developed showed a 72% acceptance rate in terms of model accuracy.
From past to present: Spam detection and identifying opinion leaders in social networks

On microblogging sites, which are gaining more and more users every day, a wide range of ideas are quickly emerging, spreading, and creating interactive environments.In some cases, in Turkey as well as in the rest of the world, it was noticed that events were published on microblogging sites before appearing in visual, audio and printed news sources.Thanks to the rapid flow of information in social networks, it can reach millions of people in seconds.In this context, social media can be seen as one of the most important sources of information affecting public opinion.Since the information in social networks became accessible, research started to be conducted using the information on the social networks.While the studies about spam detection and identification of opinion leaders gained popularity, surveys about these topics began to be published.This study also shows the importance of spam detection and identification of opinion leaders in social networks.It is seen that the data collected from social platforms, especially in recent years, has sourced many state-of-art applications.There are independent surveys that focus on filtering the spam content and detecting influencers on social networks.This survey analyzes both spam detection studies and opinion leader identification and categorizes these studies by their methodologies.As far as we know there is no survey that contains approaches for both spam detection and opinion leader identification in social networks.This survey contains an overview of the past and recent advances in both spam detection and opinion leader identification studies in social networks.Furthermore, readers of this survey have the opportunity of understanding general aspects of different studies about spam detection and opinion leader identification while observing key points and comparisons of these studies.
ReFlex Framework: Rapid Prototyping for Elastic Displays

Creating innovative applications for novel interface technologies poses several challenges for both designers and developers. Rapid prototyping helps to examine concepts, check hypotheses, and acquire early user feedback. In this paper, we present ReFlex, a framework that provides rapid prototyping and development tools for Elastic Displays, and its application in the context of a student project developing a spatial controller for remote 3D navigation.
Developing Clinical Prediction Models Using Primary Care Electronic Health Record Data: The Impact of Data Preparation Choices on Model Performance

To quantify prediction model performance in relation to data preparation choices when using electronic health records (EHR).Cox proportional hazards models were developed for predicting the first-ever main adverse cardiovascular events using Dutch primary care EHR data. The reference model was based on a 1-year run-in period, cardiovascular events were defined based on both EHR diagnosis and medication codes, and missing values were multiply imputed. We compared data preparation choices based on (i) length of the run-in period (2- or 3-year run-in); (ii) outcome definition (EHR diagnosis codes or medication codes only); and (iii) methods addressing missing values (mean imputation or complete case analysis) by making variations on the derivation set and testing their impact in a validation set.We included 89,491 patients in whom 6,736 first-ever main adverse cardiovascular events occurred during a median follow-up of 8 years. Outcome definition based only on diagnosis codes led to a systematic underestimation of risk (calibration curve intercept: 0.84; 95% CI: 0.83-0.84), while complete case analysis led to overestimation (calibration curve intercept: -0.52; 95% CI: -0.53 to -0.51). Differences in the length of the run-in period showed no relevant impact on calibration and discrimination.Data preparation choices regarding outcome definition or methods to address missing values can have a substantial impact on the calibration of predictions, hampering reliable clinical decision support. This study further illustrates the urgency of transparent reporting of modeling choices in an EHR data setting.
Improvement of Hybrid Image Enhancement for Detection and Classification of Malaria Disease Types and Stages with Artificial Intelligence

Malaria is an infectious disease throughout the world where the disease is transmitted by infected female Anopheles mosquitoes. Malaria has some symptoms that are almost like COVID-19. Malaria has several other symptoms, characterized by chills, anemia, cold sweats, nausea and vomiting, and a sudden drop in blood pressure. Identification of the type of malaria begins with preprocessing, feature extraction, and classification for identification. Image improvement is part of the preprocessing stage to improve image quality so that the malaria parasite object in the image can be seen clearly. This study tries to improve the algorithm with hybrid dark and contrast stretching. Performance evaluation of malaria parasite image improvement using Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR). The results obtained with the improvement of dark hybrids and contrast stretching can improve the image quality of malaria parasite objects with MSE value = 0.0095 and PSNR value = 22.8404, compared with dark stretching, contrast stretching, histogram equalization.