Reviewerly/exHarmony huggingface dataset:
Speckle noise reduction and image segmentation based on a modified mean filter

Image segmentation is an essential process in many fields involving digital images. In general, segmentation is the process of dividing the image into objects and background image. Image segmentation is an important step in the object detection process. It becomes more critical if a given image is corrupted by noise. Most digital images are corrupted by noises such as salt and pepper noise, Gaussian noise, Poisson noise, speckle noise, etc. Speckle noise is a multiplicative noise that affects pixels in a gray-scale image, and mainly occurs in low level luminance images such as Synthetic Aperture Radar (SAR) images and Magnetic Resonance Image (MRI) images. Image enhancement is an essential task to reduce specklenoise prior to performing further image processing such as object detection, image segmentation, edge detection, etc. Here, we propose a neighborhood-based algorithm to reduce speckle noise in gray-scale images. The main aim of the noise reduction technique is to segment the noisy image. So that the proposed algorithm applies some luminance to the original image. The proposed technique performs well at maximum noise variance. Finally, the segmentation process is done by the modified mean filter. The proposed technique has three phases. In phase 1, the speckle noise is reduced and the contrast adjustment is made.  In phase 2, the segmentation of the enhanced image is processed. Finally, in phase 3, the isolated pixels in the segmented image are eliminated and the final segmented image is generated. This technique does not require any threshold value to segment the image; it will be automatically calculated based on the mean value.
Single Image Dehazing based upon Modified Image Enhancement Algorithm

The images acquired in the bad weather such as smoke, fog and haze are often disturbed to a large extent. Therefore, dehazing has become a challenging task in many fields. By combining the quadtree atmospheric light estimation model with the guided filtering algorithm, this paper proposes an image enhancement algorithm, which can significantly improve the image contrast and preserve the details. This method can significantly improve color image and gray image without prior information. The multi-scale decomposition module provides basic image and detail image for the subsequent smoothing module and enhancement module. Through the atmospheric light intensity estimation algorithm and transmittance estimation algorithm described above, the accuracy of image restoration can be significantly improved. The details of the image can be saved by processing the residual image by guided filtering algorithm. Finally, the final image can be obtained by color correction algorithm.
Detecting African hoofed animals in aerial imagery using convolutional neural network

Small unmanned aerial vehicles applications had erupted in many fields including conservation management. Automatic object detection methods for such aerial imagery were in high demand to facilitate more efficient and economical wildlife management and research. This paper aimed to detect hoofed animals in aerial images taken from a quad-rotor in Southern Africa. Objects captured in this way were small both in absolute pixels and from an object-to-image ratio point of view, which were not perfectly suit for general purposed object detectors. We proposed a method based on the iconic Faster region-based convolutional neural networks (R-CNN) framework with atrous convolution layers in order to retain the spatial resolution of the feature map to detect small objects. A good choice of anchors was of prime importance in detecting small objects. The performance of the proposed Faster R-CNN with atrous convolutional filters in the backbone network was proven to be outstanding in our scenario by comparing to other object detection architectures.
Counterexamples to the Low-Degree Conjecture

A conjecture of Hopkins (2018) posits that for certain high-dimensional hypothesis testing problems, no polynomial-time algorithm can outperform so-called statistics, which are low-degree polynomials in the data. This conjecture formalizes the beliefs surrounding a line of recent work that seeks to understand statistical-versus-computational tradeoffs via the low-degree likelihood ratio. In this work, we refute the conjecture of Hopkins. However, our counterexample crucially exploits the specifics of the noise operator used in the conjecture, and we point out a simple way to modify the conjecture to rule out our counterexample. We also give an example illustrating that (even after the above modification), the symmetry assumption in the conjecture is necessary. These results do not undermine the low-degree framework for computational lower bounds, but rather aim to better understand what class of problems it is applicable to.
Fictional Names: Reference, Definiteness and Ontology

Definite linguistic expressions, for example proper names and singular and plural pronouns, are easy to introduce.Indefinite expressions may pave the way, but are not essential.It is also not essential that there be entities to which the successfully introduced definites refer.This is the underlying fact that makes fiction possible, and it gives guidance about fictional names: we have no need in general to suppose that there exist entities to which they refer.
An Efficient Super-Resolution Single Image Network using Sharpness Loss Metrics for Iris

Most of the state of the art super-resolution methods use deep networks with large filter sizes. Therefore, they need to train and store a correspondingly large number of parameters, thereby making their use difficult for mobile devices applications such as recognition of individuals from selfie images. To achieve an efficient super-resolution method, we propose an Efficient Single Image Super-Resolution (ESISR) algorithm, which takes into account a trade-off among the efficiency of the deep neural network, the size of the filters, and the sharpness of the images. To that end, the method implements a novel loss function based on the Sharpness metric. This metric turns out to be more suitable for recovering the quality of the eye images. Our method drastically reduces the number of parameters when compared with Deep CNNs with Skip Connection and Network (DCSCN): from 1,754,942 to 27,209 parameters when the image size is increased by a factor of 2 (x2), from 2,170,142 to 28,654 parameters when increased by 3 (x3), and from 2,087,102 to 64,201 parameters when increased by 4 (x4). Furthermore, the proposed method maintains the sharpness quality of the images.
«Double First-Class» Project Implementation: The Influence of Local Government in China

This paper discusses the influence of local governments over the realization of «Double First-Class» project in China. Based on the qualitative analysis of 36 universities’ mid-term reports, the spillover effect on local economic and social development was found to be enhancing the core competitiveness of local governments. Text analysis of eight local governments’ «Double First-Class» implementation reports showed that the way of their promoting the «Double First-Class» construction were investment and talent introduction. The local participation in the «Double First-Class» competition under the constraint of national financial resources leads to the Matthew effect of university development. Universities selected for «Double First-Class» will get more resources and develop better, while other universities will be left behind. Therefore, the central government needs to intervene the competition of local governments.
Methods Of Student Psychological Culture Development – Future Specialists In The University

The article considers the concept of «psychological culture»; the structure of psychological culture, which includes a cognitive component that determines a person's perception of himself, other people, psychological patterns of behaviour and activity; emotional-evaluative component, including the relationship of a person to himself and attitude to the world; the process-activity (technological) component that determines how a person interacts with the environment and himself; the value-semantic component that determines the significance of the actions performed, the selected methods of behaviour and activity and communicative component that determines the possibility of interpersonal human interaction; the stages of the formation of psychological culture and the methods of development of the psychological culture of future specialists in the educational space of the university are specified.The article describes the results of a study of student participation in the work of the psychological club on the development of the communicative component of the psychological culture of future specialists in the educational space of the university.The students of the experimental group participated in a program for the development of psychological culture in the form of a psychological club.The selection of the content of the club work was carried out according to the components of psychological culture: cognitive, process-activity, emotional-evaluative, value-semantic and communicative components.The forms of work of the psychological club included themed club hours; thematic events (evening of poetry and music, meetings with interesting people), thematic meetings, thematic field classes (nature, museum); psychological training and workshops.
Correction to: Measuring the Quality of Test-Based Exercises Based on the Performance of Students

A Correction to this paper has been published: https://doi.org/10.1007/s40593-021-00245-3
Real Authors and Fictional Agents

A suitable account of fiction must involve a conceptual distinction between (at least) the following figures, or roles: real authors, fictional narrators, fictional authors.Real authors are the real original utterers of fiction-involving sentences in their fictional use, the one mobilizing pretense.They may coincide (although this would be rare) either with fictional narrators or with fictional authors.A fictional narrator is the protagonist of a tale that is narrated in the first person: the internal point of view on the tale.A fictional author constitutes the tale's external point of view that vividly manifests itself when the tale is narrated by no protagonist.Fictional narrators, however, never coincide with fictional authors.For either one or the other is the fictional agent, the one-place factor of a narrow fictional context of interpretation whose contribution is to provide a fictional truth-conditional content to the fiction-involving sentences of the relevant tale.
A Controllable Text Simplification System for the Italian Language

Text simplification is a non-trivial task that aims at reducing the linguistic complexity of written texts. Researchers have studied the problem by proposing new methodologies for addressing the English language, but other languages, like the Italian one, are almost unexplored. In this paper, we give a contribution to the enhancement of the Automated Text Simplification research by presenting a deep learning-based system, inspired by a state of the art system for the English language, capable of simplifying Italian texts. The system has been trained and tested by leveraging the Italian version of Newsela; it has shown promising results by achieving a SARI value of 30.17.
Using a platform based on the Basic profile of ISO/IEC 29110 to reinforce DevOps environments

The growth of software demand has caused more competition among customers who expect faster changes and better quality in the software products delivered. The need to satisfy the continuous requirements of the market, the need to keep the quality of products and services, as well as the need to improve their processes become a difficult task for organizations. DevOps arises to handle this continuous change because it addresses the reduction of the gap between development and operation. However, the influence of this new paradigm in organizations becomes a big challenge, mainly related to a cultural change. If the change of culture is not properly implemented, it could impact a team with negative consequences. In this context, there is no specific guidance that helps organizations with their implementation. Based on the lack of guidance, this paper presents the Reinforced DevOps Guidance, which aims to help teams to achieve an evolution of their software development, software delivery, and project management processes toward a proper DevOps implementation. The guidance uses a web platform that allows a dynamic implementation. That way helps teams to understand the set of tasks to be followed and the impact of their implementation on their current organizations. This paper shows both an overview of the guidance, highlighting the web platform, and its application in a very small entity (VSE). The results show that the use of the guidance: provides support toward addressing the effort in VSEs; gives the information of the technology, processes, and teams aspects that should be improved; and allows the cultural change in a pace supported by VSEs.
Why Do Educational People Commit Corruption in Communicating Their Personal Brands?

Amid the increasingly fierce competition, personal branding has become necessary for modern workers today, including professionals in education.This article explores why people in the educational milieu, especially higher education, commit corruption in communicating their brands.We conducted interviews and one-on-one discussions with ten people who work as instructors, researchers, and structural officials in educational institutions.We identified three main reasons motivating them to commit corruption in personal brand communication: financial reasons, managerial reasons, and communicative reasons.Financial reasons refer to economic goals in improving self and family well-being and meeting daily and long-term needs.On the other side, managerial motives refer to organizational performance goals that impact personal career advancement.Meanwhile, communicative intentions apply to imaging related to the need for identity and social recognition.This research can reference decision-makers to assess performance and determine what rewards or punishments are suitable for individuals in their organizations.
Learning Document-Level Label Propagation and Instance Selection by Deep Q-Network for Interactive Named Entity Annotation

High quality annotated named entity corpora are essential language resources, but fully manual annotation is time-consuming. Interactive annotation offers an efficient alternative where humans and machines collaborate. Instances of named entity mentions tend to share the same label, when they co-occur in the same document and have similar surface forms. After selecting an instance in one sentence for manual annotation, the label of the instance can be propagated to instances in other sentences. This kind of document-level label propagation can be used to reduce human effort and improve annotation quality in interactive annotation. However, most existing literature assumes instances within different sentences are independent, and ignores document-level label propagation. This paper proposes a reinforcement learning-based approach, which learns to propagate labels among the instances within a document for interactive named entity annotation. In addition, our approach also learns instance selection for manual annotation. We optimize the objective which is a trade-off between human effort and annotation quality by training a deep Q-network. Our approach reduces human effort by more than 42% compared to baseline approaches, for achieving the same annotation quality (0.95 measured by F1 averaged on three datasets).
Sparkol VideoScribe Application for Instruction

Different types of technical applications were not included in the instruction at the Faculty of Education, University of Bengkulu.Every teaching activity is just making a paper and a daily presentation of PowerPoint, then discussing it together.This activity appears to be monotonous.This research was performed using the Sparkol Videoscribe program, which was then uploaded to YouTube and discussed in the Google classroom to optimize learning.The students are taught how to make and upload the videos to YouTube and then respond to what has been done.We asked the students to provide a feedback on the Google form to see the effect of this analysis.As a result, the students were very happy and concluded that this technique could optimize the learning, especially based on technology.This study presents Sparkol Videoscribe which can easier teachers to pass on the knowledge and increase motivation and can be an alternative media that can increase student interest in learning.
The Differential Impact of Corporate Blockchain-Development as Conditioned by Sentiment and Financial Desperation

This paper investigates whether companies that initiated blockchain-development as a primary short-term corporate strategy, exhibited abnormal corporate performance as conditioned by financial performance, leverage, social media sentiment and previous experience of technological development. Results indicate that investors were subjected to a very sophisticated form of asymmetric information designed to propel sentiment and market euphoria. Technological-development firms are found to financially behave in a profoundly different fashion to speculative firms with no background in ICT technology, who experience an estimated, increased one-year probability of default of 170bps. Information shrouding is found to be of particular regulatory concern. Rating agencies are found to have under-priced the risk on-boarded by these speculative firms as they announced their entry into the blockchain sector, failing to identify that such firms should be placed under an increased degree of scrutiny. Sophisticated digital platforms, regulatory unpreparedness and mis-pricing by trusted market observers has resulted in a situation where investors and lenders have been placed in a compromised position with direct exposure to a financial asset class becoming known for criminal activity, financial losses and frequent reputational damage.
ANALYSIS OF DENOTATIVE AND CONNOTATIVE MEANING OF INDONESIAN SEXIST METAPHORS

This research was conducted to analyse the denotative and connotative meaning of Indonesian sexist metaphors. The metaphors were collected from various sources, such as online sources, Indonesian Dictionary (KBBI), song lyrics, poems, movies, and newspaper. There were two research questions formulated as follows: 1) What are the Indonesian sexist metaphors? and 2) What are the denotative and connotative meaning of the sexist metaphors? The method of this research was qualitative method, specifically content analysis. This method has two sources, they are primary and secondary sources. The primary source was the metaphors found in various sources. The secondary sources were theories of metaphor, denotative and connotative meaning, and sexist language. The metaphors were analysed using Lakoff and Johnson’s theory (1980) and the meaning of the metaphor was analysed using Parera’s theory (2004). In identifying the sexist in the metaphor, this study used the sexist language theory from Mills (1995). The results of this research showed that there were 32 Indonesian sexist metaphors, 16 of which belonged to female and the other 16 to male. Each metaphor had both connotative and denotative meanings. Based on the findings, it could be concluded that Indonesian language has sexist metaphors for both male and female.
High-speed three-dimensional surface measurement using band-limited illumination profilometry (BLIP)

High-speed three-dimensional (3D) surface imaging by structured-light profilometry is currently driven by industrial needs, medical applications, and entertainment. However, the limitation of pattern projection speed has prevented the structured illumination to reach the kilohertz (kHz) level. The limited bandwidth of the data transmission has prevented the camera from streaming data continuously, which thus has brought difficulties in kHz-level image acquisition, processing, and display of 3D information during the occurrence of dynamic events (i.e., in real time). Besides, the tradeoff between the camera’s sensor readout rate and the activated pixel numbers has strained the existing methods from reaching a large field of view (FOV) at kilohertz (kHz)-level acquisition. To overcome these limitations, we have developed highspeed band-limited illumination profilometry (BLIP) in two configurations. The first configuration, employing a single camera with a CoaXPress interface (CI), enables real-time 3D surface information reconstruction at 1 kHz. The second configuration, employing two cameras with a CI, uses temporally interlaced acquisition (TIA) to improves the 3D imaging over 1000 frames per second on a field of view (FOV) of up to 180×130 mm<sup>2</sup> (corresponding to 1180×860 pixels) in captured images. We have demonstrated the systems’ performance by imaging various static and fast-moving 3D objects. CI-BLIP has been applied to fluid mechanics by imaging dynamics of a flag, which allowed observation of the wave propagation, gravity-induced phase mismatch, and asymmetric flapping motion. Meanwhile, TIA-BLIP has empowered the 3D visualization of glass vibration induced by sound. We expect BLIP systems to find diverse scientific and industrial applications.
Optimal Fuzzy Cluster Partitioning by Crow Search Meta-Heuristic for Biomedical Data Analysis

Biomedical data is often more unstructured in nature, and biomedical data processing task is becoming more complex day by day. Thus, biomedical informatics requires competent data analysis and data mining techniques for designing decision support system's framework to solve clinical and heathcare-related issues. Due to increasingly large and complex data sets and demand of biomedical informatics research, researchers are attracted towards automated machine learning models. This paper is proposed to design an efficient machine learning model based on fuzzy c-means with meta-heuristic optimizations for biomedical data analysis and clustering. The main contributions of this paper are 1) projecting an efficient machine learning model based on fuzzy c-means and meta-heuristic optimization for biomedical data classification, 2) employing benchmark validation techniques and critical hypothesises testing, and 3) providing a background for biomedical data processing with a view of data processing and mining.
Designing A Smart Monitoring and Alert System for Malaria Patients Based on IoT in Burundi

Malaria is an infectious disease that has a devastating effect on humans but is contagious and preventable. There were 228 million malaria cases globally in 2018, with 93% of patients and 94% dying showing up in Africa within Burundi, possessing a massive growth (51%). This study proposes an IoT design for smart monitoring and alert systems for malaria patients in Burundi. After being tested positive while they are home drinking tablets prescribed by the doctor, the patients will use a body temperature sensor. When the situation worsens, the patient shall be hospitalized and use the water level sensor to monitor the serum level, which contains quinine. Body temperature and water level sensor will be connected to Arduino Microcontroller, and all information will be gathered and sent to the IoT server through the internet. The system design is authentic and adequate in reducing mortality in Burundi. Besides, the system is equipped to set up a caution message to the medical staff in case of the patient's extreme conditions. It can be concluded that a Smart monitoring and alert system is essential for monitoring malaria patients and can alert in case of a severe condition.