Reviewerly/exHarmony huggingface dataset:
Searching for an assessment method for the 21st century. Student board game design and rubrics

Within the booming context of teaching learners the so-called 21st-century skills, tutoring teachers in novel teaching methods, approaches, and evaluation strategies, coupled with the massive trending use of games and gamification tactics, merge a necessity for teachers to be able to join together a series of requirements if they want to keep up-to-date as a part of the 21st century educational curriculum.&#x0D; In the quest to find activities that motivate English language students at the Language Center in the Autonomous Metropolitan University at Iztapalapa and Azcapotzalco campuses, it was decided to use a game-oriented activity. In many cases, a game-based approach has been used as a pedagogical activity that transmits knowledge; however, the possibility to create knowledge and to use games as evaluation tools has been set aside. Having analyzed learners’ perspective on the overall activity, it was decided that the assessment strategy needed deeper examination so that an implementation model could be proposed. It was considered that the rubrics used had the opportunity to be enriched. Thus, the types of rubrics and their characteristics were further investigated so that the rubrics initially used could be re-elaborated and improved. After the theoretical research, it was concluded that even though rubrics pose a positive assessment tool, they do need thorough planning and evaluation.
GRAPHICAL IMAGE PASSWORD AUTHENTICATION USING JARO DISTANCE

This research proposes a new authentication mechanism called Graphical Image Password Authentication Using Jaro Distance. A user can register five different related photographs using the suggested technique. To identify the similarity of the five pictures, a Gaussian mixture and the Jaro Distance method are used to preprocess the provided five similar images. All five photographs will be saved in the System database if they are comparable. The user connects in using their permitted information and a picture password based on a drawing. The password is processed by using a Gaussian mixture and implemented using the Jaro Distance technique to compare with the registered passwords in the database for authentication. The suggested method is tested with some example input image passwords, and the performance is evaluated using recall and precision measures. By ensuring authentication for user security, the proposed effort might achieve an accuracy of over 87%.
Multi-class Classification of Twitter Sentiments using Frequency based, LSTM and BERT Methods

Abstract: Twitter is one of the most popular social media applications used by people of diverse age groups. Tweet has become an important mode of communication. Tweets are unstructured and often consist of different kinds of data tokens like mentions, hash tags, emoticons etc. Analysing the sentiment of this kind of unstructured data is challenging. In this paper, the experimentation of multiclass twitter sentiment analysis is presented, using three different approaches. Starting with the simple approach of frequency based feature extraction method, that helps capture the features from a text document by counting the frequency of the occurrence of words, to more advanced approaches like LSTM and BERT have been used. Different preprocessing techniques and different classifiers are explored in this paper. The performance of all the three methods is evaluated using metrics (precision, recall and f1 score). At the end the results of all three approaches are discussed and compared against one another. Keywords: NLP, BERT, LSTM, MLP Classifier, Multiclass Classification, Embeddings, Tweets.
Teaching styles in relation to 21st century learning and innovation skills of students

The twenty-first century is focused on knowledge creation and application, and educational reforms must be geared toward fostering an inquiry culture.As a result, teachers must shift from their conventional position as information transmitters to facilitators and orchestrators of learning.This descriptive correlation study aimed to determine the extent of teaching styles, as it contributes to students' level of 21st-century learning and innovation skills.The result revealed a moderately high positive correlation between the relationship on the extent of implementation of teaching styles in relation to 21st-century learning and innovation skills.Thus, the extent of teaching styles was observed most of the time.The level of 21st-century learning and innovation skills is also evident most of the time.This lead to a significant relationship between the extent of implementation of teaching styles in relation to the level of 21st-century learning and innovation skills.
Validasi Dokumen Pengajuan Penelitian dan Pengabdian Masyarakat Universitas Nasional Menggunakan Metode Finite State Automata

Non-Deterministic Finite Automata (NFA), a form of Finite State Automata (FSA), is one method for validating research proposal documents in R&amp;D and community service activities at national universities. To carry out these activities, it is necessary to monitor the research proposal documents submitted by lecturers/researchers, one of which is to validate the research proposal document, ensuring that it follows the guidelines and avoids plagiarism, as well as the theme's suitability with the research proposal document's contents. Members of the research team can only participate in one type of study: stimulation or competitive research. Reviewers can provide research members with notes and plagiarism information, as well as refuse to submit research proposals. If the research proposal document is approved by the reviewer, the research member will receive a research certificate and will be able to continue their research with full support from the national government.
Harmonic oscillator kicked by spin measurements: A Floquet-like system without a classical analog

We present a kicked harmonic oscillator where the impulsive driving is provided by stroboscopic measurements on an ancillary degree of freedom and not by the canonical quantization of a time-dependent Hamiltonian. The ancila is dynamically entangled with the oscillator position, while the background Hamiltonian remains static. The dynamics of this system is determined in closed analytical form, allowing for the evaluation of a properly defined Loschmidt echo, ensemble averages, and phase-space portraits. As in the case of standard Floquet systems we observe regimes with crystalline and quasicrystalline structures in phase space, resonances, and evidences of chaotic behavior, however, not originating from any classically chaotic system.
A survey: research on firework algorithms

The firework algorithm is a new type of swarm intelligence algorithm, which is inspired by the process of fireworks exploding to produce sparks and continuously split and explode. This algorithm has a strong ability to solve complex optimization problems and is widely used in many practical problems. This article focuses on summarizing the current research progress of the firework algorithm and its improved algorithm, as well as prospects for its future research directions.
Machine Learning System for Malicious Website Detection: A Literature Review

Abstract: Today the most important concern in the field of cyber security is finding the serious problems that make loss in secure information. It is mainly due to malicious URLs. Malicious URLs are generated daily. This URLs are having a short life span. Various techniques are used by researchers for detecting such threats in a timely manner. Blacklist method is famous among them. Researchers uses this blacklist method for easily identifying the harmful URLs. They are very simple and easy method. Due to their simplicity they are used as a traditional method for detecting such URLs. But this method suffers from many problems. The lack of ability in detecting newly generated malicious URLs is one of the main drawbacks of Blacklist method. Heuristic approach is also used for identifying some common attacks. It is an advanced technique of Blacklist method. But this method cannot be used for all type of attacks. So this method is used very shortly. For a good experience, the researchers introduce machine learning techniques. Machine Learning techniques go through several phases and detect the malicious URLs in an accurate manner. This method also gives the details about the false positive rate. This review paper studies the different phases such as feature extraction phase and feature representation phase of machine learning techniques for detecting malicious URLs. Different machine learning algorithms used for such detection is also discuss in this paper. And also gives a better understanding about the advantage of using machine learning over other techniques for detecting malicious URLs and problems it suffers. Keywords: Blacklist, Cyber Security, Malicious URL
Optical Measurement and Performance Prediction of Solar PV System in Al-Khidhir Zone/Iraq

People have tended for investing the sources of renewable energy, especially solar energy, as they are clean and environmentally friendly sources.The present work aims to invest the measured data of solar irradiance, maximum ambient temperatures and wind speed provided by Al Khidhir Meteorological Station to predict the minimum performance of photovoltaic solar systems (NT-R0E3E-SHARP) for electric power generation.The single diode model is used to calculate the minimum output power produced by solar module and its minimum conversion efficiency based on soalr irradiance, maximum ambient temperatures and wind speed throughout 2020.The results indicate that the annual solar irradiance in Al-Khidhir zone on horizontal plane is 2084 kWh/m2/year and the daily averages , approximately, range from 2500 Wh/m2/day in January, as a minimum value, to 8000 Wh/m2/day in July as a maximum value.Accordingly, the monthly average of peak sun hours has its minimum values in winter (3hours in January) and increases toward the summer and reaches its maximum value in July (8 hours), while the annually average is 5.7 hours.The results show that the behavior of predicted rates of output power of solar PV module was conforming to the behavior of solar irradiance along year.The daily averages of output power, approximately, range from 60 W to 100W along year.Due to the thermal effect of solar radiation the degradation percentage in electrical conversion efficiency of mono crystalline silicon solar cell ranges from 7.6% in winter to 24% in summer.From the results of the present work, it can be concluded that the geographical zone of Al-Khidhir and its surroundings are promising for investing solar energy to produce electricity.
Learning Proposal for Cybersecurity for Industrial Control Systems Based on Problems and Established by a 4.0 Didactic Advanced-Manufacturing-Plant

Researches data indicate that the search for cybersecurity professionals to protect industrial control systems (ICS) in Brazil is increasing, mainly because of the rise in cyber-attacks directed at the industry. However, we observed a deficiency of professionals with the required competence in ICS cybersecurity, which involves technology-information fields (IT) and operational technology (OT). On the other hand, there is a lack of educational institutions with the right strategies for training professionals who master the technologies for ICS protection. This paper presents a strategy through procedures to address this lack by evaluating scenarios of practices for the development of competencies in ICS cybersecurity through the problem-based learning (PBL) methodology. The scenarios combine theory and practice involved in solving ICS cybersecurity problems, using PBL with the support of SENAI CIMATEC's 4.0 Advanced Manufacturing Plant (AMP).
Hawk Eye Unmanned Aerial Vehicle for Monitoring

Abstract: Quadcopters are drones also known as unmannedaerial vehicles that can be controlled remotely. Quadcoptershave uses in various fields. The Components required in building the quadcopter are its frame, Pixhawk flight controller, brushless DC motors, propellers, High Definition (HD) camera, Global Positioning System (GPS), telemetry module. Aerial security means performing security-aimed monitoring and surveillance operations with the help of airborne vehicles. These kinds of activities suggest that human officers (security organizations, law enforcement, police etc.) would be able to remotely monitor and view video and data acquired from Drones while planning and evaluating their operations. The spectrum of applications where drones are used for Security purposes include scouting and reporting emergencies, monitoring accidents and crimes, surveillance of a certain landscape area, operating in highly busy and pedestrians as well as their tracking from up in the sky, and so on. The project will serve as a bridge to connect actual happening in areasthat cannot be navigated easily by security personnel of corporate institution as the Drone will be used to hover and record the actual happening as it transmit to a ground station which records and analyses the events as they stream in, Also, due its capability of flying over different altitudes the drone can generally be used on areas with rugged terrains or over water bodies for a time dependent on its power capacity. Keywords: drone, flood, Pixhawk, quadcopter, rescue,surveillance, image processing.
Machine Learning System for Malicious Website Detection using Concept Drift Detection

Abstract: The rampant increase in the number of available cyber attack vectors and the frequency of cyber attacks necessitates the implementation of robust cybersecuritysystems. Malicious websites are a significant threat to cybersecurity. Miscreants and hackers use malicious websites for illegal activities such as disrupting the functioning of the systems by implanting malware, gaining unauthorized access to systems, or illegally collecting personal information. We propose and implement an approach for classifying malicious and benign websites given their Uniform Resource Locator(URL) as input. Using the URL provided by the user, we collect Lexical, Host-Based, and Content-Based features for the website. These features are fed into a supervised Machine Learning algorithm as input that classifies the URL as malicious or benign. The models are trained on a dataset consisting of multiple malicious and benign URLs. We have evaluated the accuracy of classification for Random forests, Gradient Boosted Decision Trees and Deep Neural Network classifiers. One loophole in the use of Machine learning for detection is the availability of the same training data to the attackers. This data is exploited by the miscreants to alter the features associated with the Malicious URLs, which will be classified as benign by the supervised learning algorithms. Further, owing to the dynamic nature of the malicious websites, we also propose a paradigm for detecting and countering these manually induced concept drifts. IndexTerms—URL Feature Extraction, Malicious Website Detection, Concept Drifts, Feature Vectors, Gradient Boosted Trees, Random Forest, Feedforward Neural Networks Keywords: (cyber attack, URL, Supervised machine learning, DNN, extraction)
Adults regularize variation when linguistic cues suggest low input reliability

Children regularize inconsistent probabilistic patterns in linguistic input, yet they also acquire and match probabilistic sociolinguistic variation. What factors in the language input contribute to whether children will regularize or match the probabilistic patterns they are exposed to? Here, we test the hypothesis that low input reliability facilitates regularization. As a first step, we asked adult participants to acquire a variable plural marking pattern from a written (Exp 1) and a spoken (Exp 2) artificial language under different conditions, where they were led to believe input was more, or less, reliable. In both experiments, input reliability was manipulated through both information about the speaker (e.g., whether the speaker was likely to make mistakes) and linguistic cues (e.g., typos or pronunciation errors). Results showed that adults regularized the written language more only when they were told the speaker would make mistakes and the plural variants resembled typos (Exp 1), whereas they regularized the spoken language more when the plural variants resembled pronunciation errors regardless of the speaker’s said reliability in the spoken language. We conclude that input reliability is an important factor that can modulate learners’ regularization of probabilistic linguistic input, and that linguistic cues may play a more critical role than top-down knowledge about the speaker. The current study lays down an important foundation for future work exploring whether children are able to incorporate input reliability cues when learning probabilistic linguistic variation.
Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)

Few-shot relation learning refers to infer facts for relations with a few observed triples. Existing metric-learning methods mostly neglect entity interactions within and between triples. In this paper, we explore this kind of fine-grained semantic meaning and propose our model TransAM. Specifically, we serialize reference entities and query entities into sequence and apply transformer structure with local-global attention to capture intra- and inter-triple entity interactions. Experiments on two public datasets with 1-shot setting prove the effectiveness of TransAM.
Leasing using Full Stack Web Application

The creation of this web application arises from the need to facilitate and optimize the time spent in the entire management of rental apartments. It is becoming more and more common to acquire and rent properties. The administration of these can become a tangle of invoices, papers, appointments, and payments that worsens over time. The solutions that we can find today to this problem are basically two: delegate the process to a third party, which is an extra expense that many people are not willing to pay, or look for one of the few software that the market offers which are very complex and do not allow classifying and dividing the information of each apartment in a simple way and therefore do not solve the basis of the problem: the organization. So, why is this project innovative? The main feature of this application is the possibility of computerizing the entire process involved in leasing one or more assets and being able to manage them yourself without depending on a third party. You don't need hundreds of fields of information or calculators of net and gross profit as the few software’s that exist in the market have. What inexperienced people who rent assets need is to be able to have their files, expenses controlled, and basic information organized. In summary, you will be able to save and classify the information of each of the leases by months, upload files, add notes, keep track of the expenses of each month along with the amount paid and owed, interact with your colleagues through general and specific forums for each asset, etc. and thus be able to access it when and where you need it in a fast and intuitive way. Keywords: Full stack web application, Dot Net, Lease management
Hierarchical, Discontinuous Agent Reinforcement Learning Rewards in Complex Military-Oriented Environments

Artificially intelligent agents are seeing increased adoption in both the video game and simulation industry for training, education, and entertainment purposes. These systems often need realistic and believable opponents that must achieve objectives in the face of competing and contradictory priorities and frequently require the rapid creation of a wide spectrum of agents with disparate behaviors that reflect tactical realism. This in turn drives the need for the dynamic training of such agents from available source data. Approaches to do so have yet to be widely investigated due to the smaller scales of these simulation environments. This paper discusses techniques to quickly design and generate a variety of AI agents that follow desired tactics and procedures, including realistic situations that require trade-off decisions between competing objectives. Techniques described include an investigation into deep reinforcement agents that have separable reward structures and can prioritize and re-prioritize goals based on a hierarchy.
An Improved Stacked Auto Encoder based Data Compression Technique for WSNs

Abstract: Because of the limited energy available to sensor nodes in wireless sensor networks (WSNs), data compression is critical in these networks. The majority of the time, data communication results in energy consumption; however, by minimizing data transmission and reception, the lifetime of sensor nodes may usually be extended significantly. To compress sensor data, we present a new Improved Stacked RBM Auto-Encoder model, which is built of two layers: an encode layer and a decode layer, which is described in detail in this work. Data from sensors is compressed and decompressed in the encode layer; data from sensors is reconstructed and compressed in the decode layer. The encode layer and the decode layer are both made up of four conventional Restricted Boltzmann Machines that are used throughout the system (RBMs). We also present an energy optimization strategy that, by trimming the parameters of the model, can further minimize the energy consumption of the model storage and calculation. We evaluate the model's performance by comparing it to the data acquired by Intel Lab in the environment. Assuming that the model's compression ratio is 10, the average Percentage RMS Difference value is 9.84 percent, and the average temperature reconstruction error value is 0.312 degrees Celsius. It is possible to minimize the energy consumption of node communication in WSNs by 92 percent. When compared to the traditional method, the proposed model achieves higher compression efficiency and reconstruction accuracy while maintaining the same compression ratio as the old method. The results of our experiments demonstrate that the new neural network model can not only be applied to data compression for WSNs, but it also has high compression efficiency and an excellent transfer learning capability. Keywords: Data Compression; Stacked-Autocoder; transfer learning; energy, consumption optimization
A COMPARATIVE KEYWORDS ANALYSIS IN PET CAFÉ AND REGULAR CAFÉ REVIEWS: A CORPUS STUDY

This paper aims to investigate the most frequently used keywords in pet’s café and regular cafe to identify the linguistics discrepancies between the reviews in the two cafes. A self- created corpora of pet’s café and regular café’s reviews were collected from TripAdvisor and google map and used as the main data to compare the linguistic features in each corpus. The analysis was conducted by using AntConc 3.5.9 for Windows (64-bit) to compare the keywords and concordance lines of notable keywords. A comparison of the keyword analysis indicates that the big discrepancies between the two corpora is the use of second- and third-persons pronouns in pet café review and the use of the first-person pronouns in regular café review. This pronouns choice suggests that in pet café, the writers adopt customers and product- based review, whereas in regular café, the writer tend to focus their reviews on author-based review. Another notable finding is the absence of Wi-fi in pet café review and the absence of adverb and preposition in regular café. Despite the difference, the reviews in both cafés are heavily dominated by the use of verbs, nouns, and only small number of adjectives found in each corpus.