Reviewerly/exHarmony huggingface dataset:
WHY YOU MUST VOTE IN AN AFRICAN DEMOCRACY

Purpose: The purpose of this study was to draw attention of election managers in Africa to the negative impact of voter apathy on poll outcomes. It encourages all eligible voters to cast their vote in democratic elections. The instances of contested elections, poor governance, election of ineffective leadership and civic violence could be avoided if the electorate came out in full to give voice to the will of the people. &#x0D; Methodology: The researcher reviewed published literature discussing the performance of postcolonial democracy of independent nations across Africa. With specific reference to academic journals, electoral documents capturing voting patterns and historical records, the researcher uses descriptive statistics and thematic analyses of texts to highlight the effects voter apathy has on national leadership and governance.&#x0D; Findings: Voter apathy compromises the expression of the will of the people and causes an erosion of citizen commitment to social responsibility. It also leads to the emergence of apathetic leadership and weak accountability of government institutions following general elections. The researcher suggests that voter apathy is one of the reasons for civil unrest, passage of unpalatable legislation and election of ineffective leaders.&#x0D; Unique contribution to theory, practice and policy: The researcher calls for democratic governments and electoral bodies in Africa to note, examine and act to preempt the negative impact of voter apathy on election outcomes. Voters are called upon not to presume that a democratic election process will automatically guarantee effective governance outcomes without their participation. The paper also recommends that the process of voter empowerment should be formalized in electoral practice, policy and procedure.
Transformers with Learnable Activation Functions

Activation functions can have a significant impact on reducing the topological complexity of input data and therefore improve the performance of the model. Selecting a suitable activation function is an essential step in neural model design. However, the choice of activation function is seldom discussed or explored in Transformer-based language models. Their activation functions are chosen beforehand and then remain fixed from pre-training to fine-tuning. As a result, the inductive biases they imposed on models cannot be adjusted during this long life cycle. Moreover, subsequently developed models (e.g., RoBERTa, BART, and GPT-3) often follow up prior work (e.g., BERT) to use the same activation function without justification. In this paper, we investigate the effectiveness of using Rational Activation Function (RAF), a learnable activation function, in the Transformer architecture. In contrast to conventional, predefined activation functions, RAFs can adaptively learn optimal activation functions during training according to input data. Our experiments show the RAF-based Transformer (RAFT) achieves a lower validation perplexity than a vanilla BERT with the GELU function. We further evaluate RAFT on downstream tasks in low- and full-data settings. Our results show that RAFT outperforms the counterpart model across the majority of tasks and settings. For instance, RAFT outperforms vanilla BERT on the GLUE benchmark by 5.71 points on average in low-data scenario (where 100 training examples are available) and by 2.05 points on SQuAD in full-data setting. Analysis of the shapes of learned RAFs further unveils that they substantially vary between different layers of the pre-trained model and mostly look very different from conventional activation functions. RAFT opens a new research direction for analyzing and interpreting pre-trained models according to the learned activation functions.
Uncommon Yet Consequential Online Harms

A consistent finding from recent research on harmful online behaviors is that they tend to be concentrated among a small number of individuals. Whether examining misinformation (Grinberg et al. 2019; Guess, Nagler, and Tucker 2019; Allen et al. 2020), radicalizing or partisan content (Hosseinmardi et al. 2021; Muise et al. 2022), or hate speech (Zannettou et al. 2020), a small number of individuals typically accounts for the vast majority of the behavior. Despite this statistical infrequency, the social consequences of such behaviors can be substantial, especially for people targeted by, or within the social networks of, the individuals perpetuating them.
Programmable photonics for optical communications

Abstract As the bandwidth requirements in communication networks continue to increase unabated, wafer level photonic integration is becoming key to miniaturizing components and increasing the number of functionalities, thereby replacing bulky systems. However, a variety of challenges such as long development times and high costs of fabrication are impeding the widescale adoption of this technology in the optical communication field.
Philosophy of Human-Centrism in the System of Anthropological Studies

Purpose. The basis of the presented research is a philosophical and methodological analysis of the human-centrism concept as a new intellectual strategy of comprehending and understanding the prospects of human existence in a situation of information-digital reality, which provides for the consistent solution of the following problems: 1) to make an explication of the conceptual content and semantic loading of human-centrism in the discourses of social philosophy and philosophical anthropology; 2) to analyse the theoretical significance and methodological role of human-centrism in the information-digital reality; 3) to characterize the main parameters of the problematic field of human-centrism in overcoming the crisis of identity and preserving the system of universal human values. Theoretical basis. The objective logic of unfolding of the modern information-digital world transforms philosophical anthropology into a new level of comprehending the human existence. Human-centrism interacts with philosophical anthropology and evaluates it from the point of its metaphilosophical, axiological-existential and methodological content. The assertion of human-centrism is caused by the identity crisis in the context of civilizational transformations, which as a result of the cumulative effect of information technology have a powerful influence on all aspects of human life and thinking. The philosophy of human-centrism as a life-affirming factor determines the development and adaptation of human self-consciousness to the modern socio-cultural environment, determined by information and technological processes. Originality. It is substantiated that human-centrism as a new strategy of modern philosophical and anthropological discourses opens new cognition perspectives of the essence of human in the information-digital reality and contributes to the construction and implementation of new dimensions of identity in interaction with a qualitatively different level of world attitude. Conclusions. The assertion of information-digital reality gives rise to a new type of philosophical and anthropological paradigm, a qualitative manifestation of which is the theory and methodology of human-centrism. It is characterized by a modern level of philosophical reflection, which allows preserving and at the same time renewing the system of universal human values. On this basis, human-centrism is a way of complementarity of all aspects of human life, who reconstructs themselves in a situation of new anthropological dimensions.
Experiences in Collecting 360° Video Data and Collaborating Remotely in Virtual Reality

This paper reports on a pilot project called Remote Research and Collaboration Using VR and 360° Video (RReCo) that was carried out in late Spring 2021 at the University of Oulu, Finland. The project explored new ways of collecting, viewing and analysing video data for the purposes of engaging in remote, collaborative research on social interaction and activity. Here we share our experiences in collecting different types of video data, especially 360°, and relate those to our user experiences in analysing the data together in virtual reality. Our remote multisite data sessions were organised using software for immersive qualitative analytics, virtual reality and live streaming. In this paper, we also reflect on the similarities and differences between our data sets, especially with view to how awareness of different technical setups may help in making informed choices, and thereby increase the reliability of research on social interaction.
The role of Alternatives in the interpretation of scalars and numbers: Insights Insights from the inference task

Numerical noun phrases (NNPs) are ambiguous between at least two and exactly two interpretations. This ambiguity has been commonly discussed as a case of Scalar Implicature (SI), where meaning is enriched by exclusion of an alternative. But the SI approach to NNPs has also been widely challenged. We tested NNPs and scalar expressions in inference tasks and found that scalars were sensitive to a manipulation that altered the relevance of alternatives, whereas exactly readings for NNPs were not. Our findings provide a theory-critical challenge to the SI view of NNPs and support alternative views. EARLY ACCESS
Design and development of the application monitoring the use of server resources for server maintenance

Abstract Websites must assure web server availability in light of the worldwide increase in internet users. Web server monitoring is the process of examining web application server utilization by showing data in the form of statistics or graphs. Data about web server use will help server owners make decisions about server availability. The purpose of this study is to create a web monitoring program capable of retrieving and storing data from Java application server resources. The research methodology utilized in this study is divided into two phases: data gathering and software development. The data gathering step includes doing a literature study, conducting a survey, distributing questionnaires, and analyzing comparable apps. The development phase employs the waterfall methodology. The outcome of this study is a web application that can monitor the Java application server’s resource use and send email alerts when resource usage becomes excessive. To summarize, web application server monitoring may be utilized to alleviate developer workload.
A Fast Algorithm for Hunting State-Backed Twitter Trolls

In recent years, state-backed troll accounts have been adopted extensively by many political parties, organizations, and governments to negatively influence political systems, persecute perceived opponents, and exacerbate divisiveness within societies. Thus, the need for an automatic state-backed troll classification system has increased. Various algorithms have been proposed in the literature to handle this problem, but a majority of them consider all types of trolls as one type which decreases the performance of classification algorithms. Our goal in this paper is to design a thorough method for detecting state-backed trolls on Twitter with the ability to work efficiently in any case regardless of the language, the location, and the purpose of the troll account. For accurate classification, a set of novel effective and powerful features from various categories are proposed. To train our algorithm, we gathered a large and relevant dataset from Twitter. The results show that the proposed algorithm achieves high classification accuracy (approximately 99%) and has the ability to classify state-backed troll accounts regardless of the language or the location of the account.
Is Tiny Deep Learning the New Deep Learning?

The computing everywhere paradigm is paving the way for the pervasive diffusion of tiny devices (such as Internet-of-Things or edge computing devices) endowed with intelligent abilities. Achieving this goal requires machine and deep learning solutions to be completely redesigned to fit the severe technological constraints on computation, memory, and power consumption typically characterizing these tiny devices. The aim of this paper is to explore tiny machine learning (TinyML) and introduce tiny deep learning (TinyDL) for the design, development, and deployment of machine and deep learning solutions for (an ecosystem of) tiny devices, hence supporting intelligent and pervasive applications following the computing everywhere paradigm.
Noise Removal Techniques for Lung Cancer CT Images

Objectives:To analyze various filtering methods to eliminate noises present in the lung CT images and to enhance the image, which help in further evaluation of CT images for accurate lung cancer detection.To compare the proposed method with existing filtering techniques and to find the best filtering technique.Methods: For input lung CT images noises like salt along with pepper noise and speckle noise are added.For noisy images different filtering methods like Median filter, Wiener filter, Gaussian filter and Guided filter are applied.The performances of different filters are computed in terms of metrics for evaluation like PSNR, SSIM, MSE, and SNR.Based on the performance metrics the best filter is selected to remove noise in the lung CT images.Findings: The results of the experiment shows that the median filter is more efficient in comparison to other filtering methods in eliminating noises that exist in lung CT images by owning fewer mean square error (MSE) value of 4.065604, a high SNR value of 36.5931, a high SSIM value of 0.983545, and high PSNR value of 42.0395.Novelty: Different filtering methods are analyzed for different noise densities from 5% to 50% and chosen best filter by considering different evaluation metrics.The proposed method is compared with existing filtering techniques.The method can be used for elimination of noise in the other imaging modalities.
Comparison of How Arabic and English Foreign Language Learners Compliment and Respond to Praises

Researching the speech acts performed between Arabic and English speakers is one of the popular trends in pragmatic research. However, the majority of previous studies have only obtained data from native Arabic and English speakers, or Arabic natives who are learning English as a foreign language. This study intends to fill the research gap of comparing the speech acts of giving praise and responding to praise made by students who are studying both English and Arabic as their foreign languages. This descriptive study used two instruments, namely a discourse completion task questionnaire consisting of twelve scenarios and an interview, involving 70 English learners and 70 Arabic learners from UIN Alauddin Makassar which is the only university in Indonesia that offers the unique Foreign Language Intensification Development (PIBA) program. Results have determined that the dominant praise strategies of Arabic learners are awe and congratulating, while English learners tend to use surprise and congratulating strategies. Both language learners dominantly use the strategies of giving appreciation and reassigning praises as their praise response strategies. This study concluded that Arabic foreign language learners tend to have a small repertoire of praise and praise response strategies, while English foreign language learners’ arsenal of strategies is much more diverse.
Neurons-Samples Theorem

Artificial neural network (NN) has become one of the most widely used machine learning (ML) models for problems in science and engineering, including the fast-developing artificial intelligence (AI) technology. In training an NN model for a problem, one of the most frequently asked questions is how many neurons or layers of neurons should be used for a given dataset with a number of samples (or data points). This paper provides an answer to this critical question, by presenting a Neurons-Samples Theorem, which states, in short, that the number of neurons should be equal or less than the number of samples used to train the NN.
Quantum metrology with N-qubit W superposition state under local and nonlocal operation

Abstract We study the performance of N- qubit W superposition state in quantum metrology. Taking advantage of the general Ising-type Hamiltonian (including local and nonlocal operation), we analytically present the Quantum Fisher information (QFI) of N -qubit W superposition state under different situations and then investigate its phase sensitivity. The results show that the phase sensitivity under local operation displays a crossover from W state to GHZ state, where it is same as W state in few-qubit case ( N ≤ 6) but asymptotically equal to GHZ state for large qubit case ( N &gt;&gt; 1). Interestingly, the 4-qubit W superposition state is found to have the same sensitivity with 4-qubit GHZ state, but be more robust than latter. Besides, the optimal theoretical proposals are provided for ideal metrology. Under the phase/amplitude damping channel, the phase sensitivity of W superposition state (except for N =3) is ultimately decreased to standard quantum limit, while it turns worse in depolarizing channel. Finally, the tunable phase sensitivity under nonlocal operation is studied and the general Heisenberg limit is surpassed with the increasing interaction strength γ . Meanwhile, a plateau of QFI and phase sensitivity is found for all large qubit W superposition state, which is similar to the study of GHZ state and again verifies the common feature of GHZ-type states in quantum metrology.
Implementation Of Mobile-Based OOAD Interactive Learning Media

The lack of interest in student learning is due to the learning media used are less attractive and effective to understand the material. This study aims to implement mobile-based interactive learning media regarding OOAD material. The media feasibility test uses a blackbox testing scenario and the analysis uses the Gutman scale technique. From the test results, it was found that the percentage of blackbox testing was 100% and the functional requirements test by the resource persons obtained a percentage of 100%. Based on the results of these studies, it can be explained that this learning media is very good and has been feasible to be implemented.
Detection of Student Drowsiness Using Ensemble Regression Trees in Online Learning During a COVID-19 Pandemic

Online lectures are mandatory to deal with the implementation of education during the COVID-19 pandemic. This significant change certainly creates a different experience for students. Regarding online learning, several public health experts and ophthalmologists say that residual radiation from electronic screens is causing an epidemic of eye fatigue. Research on smart classrooms actually appeared several years ago, but in reality it has not been implemented according to the planned concept. The current smart classroom research environment only uses outdated methods, which make the computer system incongruent (such as decision trees in video feeds) or only to the level of empirical studies or blueprints, which are not much help for other academic footing or reference materials. to students. This study aims to build an intelligent system that can evaluate students' attention during online classes, use teaching videos as learning feeds and input for predictions and also use advanced algorithms in several computational domains, namely face segmentation, landmarking, PERCLOS observations, Yawning and decision analysis using Ensemble Regression Trees to detect students' sleepiness, which is expected to patch up the shortcomings of the PERCLOS algorithm and the problems found in the single regression tree-based implementation. Based on the results of the tests that have been carried out, the system developed has been able to observe sleepy objects in learning videos with an accuracy of 80% so that later it can be a lesson for teachers why there are students who are sleepy during online classes either because of uninteresting material or other reasons.
Classification of Damaged Road Images Using the Convolutional Neural Network Method

Objective: Automatic identification is carried out with the help of a tool that can take an image of road conditions and automatically distinguish the types of road damage, the location of road damage in the image and calculate the level of road damage according to the type of road damage.Design/method/approach: Identification of damaged roads usually uses manual RCI system which requires high cost. In this study, a comparison framework is proposed to determine the performance of the image pre-processing model on the image classification algorithm.Results: Based on 733 image data classified using the CNN method from 4 models of pre-processing stages, it can be concluded that training from grayscale images produces the best level of accuracy with a training accuracy value of 88% and validation accuracy reaching 99%.Authenticity/state of the art: Testing of 4 pre-processing models against the classification algorithm used as a comparison resulted in the best algorithm/method for managing road images.
Development of a smart attendance system using near field communication (SMAT-NFC)

Taking attendance for classes has become more challenging, especially at institutions, due to the growing students population. Typically, the teacher calls names and writes them down. This approach has a number of drawbacks because when recording their attendance, students are diverted from the presentation and may register for absent classmates. The smart attendance solution suggested in this study allows students to tap their student identity (ID) card on a near field communication (NFC) reader, streamlining the attendance procedure by using near field communication (NFC) technology. The technique is affordable, effective, and simple to keep up. Automating student attendance saves time and alleviates anxiety. The average time for the NFC system to take a student's attendance is 3.5 seconds, compared to 16 seconds for the conventional method. The inaccuracy associated with recording attendance is reduced by 31% thanks to NFC technology. The numerical results of the NFC-based scheme are compared with the traditional scheme and observed that the proposed scheme outperforms the conventional scheme.
Abnormal Data Cleaning Method for Wind Turbines Based on Constrained Curve Fitting

With the increase of the scale of wind turbines, the problem of data quality of wind turbines has become increasingly prominent, which seriously affects the follow-up research. A large number of abnormal data exist in the historical data recorded by the wind turbine Supervisory Control And Data Acquisition (SCADA) system. In order to improve data quality, it is necessary to clean a large number of abnormal data in the original data. Aiming at the problem that the cleaning effect is not good in the presence of a large number of abnormal data, a method for cleaning abnormal data of wind turbines based on constrained curve fitting is proposed. According to the wind speed-power characteristics of wind turbines, the constrained wind speed-power curve is fit with the least square method, and the constrained optimization problem is transformed into an unconstrained optimization problem by using the external penalty function method. Data cleaning was performed on the fitted curve using an improved 3-σ standard deviation. Experiments show that, compared with the existing methods, this method can still perform data cleaning well when the historical wind turbine data contains many abnormal data, and the method is insensitive to parameters, simple in the calculation, and easy to automate.