Reviewerly/exHarmony huggingface dataset:
Summarization of CCTV Videos using KNN Classifier

In this modern era, surveillance cameras are omnipresent. Be it home, office, malls, highway, CCTV cameras are found everywhere monitoring the day to day activities. Surveillance is an important task. They aid in detecting any fishy activity and act as valuable proof. However, the amount of video footage recorded is enormous. Most of the times the useful information contained in it is very less. It becomes a very difficult task to go through so many hours of footage to find the useful content. Storing the videos also takes a huge amount of space. Thus, a need for surveillance solutions is recognized that can condense such long footage into short videos while still retaining important events. Our model can summarize hours of footage shot by CCTV cameras into a short clip that shows all interesting events simultaneously. All moving objects that are present in the video are identified and tracked by our program. These events are then overlaid on a single clip, while also displaying the timestamps for each event, thus letting the user perform surveillance for multiple events. Our model uses KNN classifier for extracting and subtracting background from videos and a custom Object Tracking algorithm to detect moving objects, which is then overlapped on extracted background.
Enhanced Classifier Model for Detecting Spam Reviews Using Advanced Machine Learning Techniques

Now a days lot of people, uses e-commerce websites for purchasing the products. Before purchasing any product once, the reviews will be checked by the user. In this aspect, mainly we are focusing on the fake review detection using classification of those reviews by the polarity score index. Traditional Naive Bayes and its variants (Multinomial, Bernoulli model) are used in estimating the initial weights of the features in classifying the sentiments. The posterior knowledge of NB algorithm is used in finalizing the initial value of each term present in the reviews. Later, Gradient ascent technique is used to estimate the exact weights of the attributes towards improving the likelihood of the classifier. The use of Gradient ascent is finalizing the exact weight of each term towards improving the likelihood of text classifier. The proposed work has its advantages in estimating the intensity in sentiment present in review based on context available to determine fake or not using polarity score. The design of novel supervised text classifier mainly deals with extracting impactful features from text based on context. The outcome from the present work is made in use with context-based sentiment analysis. It avoids the use of dictionaries and updating the dictionary based on context and it save lots of time in processing hung number of reviews.
Optimization of weights in ELM for face recognition

Since last many decades, identification of the human beings on the basis of their face images is getting attraction in the domain of recognizing any pattern. In the proposed approach, a novel algorithm is utilized for the recognition of face images. Extreme learning machine (ELM), which is a non-iterative algorithm, is taken into consideration by computing their input weights with the help of genetic algorithm (GA). PCA is utilized here as the dimension reduction tool. Various face databases like ORL, Georgia Tech and YALE are tested over the proposed approach and it can be observed from the percentage error rates for all the databases that improvement through this proposed algorithm is achieved. The results are also compared with the standard extreme learning machine algorithm to show the improvement by the proposed approach compared to the standard ELM.
Lost in translation [version 2; referees: 2 approved]

Translation in cognitive neuroscience remains beyond the horizon, brought no closer by supposed major advances in our understanding of the brain. Unless our explanatory models descend to the individual level—a cardinal requirement for any intervention—their real-world applications will always be limited. Drawing on an analysis of the informational properties of the brain, here we argue that adequate individualisation needs models of far greater dimensionality than has been usual in the field. This necessity arises from the widely distributed causality of neural systems, a consequence of the fundamentally adaptive nature of their developmental and physiological mechanisms. We discuss how recent advances in high-performance computing, combined with collections of large-scale data, enable the high-dimensional modelling we argue is critical to successful translation, and urge its adoption if the ultimate goal of impact on the lives of patients is to be achieved.
의사결정나무를 활용한 실시간 온라인 한국어 강의에서의 학습자 만족도 분석 -상호작용 요인을 중심으로-

This study identified factors of interaction influencing the satisfaction of 74 foreign learners with real-time online Korean lectures using a survey design. The study developed two types of prediction models for satisfaction with learning effects and satisfaction with lectures using a decision tree algorithm, which produced the best accuracies of 75.0% and 87.5%, respectively. Four common influential factors affected the two types of prediction models: gathering feedback on assessments, providing appropriate time, identifying the learner’s learning status, and providing interesting materials. In addition, this study developed four rules for satisfaction with learning effects and six rules for satisfaction with lectures from the prediction models. Using the derived rules, this study proposed helpful directions for enhancing foreign learners’ satisfaction in real-time online Korean language lectures. (Korea University Korea University Gyeongsang National University)
Financial Openness and Income Inequality: Do Institutions Matter for Africa

There has been little empirical literature on the relationship between financial openness and income inequality in Africa. Using a panel threshold estimation technique we examine whether the effect of financial openness on income inequality is dependent to the institutional quality. We adopt the methodology proposed by Hansen (1999) on a panel dataset of 21 African countries over the period 1985-2014. Our results confirm the existence of a two-regime split. In fact, financial openness seems to significantly widen the income inequality in countries with low level of institutional quality while it does not have a significant effect in countries with relatively high institutional quality. These results concur with a well-established literature and demonstrate that financial openness-income inequality link is contingent on the quality of the political and institutional environment in the African countries.
Adaptive Strategies for Oscillatory Systems:Integrating Machine Learning and ControlTechniques with Applications in Neuroscience

Author(s): Matchen, Timothy Dandeneau | Advisor(s): Moehlis, Jeff | Abstract: Oscillators are ubiquitous in nature. As such, a significant body of literature has been devoted to studying their dynamics and how to control those dynamics. Many systems, however, do not maintain the core assumptions guiding the development of this literature-- systems that are either too complex or too poorly understood to allow for simple mathematical representations. Machine learning can serve as a powerful tool to supplement our understanding of dynamical systems in situations where traditional methods fail. In this dissertation, we develop first a control strategy for oscillators using standard techniques and assumptions about our dynamical system, then explore the ways in which machine learning can replace some of the strictest requirements on developing control strategies. We demonstrate how machine learning can extract meaningful information from complex systems in neuroscience and use that information as the basis for control. Lastly, we discuss some emerging strategies for further marrying the disciplines of dynamics and control and machine learning.
Fabrication of Polyvinyl Alcohol/Bentonite Nanocomposites Using Sol-Gel Method

This study aimed to find the characteristics of mechanical, morphological, and thermal properties of poly(vinyl alcohol) (PVA)/ bentonite nanocomposites. The method used in making nanocomposites is a sol-gel method. The natural bentonite (size of 35.26 nm) obtained from North Sumatra, Indonesia, was added to the solution of poly(vinyl alcohol) with the quantity varied, namely 0, 2, 4, 6, 8 wt %. The nanocomposites yielded were then characterized by using tensile test, scanning electron microscopy, and differential scanning calorimetry. The results showed that the maximum weight of added bentonite, which improved the mechanical properties and produced a more homogenous surface, was 6 wt %, whereas the variation of 2, 4, and 8 wt % of bentonite resulted in a decreased elasticity modulus. However, thermal properties are preferable to improve in the 0 wt % of bentonite.
Analysis of Electric Car Front Chassis In Crash Test Using Fea Software

One of the important components of electric cars is the chassis which is a component of the vehicle whose main function is to frame to withstand the burden of vehicles and impact loads during a collision so that it can protect passengers. The properties of the electric car chassis and body is simulated using software based on full-frontal fixed barrier method where collision or collision with wall rigid barrier or rigid wall is applied. In this model, the car is crashed to rigid wall with various speed namely 30 km/h, 45km/h, 60 km/h and 100 km/h. From model analysis and result it can be concluded that the model with the initial design in this study is still in poor condition in modeling, because it is not safe which still has a value of SF 1.
Explainable AI for Entertainment: Issues on Video on Demand Platforms

With the proliferation of Artificial Intelligence-based systems, several questions arise involving ethical principles. In addition, the human-centered approach takes the focus on the user experience with these systems and studies user needs. A growing issue is the relationship between the transparency of these systems and the trust of users, since most systems are considered black-boxes. In this scenario, the Explainable Artificial Intelligence (XAI) emerges, with the proposal to explain the rationale of the decision making of the algorithms. XAI then starts to gain space in systems that involve high risk, such as health. Our research aims to discuss the importance of transparency to improve the user experience with recommendation mechanisms for entertainment, such as Video on Demand (VoD) platforms. In addition, we intent to raise the adjacent consequences of including XAI, such as improving the control and trust of VoD platforms. For this, we conducted an exploratory research method named Directed Storytelling. The study was conducted with thirty-one participants, all users of VoD platforms, regardless of time and frequency of use of this kind of systems. We note that people understand that there is an automated mechanism making recommendations for content in a personalized way for them, based on their browsing history, but the rules are not explicit. Thus, many users are suspicious of being manipulated by the system’s recommendations and resort to external recommendations, such as tips from third parties or Internet searches through specialized channels.
Comparative analysis of MEAN stack and MERN stack

Current web applications are designed using a ‘stack’ of various technologies. A stack is a collection of frameworks and tools used to develop a software product. MEAN stack and MERN stack are two of the most popular and extremely powerful stacks used in web development. JavaScript provides an option to use both MEAN and MERN stack to develop web application by eliminating the need for switching the code. Both MEAN and MERN have a stellar combination of tools to help you build well-functioning software products. This ensures faster development of web applications and helps developers quickly get products to market as we only use a single language for frontend, backend rather than old ways of using different languages for frontend and backend and eventually leads to a huge cut in development costs and improves efficiency. Selection becomes intimidating for newcomers to pick the right stack for their applications. This paper aims at providing the various advantages and disadvantages of MEAN and MERN stacks, which enables the newcomers to pick appropriate right stack as applicable for developing a web application.
LibGuides: Early Childhood Reading Support: Books for Children

Here are a few books to excite early readers. Many of these were class favorites of my students.
The Role of Less Contact Technology in the Covid-19 Pandemic

Abstract. The Covid-19 pandemic requires people to change their behavior to interact with new normal conditions and with the concept of less contact economy, the trend of using technology will change towards optimizing the use of online technology or more widely known as digital transformation. Online and realtime remote practicum learning system that can facilitate students who are constrained by distance, facilities in schools that have inadequate practice equipment, and prevent direct contact with one another (Less Contact) so that learning outcomes in practicum can be fulfilled according to its competence during the Covid-19 pandemic. The concept of a Waste Bank based on Information Technology (using a Smartphone Application and based on IoT (internet of Things) can facilitate residents and prevent direct contact (Less Contact). The waste weighing device is placed into the homes of customers who are included in the BANK SAMPAH organization. every resident's house. If the plastic waste is in the position of transportation, the officer will carry out the transportation of the waste to be used as balance. Keywords: Less Contact, Covid-19.
Maximum cross section method in the filtering problem for continuous systems with Markovian switching

Abstract New solution algorithms of optimal filtering problem are proposed for systems with random structure and continuous time. This problem consists in estimating the current state of system based on the results of measurements. The mathematical model of the system includes nonlinear stochastic differential equations whose right-hand side determines the structure of the dynamic system or mode of operation. The right-hand side may vary at random time moments. The number of structures of the system is assumed to be finite and the process of changing the structure to be Markov or conditionally Markov. The state vector of such system consists of two components, namely, a vector with real coordinates and an integer structure number. The law of change of the structure number is determined by the distribution of the random time interval between switchings with a given intensity dependent on the state of system.
Hierarchical system mapping for large-scale fault-tolerant quantum computing

With respect to large-scale quantum computing, it is important to know precisely and quickly how much quantum computational resources are necessary to study components and the whole system. If such quantum resources are provided, how much performance a quantum computer provides is also a critical issue. Unfortunately, it is practically infeasible to deal with such problems with conventional methods based on a non-structured description about quantum algorithm. To overcome the problems, we propose a fast method by using a hierarchically structured description about quantum algorithm which is much more compact than the conventional method. During the process, the dedicated computing regions and their interconnection are dynamically mapped onto a structured quantum computing system architecture. In our study, the proposed method works very faster such as 1 h than 1500 days for Shor algorithm to factorize a 512-bit integer. Meanwhile, since the combination of structured code and architecture provides a high degree of locality, it requires less SWAP chains, and hence, it does not increase the quantum computation depth more than expected.
Гавриїл Музическу – легенда хорового мистецтва

Gavriil Musicescu is one of the best masters of choral art. The artist’s life andcreative career are closely connected with Southern Bessarabia, where his muse foundpure sources of the national history, Christian values and love of his native land and ofhis musical heritage.Gavriil Vakulovych was a highly trained musician, who not only skilfully arrangedRomanian folklore, but also penetrated and revealed the essence of its treasures.Perfectly combining the qualities of a profound researcher and an educationspecialist, a true teacher and an educator of youth, Gavriil Musicescu developed anexceptional creative activity in order to improve aesthetic values and music education.He managed to make a kind of artistic revolution in church music, for the first timeintroducing female voices into the choir.Musicescu had outstanding achievements in the field of training. GavriilVakulovych was strictly demanding and at the same time paternally kind. He generouslypassed on his scientific experience to the current generation of masters of choral art.Gavriil Musicescu managed to share his love of music, folklore and art with hisdaughters Sofiia and Floritsa and grandchildren Ionel and Oleskandru Teodorianu.In the history of Romanian choral art, Gavriil Musicescu will forever remain thebest conductor and leader of one of the largest choral ensembles «The MetropolitanChoir of Jassy».
Artificial Dummies for Urban Dataset Augmentation

Existing datasets for training pedestrian detectors in images suffer from limited appearance and pose variation. The most challenging scenarios are rarely included because they are too difficult to capture due to safety reasons, or they are very unlikely to happen. The strict safety requirements in assisted and autonomous driving applications call for an extra high detection accuracy also in these rare situations. Having the ability to generate people images in arbitrary poses, with arbitrary appearances and embedded in different background scenes with varying illumination and weather conditions, is a crucial component for the development and testing of such applications.The contributions of this paper are three-fold.First, we describe an augmentation method for the controlled synthesis of urban scenes containing people, thus producing rare or never-seen situations. This is achieved with a data generator (called DummyNet) with disentangled control of the pose, the appearance, and the target background scene.Second, the proposed generator relies on novel network architecture and associated loss that takes into account the segmentation of the foreground person and its composition into the background scene.Finally, we demonstrate that the data generated by our DummyNet improve the performance of several existing person detectors across various datasets as well as in challenging situations, such as night-time conditions, where only a limited amount of training data is available. In the setup with only day-time data available, we improve the night-time detector by 17% log-average miss rate over the detector trained with the day-time data only.
Mapping of Land Drought Potential in Cirebon Regency-West Java Based on Geographic Information System and Remote Sensing

Drought is a natural disaster characterized by unbalanced water conditions.Land damage caused by drought occurred in Java Island precisely in Cirebon Regency, West Java. The lack of map data containing information on potential drought-stricken areas also serves as one of the factors hindering the resolution of drought problems.The creation of drought potential maps in a region can be done using remote sensing techniques (indraja) and Geographic Information System (SIG). As for identifying drought using tenik remote sensing can use landsat 8 imagery with NDVI and TCT methods. GIS can analyze supporting data in the form of rainfall maps and administrative limits so that it can present a map of potential land droughts. The results of this study show that drought with dry grade only occurs in Gegesik and Kapetakan sub-districts. Cirebon regency has a normal drought level in November 2019. November is the end of the dry phase in Cirebon Regency in 2019.
Customers Review on Car Service using Natural Language Processing

Natural language (NLP) is a theory-motivated range of computational techniques for the automatic analysis andrepresentation of human language. In the era of Google, customers’ satisfaction is obtained online through ratingsand reviews. In this paper one such reviews of a leading car company LANSON TOYOTA in koyambedu, Chennaiis taken and analyzed using NLP. Text analysis is carefully carried out and is used to classify the reviews intopositive and negative
Methods for the development of stochastic competence in mathematics lessons at school

Stochastic Oscillator is the full name of one of the most popular technical indicators, which is included in 80% of all existing trading strategies, if not more. It is a favorite tool for identifying trend reversal points for both beginners and seasoned pros. And all thanks to its simplicity and high efficiency. The following article investigates the ways to promote stochastic competence in a school setting.
Blockchain enabled Security Framework for handling Security and privacy challenges in Smart cities

One of the intriguing secure applications with regards to the keen city is communicating data to authentic clients, alluringly with client protection. Broadcast encryption has been considered as a helpful device to ensure the information security and give information access control. Most works in broadcast encryption are about the information security, while less consideration is paid to the information access control and the character protection. In this paper, in view of transmission encryption, we present a plan called Fully Privacy-Preserving and Revocable Identity-Based Broadcast Encryption, which protects the information security and the personality security of the collector just as the denied client. The information can be safely ensured and just the approved client can get to the information. The repudiation cycle doesn't uncover any data about the information substance and the collector personality. The general population adapts nothing about the recipient character and the repudiated client personality. These properties lead to pleasant applications in the shrewd city where character protection is attractive. The security of our plan is end up being semantically secure in the arbitrary prophet model
Distributed deep Autoencoder for recommendation system

Recommender systems are one of the prominent area often the researchers are attracting to apply the concepts of Deep neural networks. Many of them introduced the problems facing by users like information load, Sparsity, Top-K recommendations, rating predictions and user feedback and giving best suitable solutions. Many machine learning and matrix factorization techniques are used to solve the problems but problems are linear modeling. Now in this paper, we propose distributed auto encoder with optimization to capture non-linear relationships between users and items to give predictions for missing values, giving top-k recommendations. We did several experiments on movielens 100k,movielens20M as compared with base line methods. Our proposed model performance outperforms the other existing models in terms of RMSE, MAE, HitRatio and Novelty evaluation metrics.