Reviewerly/exHarmony huggingface dataset:
Biased Online Media Analysis Using Machine Learning

Nowadays, media is widespread, whether social media or news TV channels, and has great manipulative powers. They can make the masses go for or against a particular topic, person or scenario and prove to be the significant element in moulding the mind-set of people. There are news sources that are very clear and expressive regarding their news content. Still, some can be paid to bring biased reports against or favour some party, organization, culture or situation. This paper’s primary purpose is to determine whether a report brought to the front by the media channels can be biased or not depending on the previous data records and status. After going through this research paper, the reader can get a basic idea of what has been done to determine whether a news report is biased or fair. The inspiration for this work was derived from the fact that today’s media is being adulterated with the increment in dirty politics and the likeliness or preference of one political party over the other. Much of the inspiration was also drawn from the literature survey. We faced many challenges in determining and designing the machine learning algorithm model and an interactive GUI for ease of use. Finally, in this research, the XGBoost classifier achieved the highest accuracy of about 65.6% among all the proposed classification models, which is more remarkable.
Video Anomaly Classification Using DenseNet Feature Extractor

The proposed research study emphasises the idea of recognising abnormalities in surveillance videos, which are by far the most widely used source of anomaly data. The suggested methodology includes the development of an algorithm capable of detecting anomalies with better accuracy and precision than previously developed methodologies. The model has gone through stages of development. The development phases comprise extracting features from surveillance videos using pre-trained convolutional neural network (CNN) architecture, i.e. DenseNet121, from the transfer learning technique and training the model with appropriate hyperparameters to categorise the anomalies. We also used labels that are labelled at the frame level to classify the video. The model was trained, and hyperparameters were adjusted until the best results were obtained. The Area Under the ROC Curve (AUC) score obtained on the UCF crime dataset was 82.91%, which is better than many previously published state-of-art results. We also contrasted the AUC score data for several deep learning methodologies proposed by other researchers to demonstrate the competitiveness and efficiency of our methodology.
Anomalous Human Activity Detection Using Stick Figure and Deep Learning Model

The population of the world is estimated to reach 8 billion by the year 2024. With the increase in human population, the crime rate keeps increasing rapidly. Law enforcers cannot be present in every corner of the world to monitor and avert crimes. But with the help of advanced computer vision techniques, advanced surveillance systems can be developed where the pose of a human is estimated to predict the action of the individual: normal or suspicious. This paper focuses on finding the key points on the human body and to develop and train a model using deep neural network which takes the key points as input and outputs the pose of the individual. This greatly reduces human surveillance and provides precise and accurate surveillance in every place required.
Rwandan Pay-As-You-Go Solar Home System User Payment Behavioural Types

The pay-as-you-go (PAYGo) model is now the principal way through which solar home systems (SHSs) are distributed in Sub-Saharan Africa. By alleviating the upfront cost and providing flexible payment schemes, the PAYGo model helps tackle what is still the main barrier for SHS adoption-i.e., affordability. However, the scheme’s design and evaluation are still largely guided by assumptions on user behaviour. This work provides a first evidence-based look into SHS PAYGo user payment patterns and behaviours, by using payment records of over 32,000 Rwandan SHS users. Three clustering algorithms are implemented to conduct a customer segmentation, employing an ensemble validation method which facilitates qualitative oversight. The analysis reveals five user payment behavioural profiles which serve to aid improvement in the current PAYGo model design.
Professional development of educators' online learning during the COVID-19 pandemic in the Republic of Croatia

Preschool educators work in kindergartens and provide care and education, upbringing, and social and health care of children from the first year of life until they enroll in primary school. During their work, preschool educators are obliged to improve their skills and develop professionally in order to improve the educational process. The COVID-19 pandemic has caused a number of limitations to educators, which in turn affect the quality of the professional development. Modern forms of online learning are available via Internet, digital devices, and various learning platforms. The paper investigates the conditions for educators' professional development in the online environment during the COVID-19 pandemic in the Republic of Croatia. The aim of the research was to determine the conditions and level of support for online professional learning of preschool educators. The research was conducted using a survey method and the questionnaire was used to assess satisfaction with the conditions of online professional learning of preschool educators and developed for the purpose for this research. The results of this research emphasize the need to examine the possibility of using digital technology and the Internet in online learning of preschool educators. The research improved the field of professional development of preschool educators.
Analysis of Procedural Problems in Cybercrime Investigations

The approach used to investigate cybercrime in developed nations is examined in this article, along with the innovations that might be implemented in Georgia to ensure successful investigations. Detailing the procedures outlined under the Budapest Convention on Computer Crimes, attention is given to the procedural issues that crop up while gathering and presenting electronic evidence in criminal investigations. The convention's individual articles are examined, including those that deal with information requests, and a comparison between the convention's legal provisions and the standards incorporated into Georgian law is made. The article examines the practice of collaboration between developed country law enforcement bodies and Internet service providers. The absence of computer data classification in Georgian law is highlighted, which results in the existence of a single, all-encompassing rule for all forms of electronic information. The paper also covers court rulings on the admissibility of electronic evidence and offers advice on whether it would be wise to clarify or improve the criminal procedure laws. The article explains the need for varied time limits and tactics for getting and maintaining information from internet service providers based on the classification of cybercrimes into distinct crime categories, such as serious, particularly serious, and crimes against national security.
Quantum internet

The Quantum Internet - i.e. an heterogeneous network enabling quantum communications among remote quantum nodes by leveraging on quantum transmission channels in synergy with classical transmission channels - is attracting worldwide academic and industrial interest [1, 2, 3, 4, 5, 6, 7], given its potential of enabling applications with no counterpart in the classical Internet [8]. Indeed, the Quantum Internet, by transmitting qubits and by distributing entangled quantum states, comes with a whole new dazzling functionalities [9, 10].
On Linear Complexity of Finite Sequences: Coding Theory and Applications to Cryptography

We define two metrics on vector spaces over a finite field using the linear complexity of finite sequences. We then develop coding theory notions for these metrics and study their properties. We give a Singleton-like bound as well as constructions of subspaces achieving this bound. We also provide an asymptotic Gilbert-Varshamov-like bound for random subspaces. We show how to reduce the problem of finding codewords with given Hamming weight into a problem of finding a vector of a given linear complexity. This implies that our new metric can be used for cryptography in a similar way to what is currently done in the code-based setting.
Learning Entity Linking Features for Emerging Entities

Entity linking (EL) is the process of linking entity mentions appearing in text with their corresponding entities in a knowledge base. EL features of entities (e.g., prior probability, relatedness score, and entity embedding) are usually estimated based on Wikipedia. However, for newly emerging entities (EEs) which have just been discovered in news, they may still not be included in Wikipedia yet. As a consequence, it is unable to obtain required EL features for those EEs from Wikipedia and EL models will always fail to link ambiguous mentions with those EEs correctly as the absence of their EL features. To deal with this problem, in this paper we focus on a new task of learning EL features for emerging entities in a general way. We propose a novel approach called STAMO to learn high-quality EL features for EEs automatically, which needs just a small number of labeled documents for each EE collected from the Web, as it could further leverage the knowledge hidden in the unlabeled data. STAMO is mainly based on self-training, which makes it flexibly integrated with any EL feature or EL model, but also makes it easily suffer from the error reinforcement problem caused by the mislabeled data. Instead of some common self-training strategies that try to throw the mislabeled data away explicitly, we regard self-training as a multiple optimization process with respect to the EL features of EEs, and propose both intra-slot and inter-slot optimizations to alleviate the error reinforcement problem implicitly. We construct two EL datasets involving selected EEs to evaluate the quality of obtained EL features for EEs, and the experimental results show that our approach significantly outperforms other baseline methods of learning EL features.
Talk to Your Data: a Chatbot System for Multidimensional Datasets

Finding information may be a complex task for end users, either due to the format in which data is stored, or difficulty in formulating a query that fits the database structure. Conversational interfaces, such as chatbots, can minimize this issue, by facilitating query formulation through natural language. Despite the several applications of chatbots for data querying, the multidimensional aspect of data is rarely addressed in the literature, making the search for information even more challenging. Chatbots can be used for allowing the user to “talk to the data” by adding metrics and dimensions to a query, without relying on technical expertise. Thus, this paper presents a chatbot approach for querying multidimensional data, which captures users intentions and links them to the multidimensional metadata. This linking process allows the bot to set query parameters, using them for accessing the data. The chatbot was implemented for querying an open database containing about 2.5 billions records and over 1700 attributes (including dimensions and metrics), and it was evaluated through an empirical user study involving a group of participants performing a set of search tasks. The evaluation results supported the usefulness of the proposed approach in querying multidimensional data and retrieving information.
Make It Manageable: Version Control and Build Management

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
Learners’ Perceptions and Needs Analysis of Global Citizenship Education: Focused on Korea High School English Education

This study aims to identify the learners’ perceptions and needs analysis for Global Citizenship Education (GCED) in high school English education. To achieve the purpose of the study, an online survey was conducted on 369 students attending 10 general high schools across the country. First, most respondents shared the value and necessity of GCED as an education, but practical education related to GCED was rarely dealt with in the school fields. Also, it was very limited in the case of the school level or the subject where GCED was practiced, and in particular, the response to the high school English education could not be confirmed. Second, most students expected their needs to be reflected in the composition of the high school English curriculum and the production of English textbooks. In addition, there was a clear desire from students to reflect GCED-related contents and materials in the composition of the high school English curriculum and English textbook production. These results will not only help teachers and educators to understand how much high school learners are aware of GCED in English education and what they require but also suggest the direction of the high school English curriculum operation.
Adversarial attacks for machine learning denoisers and how to resisit them

Adversarial attacks rely on the instability phenomenon appearing in general for all inverse problems, e.g., image classification and reconstruction, independently of the computational scheme or method used to solve the problem. We mathematically prove and empirically show that machine learning denoisers (MLD) are not excluded. That is to prove the existence of adversarial attacks given by noise patterns making the MLD run into instability, i.e., the MLD increases the noise instead of decreasing it. We further demonstrate that adversarial retraining or classic filtering do not provide an exit strategy for this dilemma. Instead, we show that adversarial attacks can be inferred by polynomial regression. Removing the underlying inferred polynomial distribution from the total noise distribution delivers an efficient technique yielding robust MLDs that make consistent computer vision tasks such as image segmentation or classification more reliable.
Wavelength tunable resonant phase-change synaptic weights for photonic neuromorphic computing

The prohibitively large inclusions of micro-ring resonators, interconnected waveguide crossbar arrays, and multi-port multi-mode interferometers components demonstrated in the latest integrated photonic neural network hardware accelerators, pose a significant scaling issue for implementing large number of neural connections required to accurately represent the cognitive functions of a biological brain. In this investigation we utilize phase-change chalcogenide material GST to replicate a non-volatile synaptic weight with built-in memory functionality by employing metamaterial design principles for wavelength-division multiplexing photonic architectures. The transmission response of the optimized GST metamaterial gives rise to contrast ratios of 6dB in both positive and negative weighting values.
Proposed Crowd Counting System and Social Distance Analyzer for Pandemic Situation

In this paper, human detection and social distance analysis are discussed for both congenital and acquired diseases. Mitigating such pandemics can be done by maintaining social distance and avoiding overcrowding. In such conditions, self-taken measures like hand washing, environmental cleaning, facemask use, and avoiding overcrowding, and maintaining social distance are effective. It is suggested that by following these small steps, the spread of the virus could be minimized. This study uses OpenCV for both object detection and object tracking. Additionally, it is used to calculate social distance between people. An object tracking algorithm is used to identify people's presence movement detection through PIR sensor, whereas the human face detection is tracked by camera module. The standard distance for calculating distance is six feet because it is the distance needed to maintain social distance. Models were trained and then used for both object detection and object tracking, with the most popular one being OpenCV. A custom dataset was created for training this model, and it was used to understand and identify the human face. In addition to detecting and tracking people accurately, the developed model can be applied in other areas of research where humans are the primary focus, for instance, self-driving cars, crowd analysis, or any other type of research that prioritizes human detection. A significant challenge is that people behave in unpredictable ways during gatherings, so the proposed system might prove powerful in dealing with people's unpredictable behavior. In the present study, we evaluate if the distance between people in halls is maintained and estimate the number of people gathering in those halls.
Sketch-Based Face Recognition

Automatic facial recognition has attracted huge attention in recent years owing to rising demands in applications like law enforcement, video surveillance, banking, and security systems. People have a false opinion that only images reflect a person's face rather than sketches. In a complex environment like poor illumination, varying pose, and expressions, sketch-based recognition outperforms image-based recognition. This paper proposes a sketch-based face recognition (SBFR) method using a modified Mask RCNN framework. The proposed model includes recognition of a person even if there is a change in illumination, age, and facial expression. The method is validated with our local dataset “POPSKETCH,” and it comprises hand-drawn sketches of 28 individuals and converted sketches from original images of 34 individuals. The proposed Mask RCNN method achieves a better recognition rate of 90.67 which is higher than face recognition using the AlexNet network, YoloV5, and Mask RCNN network model.
The effects of using a smartphone application to learn English sentences on industrial college students.

This study examines the effects of using a smartphone application(App) for English learning on industrial college students. It focuses on the learning of English sentences by participants who are lacking practical communication skills. 42 participants used the App to learn sentences taught in an English elective course over a semester. They took the conversation class first and learned key sentences from the class using the App every week during the semester. When the course was finished, this study conducted a post-semester survey to examine the effects of using the App. As a result, first, this study found that participants had the attitudes of being repetitive and steady, self-directed, and willing to use spare time for learning English. Second, this study found that the App helped to improve participants’ motivation, confidence, and interests. The results also showed that the App helped students with their English reading, listening, vocabulary and expressions, understanding of sentences, and English conversation skills well. Overall, this study confirms that using an App satisfies the needs of industrial college students who want to improve their practical communication skills. Finally, this study provides some suggestions on how to improve English communication skills for industrial college students.
Effectiveness of Vertically Mounted Solar Panels on Buildings - A Case Study of Radisson Blu Hotel, Nairobi

Solar energy is a renewable energy source that can help reduce a hotel’s energy demand. At present most Kenyan hotels depend on petroleum products as their main energy source. The concept of wall-mounted solar panels is still quite new. This research seeks to study the effectiveness of vertically mounted solar panels on hotel buildings (Radisson Blu Hotel, Nairobi Upperhill) based on economic and technical benefits. It discusses the results of an experimental study based on simulation and actual site measurements using proposed tools and software. Technical details of the solar PV trainer will be used for this study. The concept and results of this study can be applied to other buildings in general.
Modelling Photovoltaic power output using Machine Learning techniques

This work focuses on modelling the power output of multiple PV technologies installed at the outdoor test facility of the Council for Scientific and Industrial Research (CSIR) in Pretoria. Extreme Gradient Boosting (XGB) and Artificial Neural Network (ANN) machine learning models are trained with historic time-series datasets (measured meteorological and PV electrical parameters) to model the historical output power of the photovoltaic (PV) system. To facilitate the training, sub-hourly measured data from January to November 2019 were averaged at hourly intervals. For testing, sub-hourly data from January 2020 to March 2020 was divided into clear, moderate, and cloudy skies monthly records. Outliers were identified and removed from the data during pre-processing. The short circuit current (Isc) and PV output have shown a very strong correlation (r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.93) since PV output is heavily influenced by array irradiance and current generation. PV output strongly correlated with plane of array irradiance and albedo (r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.83,0.69), and with module temperature (r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.70), depending on the module type. To quantify model accuracy, root mean squared error (RMSE) was used. ANN outperforms XGB by a wide margin based on the RMSE values. ANN produced the lowest RMSE values with 4. 1W to XGB record high 17. 5W during moderate skies. The majority of the observed maximum RMSE values came from XGB modelling. The trained models will be used to predict PV output power using only forecasted weather data as inputs in future work.
The Nexus between Smart Mobility Digitization and Carbon Neutrality through Blockchain Technology

The digitalization of mobility users will transform them from a B2C to a B2B demographic through leveraging their digitized profiles. This digital transformation of their (mobility consumers) data ensures necessary infrastructure, interfaces and eco-systems are built and maintained in an efficient and sustainable way. The nexus of smart mobility digitalization is essential in realizing sustained growth opportunities in both businesses and personal lives, this is further advanced by blockchain technology that enables peer-to-peer trade of goods and services.