Reviewerly/exHarmony huggingface dataset:
Evolutionary Algorithm for Optimized CNN Architecture Search Applied to Real-Time Boat Detection in Aerial Images

Abstract When processing the detection of boats in aerial images by neural networks, we have always been concerned about the execution time of these networks in the equipment on board the Unmanned Aerial Vehicle (UAV). Throughout its mission, the UAV will capture images that must be processed in real time. For this purpose, a network optimized for execution time is essential. This article proposes an enhanced Network Architecture Search (NAS) method for searching for time-optimized detection networks, for a given dataset, using an evolutionary algorithm. The search uses mutations as a mechanism of evolution that affect the structure of the network and the hyper-parameters of its layers. Its original fitness function allows the choice of architectures that are not very greedy in terms of operations, specifically favouring small networks whose advantages are to be fast and quick to train, thus accelerating the search algorithm. Using this method, we were able to obtain detection networks with an improved mean Average Precision (mAP) compared to the initial network (parent) but with much fewer FLoating-point OPerations (Flops): 68% of operations reduction. This induces considerable gain in terms of execution time with 50 Frames Processed per Second (FPS) in an embedded environment on a drone.
The choice of career in the distance education programs of the "Minuto de Dios University Corporation, UNIMINUTO"

The objective of this article is to offer elements that explain the career choice of university students of the "Corporación Universitaria Minuto de Dios.ONE MINUTE".The question that seeks to answer is how influence the socioeconomic variables of students to choose a virtual educational program?The school enrollment database of the same institution from 2005 to 2013 was used.The interpretation of the information was carried out with the theory of the context of action Young, Valach and Collin (2002).It is concluded that single women from 19 to 21 years of age who study an undergraduate degree, mainly occupational health administration, psychology and public accounting, and who come from a low socioeconomic level, choose such careers because their labor market expectations are high and, they fulfill the function of fulfilling themselves as people in said careers.
MECHANISMS OF PROFESSIONAL COMPETENCE DEVELOPMENT OF FUTURE TEACHERS BASED ON THE USE OF CLOUD TECHNOLOGIES

The Internet provides various services for storing information for the modern user. These services, in turn, affect education, open many opportunities for improvement and activation of the educational process, stimulate the emergence of new teaching methods.
Playing to the quantum beats

Computers and digital technology are central to the modern music industry – but what could quantum computers bring to the party? Philip Ball tunes in to an avant-garde band of musicians and scientists who are exploring how quantum computing can be used to make and manipulate music.
Sentiment Analysis for E-Commerce

Abstract: The rise of e-commerce has brought about new challenges in understanding customers; needs and preferences. One of the ways to better understand customers is to analyse their feedback on products and services. Sentiment analysis is a powerful tool that can be used to gain insights into customers&amp;; opinions and emotions towards products and services. The proposed project, "Happy Shopping" is an application that uses sentiment analysis to analyse customer feedback on products and services. The interface will display a summary of the most common sentiments expressed by customers for each product or service. This will enable customers to make more informed buying decisions based on the sentiments expressed by other customers. In conclusion, the "Happy Shopping" project aims to leverage sentiment analysis to enhance the customer experience in an e-commerce platform. The project will provide customers with valuable insights into other customers; sentiments towards products and services, as well as improve customer support services.
USER AUTHENTICATION USING EYE-BLINK PASSWORD

Personal identification numbers (PINS) are used to authenticate a user for security purpose. In order to authenticate himself the user should physically input the PIN, which might be susceptible to password cracking via shoulder surfing or thermal tracking. Authenticating a PIN with hands-off eye blinks PIN entry techniques, on the opposite hand, offers a safer password entry option by not leaving any physical footprints behind. User authentication using eye-blink password refers to finding the attention blinks in sequential image frames, and generation of PIN. This paper presents an application with three layer authentication system that are face detection, PIN entry by eye blink and OTP (One Time Password) to avoid thermal tracking attacks and shoulder surfing.
Reassessment of Geophysical Investigation of Zinc(Z), Cupper (Cu) and Lead (Pb) Potential Mineralization Deposits using Aeromagnetic Data in Wase Middle Benue Trough, Nigeria

Abstract: This effort aims to delineate structures responsible for exploring mineral resources in the Wase middle Benue trough area of Northcentral, Nigeria by evaluating the aeromagnetic data over that region. Oasis Montaj version 6.4 was used to qualitatively and quantitatively evaluate the aeromagnetic data. Techniques for Source Parameter Imaging (SPI) interpretation, including First Vertical Derivative, Analytical Signal, Upward Continuation, and Tilt Derivative, were used in the study. Analytic signal and tilt derivative maps showed different patterns of magnetic signatures independent of magnetization direction with amplitude ranging from 0.1 to 0.2nT/m and -1.3 to 1.4nT/m. They also showed qualitative interpretation applied aids enhance linear features at the expense of deep intrusive bodies like lineaments, fractures, and fault zones which trend Northeast Southwest (NE- SW) in the map of the study area. The boundaries of the geologically interesting anomalies were noticeably accentuated by the upward continuation done at 2 km. Nonetheless, this provides an approximation of the depth of the source of magnetic signatures in the area. The depth measured by SPI ranged from 103.9 to 1473.0 m. In the Eastern and Western regions of the region, respectively, shallow and deep-seated bodies are seen. The depth of the analysis was about 2000m, in conclusion, it was believed that the linear characteristics in the area that are trending northeast to southwest are possible structures for the prospecting of mineral deposits like zinc, copper, and lead
MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification

This article presents a dataset of 10,917 news articles with hierarchical news categories collected between 1 January 2019 and 31 December 2019. We manually labeled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories. This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news.
Entanglement Purification for Logic-Qubit of Photon System Based on Parity Check Measurement Gate

It has been found that logic-qubit entanglement has great potential for applications in quantum communication and quantum networks in recent years. However, along with the effects of noise and decoherence, the fidelity of the communication transmission can be greatly reduced. In this paper, we investigate the entanglement purification of logic bit-flip error and phase-flip error in polarization logic-qubit entanglement based on the parity-check measurement (PCM) gate, which is constructed by the cross-Kerr nonlinearity and used to distinguish the parity information of two-photon polarization states. The probability of entanglement purification is higher than the scheme using the linear optical method. Moreover, the quality of logic-qubit entangled states can be improved by a cyclic purification process. This entanglement purification protocol will be useful in the future when faced with long-distance communication with logic-qubit entanglement states.
Techniques Used by College Level English Teachers to Assess Interactive Speaking: A Phenomenological Study

Interactive speaking is a crucial component of speaking skill. It is essential for effective and successful communication. Assessment of students’ interactive speaking helps teachers to enhance students’ overall speaking proficiency in a second or foreign language. Several techniques can be used to evaluate interactive speaking and the application of techniques may vary according to the forms of speaking. The present article focuses on exploring the techniques used by college level English language teachers to assess the interactive speaking of the students who study major English at the bachelor's level. The study was conducted using qualitative approach and descriptive phenomenology design. The sample population included 13 English language teachers who taught English at affiliated colleges in the Banke district. I used convenience sampling to select the colleges and simple random sampling to select the sample population. The only technique used for collecting data was structured interview. Each of the interviews was recorded using an audio recorder and transcribed verbatim. The data was analyzed using thematic approach to qualitative data analysis. The study revealed that oral question and answer, formal and informal debates, interviews, role plays, and discussion are the most commonly used techniques to assess interactive speaking skill at the college level. The study concluded that the techniques used in assessing interactive speaking may slightly vary depending on the level of the students but the teachers can use these techniques to the students of all levels.
Fake Currency Detection Using Image Processing and Random Forest Algorithm

It became relatively easy to make counterfeit currency as technology advanced, such as colour printing. Counterfeit notes are produced so precisely that it's impossible to tell the difference between a genuine and a fake. We can define counterfeit currency notes as copies of actual currency notes produced without the approval of the state or central authority and used to conduct criminal activities. A parallel economy is run with the help of this fake currency note. This parallel economy causes a country's economy to deteriorate, lowering the value of its currency in the global market. As a result, it became critical to build a comprehensive technique to detect counterfeit notes to decrease the flow of counterfeit notes into the market. The fake currency detection system for Indian cash using image processing and machine learning is discussed in this research study. In order to identify real currency notes based on the distinctive features seen on the Indian currency note of 100 rupees, this research uses image processing and the random forest approach. While omitting the texture and colour of the note, banknote identification is accomplished by comparing received banknotes to a database of previously learned images. On the sample size of 400 notes, we found accuracy of 84.25%, recall of 66.25%, and precision of 78.63%. The recommended solution outperforms convolutional neural networks in terms of accuracy and training speed while using a simpler methodology. To prevent inflation and economic loss, this approach can also be used to different types of currency notes.
104 The use of language in affective computing for human-computer interaction

The field of Human-Computer Interaction has traditionally focused on the study, design, and implementation of techniques that facilitate an effective communication between a computational system and the user. In the last decades, the developments in this area have evolved from the design of useful and usable interfaces to the construction of complex interactive systems able to motivate and engage the user in the execution of a specific task. A relatively new field of research that has contributed to the advancement of human-computer interaction in this direction is known as affective computing. The term affective computing was coined in 1997 to refer to efforts dedicated to the construction of artificial systems able to recognise emotional states from the user and simulate emotions in advanced interactive systems such as embodied conversational agents and robots. A key component in the development of these systems is the use of natural language, as a source to identify human emotions, and as a medium to interact with the user. In this chapter we present a brief overview of the affective computing research area, emphasising how language is used for emotion recognition and emotional speech production. In the second part of the chapter, we describe our work in the recognition of emotions using paralinguistic information and the modelling of emotions for embodied conversational agents.
Law on implementation of educational socialization in Vietnam

In Vietnam, the Socialization of education was officially introduced and implemented from the Government's resolution, on the direction and policy of the socialization of educational activities, health, and culture. These causes aim to gradually increase people's enjoyment of education, health care, culture, and physical and mental development. However, in implementing the Socialization of education, there are some difficulties, limitations, and inadequacies, so perfecting the system of legal regulations on education socialization will contribute to solving the above challenges. This study aims to analyze the concept of socialization in education. It uses a descriptive method based on government resources and previous literature. Socialization is building a community of people's responsibility for creating and improving a healthy and favorable socio, economic environment for educational, medical, and cultural activities in each area. At the local level, this is the responsibility community of the Party committee, the People's Council, the People's Committee, mass organizations, economic organizations, and businesses located in the locality and of everyone. Keywords: Education; law; mechanism; socialization; socialization of education.
Discussion on the Effective Use of Error Resources in Primary School Mathematics Classroom

With the development of the times, most primary and secondary schools have begun to carry out curriculum and teaching reform, in order to furthercultivate students’ ability of inquiry thinking. Mathematics is a relativelyabstract course. With students’ high thinking ability, they are faced withmany problems and challenges. In this process, the teaching mode ofteachers is too traditional. In class, teachers always give endless lectures, sothat students have little time for independent thinking and inquiry learning.At the same time, teachers pay too much attention to the use of “correctresources” in the teaching process, but ignore the use of “wrong resources”and the use of value. The long-term continuation of this teaching modewill reduce the enthusiasm and initiative of primary school students inmathematics learning, and learning is very bad for students. Therefore,teachers should pay attention to the correct use of “wrong resources”, andmake effective use of these resources for classroom teaching, in order toimprove students’ learning enthusiasm and the cultivation of inquiry ability.
An Efficient Blockchain-Based Framework For File Sharing

Abstract File sharing is the foundation of the Internet. But the traditional centralized service architecture will result in huge infrastructure costs and maintenance costs. Due to the lack of effective file management system, a lot of sensitive information is out of control and loss of confidentiality document has occur from time to time. In order to address the difficulty of tamper detection and the lack of supervision in the entire process of file transmission in the current Internet environment, this paper designs a block-chain-based system architecture for secure sharing of electronic documents. An efficient Blockchain model is used in our framework, and with the help of distributed storage system and asymmetric encryption technology, file sharing can be controlled, reliable and traceable in the transmission process.Referring to existing consensus mechanism, e.g., Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT), we propose a new consensus for efficient and secure file sharing.
BLOCKCHAIN BASED SECURE FILE STORAGE AND SHARING USING DECENTRALIZED APPROACH

These days, instead of using local storage devices, cloud storage is used to store and retrieve data.Cloud storage is based on the internet and provides data that is more dependable, safe, and readily available.Nonetheless, the information is crucial and shouldn't be shared with anyone not authorised.On the cloud, there is a lot of data that needs to be secured against unauthorised access.A variety of algorithms are employed to protect the security and privacy of data.Every system aims to achieve availability, confidentiality, and integrity (CIA).Nevertheless, these CIA features are not offered by the current centralised cloud storage.Decentralized cloud storage and blockchain technology are thus utilised to increase the security of data and storing methods.It efficiently aids in preventing data from being altered or having a portion of it deleted.A chain of blocks connects the data contained in blockchain to one another.Each block has a hash value that is saved in the following block.Consequently, it lessens the likelihood of data alteration.The SHA-3 Hashing algorithm is employed for this.
Investigation on Sentiment Analysis in Big Data using Machine Learning

With the advent of Web 2.0, users have developed a growing interest in sharing their content, which has been further amplified by the emergence of various social networking sites. These platforms have provided an excellent opportunity for users to share their opinions with people all over the world. The opinions expressed by customers on the internet can have a significant impact on the service industry. As a result, various industries such as educational institutions, researchers, and business organizations are focusing on opinion mining, also known as sentiment analysis (SA), to extract the views and opinions posted by the public. The primary objective of this study is to provide a comprehensive overview of sentiment analysis using machine learning approaches. Furthermore, this paper sheds light on the significant challenges faced by SA, which present a vast scope for future research.
Underwater Image Enhancement Using MIRNet

In recent years, enhancement of underwater images is a challenging task, which is gaining priority since the human eye cannot perceive images under water. The significant details underwater are not clearly captured using the conventional image acquisition techniques, and also they are expensive. Hence, the quality of the image processing algorithms can be enhanced in the absence of costly and reliable acquisition techniques. Traditional algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color deviation. In the proposed model, the authors used a deep learning model for underwater image enhancement. First, the original image is pre-processed by the white balance algorithm for colour correction and the contrast of the image is improved using the contrast enhancement technique. Next, the pre-processed image is given to the MIRNet for enhancement. MIRNet is a deep learning framework that can be used to enhance the low-light level images. The enhanced image quality is measured using peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity index (SSIM) parameters.
Hebbian learning can explain rhythmic neural entrainment to statistical regularities

In many domains, learners extract recurring units from continuous sequences. For example, fluent speech is perceived as a continuous signal (at least in unknown languages). Learners need to extract the underlying words from this continuous signal and then memorize them. One prominent candidate mechanism is statistical learning, whereby learners track how predictive syl- lables (or other items) are of one another. Syllables within the same word are more predictive of one another than syllables straddling word boundaries. However, it is controversial whether such statistical learning abilities lead to memories of the underlying words or just to pairwise associations among syllables. The strongest evidence for the former possibility comes from evoked electrophysiological responses time-locked to word boundaries (e.g., N400’s) and rhythmic neural activity with a periodicity of word durations. Here, I reproduce such results with a simple Hebbian network. When exposed to statistically structured syllable sequences (and when the underlying words are not excessively long), the network activation is rhythmic with a periodicity corresponding to a word duration and an activation maximum on word-final syllables. This is because word-final syllables receive more excitatory input from earlier syllables with which they are associated than syllables that occur earlier in words (and are less predictable). The network also reproduces results that were interpreted as encoding ordinal positions within words. Hebbian learning can thus explain rhythmic neural activity in statistical learning tasks without any memory representations of words. Learners might thus need to rely on cues beyond statistical associations to learn the words of their native language.
Issue detection and prioritization based on app reviews

Abstract Opinion mining for app reviews aims to analyze people's comments on app stores to support software engineering activities, particularly software maintenance and evolution. One of the main challenges for software quality maintenance is promptly identifying emerging issues, e.g., bugs. However, due to a large amount of textual data, manually analyzing these comments is challenging, and machine learning-based methods have been used to automate opinion mining. Our objective is to minimize the time between the occurrence of the issue and its correction, providing means for the quick identification of the issue through prioritization levels. This paper introduces the automatic generation of a risk matrix from user reviews to answer the following research question: how do we prioritize and treat reviews in time so that the app is competitive and guarantees the timely maintenance and evolution of the software? We present the MApp-IDEA (Monitoring App for Issue Detection and Prioritization) method to detect issues and classify the reviews in a risk matrix with prioritization levels. We present an approach that (i) automatically collects reviews, (ii) detects issues, (iii) classifies reviews in a risk matrix, and then (iv) models the temporal dynamics of issues and risks through time series to trigger alerts. The results showed that our unsupervised issue detection approach is competitive compared to state-of-the-art supervised methods. We performed an empirical evaluation with 50 mobile applications (apps) and processed approximately 5 million reviews, where we detected 230,000 issues and classified them into priority levels using a risk matrix. We have shown that opinions extracted from user comments provide important information about the app's issues and risks. In evaluation, we found that issues detected early with our approach are associated with later fix releases by developers.