Reviewerly/exHarmony huggingface dataset:
Optimizing Optimizers: Regret-optimal gradient descent algorithms

This paper treats the task of designing optimization algorithms as an optimal control problem. Using regret as a metric for an algorithm's performance, we study the existence, uniqueness and consistency of regret-optimal algorithms. By providing first-order optimality conditions for the control problem, we show that regret-optimal algorithms must satisfy a specific structure in their dynamics which we show is equivalent to performing \emph{dual-preconditioned gradient descent} on the value function generated by its regret. Using these optimal dynamics, we provide bounds on their rates of convergence to solutions of convex optimization problems. Though closed-form optimal dynamics cannot be obtained in general, we present fast numerical methods for approximating them, generating optimization algorithms which directly optimize their long-term regret. These are benchmarked against commonly used optimization algorithms to demonstrate their effectiveness.
Station Correction of P-Alert Network to Improve Magnitude Estimation for Earthquake Early Warning

&amp;lt;p&amp;gt;Magnitude estimation for earthquake early warning has been shown that it can be achieved by utilizing the relationship among the first three seconds P-wave amplitude, hypocentral distance and magnitude. However, the regression models in previous studies about P-Alert didn't include station correction factors, which may cause non-negligible effects. Thus, to improve the precision of magnitude estimation, we take station corrections into consideration when building the regression model. For the reason that station corrections are the unobserved latent variables of the model, we adopt the iteration regression method, which is based on the expectation-maximization algorithm, to determine them. By using this method, we are able to approach the values of both the station corrections and the coefficients of the regression model after several iterations. Our preliminary results show that after utilizing the iteration regression method, the standard deviation reduces from 0.30 to 0.26, and the station corrections we get range from -0.70 to 0.66.&amp;lt;/p&amp;gt;
A Study of the Effect of Teachers Behavior on Students Academic Achievement at Secondary Schools Level

The present research was designed to explore the effect of teachers behaviour on students academic achievement at secondary school level. It was descriptive research based on survey design and was conducted in Gujranwala division. The data was collect from 1221 students of the 10th class who were selected by applying simple random sampling technique via self-developed questionnaire. The collected data was arranged, coded, and entered into computer for analysis. Data was analyzed by applying inferential statistics with the help of SPSS (version 20) software. The result shows that there is significant difference in the effect of teachers behaviours on the academic achievement of students. The teachers who always adopted directive behaviour in the class, their students learn more because majority of them performed excellent in exam which indicates high academic achievement of students. The teachers who always adopted authoritative behaviour in the class, their students learn moderately because majority of them performed good in exam which indicates moderate academic achievement of students. The teachers who always adopted aggressive behaviour in the class, their students learn poor because majority of them performed average in exam which indicates poor academic achievement of students, and the teachers he/she who always adopted tolerance behaviour in the class, their students learn moderately because majority of them performed very good in exam which indicates very good academic achievement of students.
Методика управления проектами Agile & Scrum

This article describes Agile software development philosophy and Scrum methodology. Describes the main difference between Agile Scrum and PMI. The main idea is to describe what companies can use Scrum to manage their projects
항공사 수요예측모형을 활용한 수익경영성과 분석 연구

Predicting potential demand in the future is an important issue in terms of estimating the size of demand in the light of uncertainty in the future market for a particular service offering. Air demand is selected by a variety of factors, and is ultimately determined by schedule, day of the week, season, price, service awareness, airline recognition, quality of service, reservation convenience, loyalty program, etc., which includes departure and arrival time. In order to realize revenue growth through the airline's profit management, accurate demand forecasts must be first of all. Therefore, this study uses the eight models presented by PROS to demonstrate the optimal demand forecast from the specific point of departure D-352 to the point of departure of the aircraft, using the eight models presented to determine the optimal profitability in the revenue management process, so as to establish the rationale for selecting the appropriate demand forecast model for each demand situation.
Application Design Of Delivery Services Cv. Niskala Forward Globalindo

Writing this paper aims to describe the design of the User Interface and User Experience designs that can be used in the service delivery application of a company in Indonesia. This design useful for making the application display easier to understand, comfortable to view, and comfortable to use.The use of the user interface design will be applied to mobile applications that will be used by customers of delivery services. In the delivery service, there will be several forms of service that differentiate it from similar companies.
Key features of the fourth industrial revolution in South Africa???s basic education system

4IR brings a variety of changes in all the spheres of life, including industry and education. The unprecedented speed of technological innovation characterizes 4IR. Education has attempted to keep up with these changes; however, technology's effectiveness and its challenges are not well documented in this sector. This study seeks to assess the availability of the basic features needed to drive the 4IR in South Africa’s basic education system. Essentially, the research question that the study sought to answer was ‘what are the key features in place to drive 4IR in South Africa’s basic education system’? Data was collected using the interviews. Interviews were conducted through telephone, Zoom, and Microsoft Teams with various school principals.Our key findings are that the basic education sector in South Africa is not geared for 4IR; however, there are pockets of excellence driven by the private sector. The study found that various online platforms such as WhatsApp/Google Apps, YouTube licensing, Office 365 licensing/ MS Teams and/or Zoom were used to facilitate teaching and learning, mainly in urban schools. The results indicated that many rural teachers were not competent as urban teachers in using technology to teach. Accordingly, teachers relied heavily on traditional teaching methods such as green board and chalk.This study implies that the South African government must prioritize technologies that can facilitate teaching and learning in the 4IR as many schools did not have these technologies, especially in the rural areas. Very few interviewees understood 4IR and its implications. There is a need for higher learning institutions to design short learning programs aimed at introducing educators to 4IR. After the procurement of technologies in schools, there is usually a tendency to provide overlook the importance of training. As a result, there is a need for training to be intensified on how to operate these technologies for teaching and learning in South Africa.
Multilevel Thresholding Image Segmentation Based on Extension of Artificial Raindrop Algorithm

A multilevel thresholding image segmentation method based on Extension of artificial raindrop algorithm is proposed to solve the problems of slow speed, low precision and poor stability when multiple thresholds are located parallelly via intelligent optimization algorithm. After Otsu method was selected as the fitness function of ARAE, the best threshold is located gradually by simulating the raindrop movement in nature. Experimental results show that the proposed method not only has a high segmentation precision, but also is more suitable for finding more thresholds.
A Review of Modelling and Control of Inverted Pendulum System

Two wheeled inverted pendulum mobile robot provides the best substitute for human-piloted vehicles in case of danger to human life but it is always a challenging task to control, stabilize the motion and complex trajectory of these vehicles, particularly in challenging environment. Every such vehicle has complex dynamics, unstable and unpredicted behaviour, and complex structure. This paper reviews the research on modelling and control of two-wheeled robots thus far – particularly the controllers for balancing a two-wheeled robot.
Dual Encryption based secured data maintenance using Fog Computing

Many people and organizations are accessing data in the cloud due to the rapid growth of cloud computing .Fog computing interface used between cloud and the end devices to reduce the overhead of the cloud computing and better network services, with secure data transmission requirement. In this paper, Identity Based Broadcast Encryption with Decrypting Time Interval (IBBE-DTI) scheme is proposed for protecting the data securely. This facilitates the data to be encrypted with authentication token which is the combination of private key and identities of users who wants the data. To increasing the security, data owner additionally set decrypting time interval along with the data to be encrypted which leads to additional key namely Time Secret Key (TSK) for decryption. This paper proposed a dual verification process for efficient user authentication based on authorization token, which will be defined by the data owner, and a Unique Signature, which will generate by the fog processor at the time of encryption. This proposed work supports both security of data and verifies the user authentication for efficient data transmission with low computation overhead.
SEMI-SUPERVISED ASPECT-BASED SENTIMENT ANALYSIS ON ALBANIAN RESTAURANT REVIEWS

The aim of our research is to analyse the power of sentiment analysis on hearing the voice of customers. Aspect-based sentiment analysis, unlike general sentiment analysis, shows the opinion for each aspect of a specific product, service, person, event or any social issue add it gives more detailed information about people’s attitude.By considering the huge amount of online data generated by the social networks’ users, we examined the possible application areas for aspect-based sentiment analysis in the Albanian ecosystem and found out that the top topics are politics, restaurants and sports. That is why we targeted the restaurants reviews and analysed their sentiments in three topics: service, food and ambiance.By analysing the sentiments of the customers, one can identify the liked and disliked properties of the existing products/services not only for themselves but for the competitors as well.Since aspect-based sentiment analysis, unlike general sentiment analysis, shows the opinion for each aspect of a specific product or service, it gives more detailed information about customers’ suggestions.The first task of ABSA is the aspect term extraction which then is followed by the sentiment identification for each aspect and at last the sentiment polarity is extracted.Depending on the available data, there are different methodologies that can be used for these tasks, including supervised, semi-supervised and unsupervised learning methods, which can be applied in language-independent and topic-independent tools.In our case we have used nearly unsupervised learning approach, since to our best knowledge there is still no publicly published labelled dataset for Albanian texts. On the other hand there is huge amount of unused data which is generated from the reviews on social media.The model that we have used for conduction the ABSA tasks depends on Non-Negative Matrix Factorization (NMF) topic modelling algorithm and MaxEntropy algorithm.In both of the ABSA stages, we managed to gain an acceptable accuracy for aspect term extraction as well as for sentiment extraction and polarity.In this research we have used a corpus of around 2500 reviews from two different restaurants. The purpose of considering data from different restaurants is to compare them in the above mentioned topics and find out for which of them the costumers have more positive opinion than the other.So, by using aspect-based sentiment analysis, they can find all the strengths and weaknesses of their competitors’ products/services and business strategies.
Ethnopsychological features of the preparation of student girls for family life

In this paper, ethnic psychological mechanism of preparing student girls for family and influencing factors are given. A conscious, educated person benefits others, is not deceived by someone. At the same time, there will be no excessive burden on others. He sees everything on the scales of reason and logic, justice. Methodical education of children, emotional environment in the family, material of the family in terms of Supply, Organization of travel and performances, “cell”, “house roles” “mistress”, partnership in sexual relations, family subordination organization is studied as such groups. Thus due to the inconsistency of the available opportunity with the wishes of the students occurs. Usually, students think more seriously about the correct choice of a higher educational institution and specialty in the II and III courses.
Nucleus Segmentation from Microscopic Bone Marrow Image.

Acute myeloid leukemia (AML) is a type of cancer that originates in the bone marrow and moves into the blood quickly. The disease progresses rapidly if not treated. Therefore identification of the disease at an early stage is very important. In the proposed work, different image segmentation techniques like K-medoids and watershed, region growing and active contour have been applied for the segmentation of the nucleus from the blast cells and the results are analyzed.
AN UNDERSTANDING ON SAP HANA IN LIGHT OF SPEED, AGILITY AND RESILIENCE & IT'S IMPLEMENTATION STRATEGIES ON POST COVID SCENARIO

In this article author tried to give an idea to readers regarding much talked about New generation ERP system of SAP i.e SAP HANA system, How it works and its technical advantages in terms of Strategic Agility & resilience, along with its suitability on post covid situation. This article also talks about the different Implementation & transition scenarios of SAP S4 HANA . SAP S/4HANA is an ERP business suite based on the SAP HANA in-memory database which enable the user companies to perform business transactions and simultaneously helps in analyzing business data in real-time. This article also describes the different deployments models of SAP S4 HANA and also describes the multiple transition scenario's for moving to SAP HANA . In this article author also tried to identify the potential reasons for shifting to S4 Hana from ECC6.
Multi-modal Ensemble Models for Predicting Video Memorability

Modeling media memorability has been a consistent challenge in the field of machine learning. The Predicting Media Memorability task in MediaEval2020 is the latest benchmark among similar challenges addressing this topic. Building upon techniques developed in previous iterations of the challenge, we developed ensemble methods with the use of extracted video, image, text, and audio features. Critically, in this work we introduce and demonstrate the efficacy and high generalizability of extracted audio embeddings as a feature for the task of predicting media memorability.
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation

Many representative graph neural networks, $e.g.$, GPR-GNN and ChebyNet, approximate graph convolutions with graph spectral filters. However, existing work either applies predefined filter weights or learns them without necessary constraints, which may lead to oversimplified or ill-posed filters. To overcome these issues, we propose $\textit{BernNet}$, a novel graph neural network with theoretical support that provides a simple but effective scheme for designing and learning arbitrary graph spectral filters. In particular, for any filter over the normalized Laplacian spectrum of a graph, our BernNet estimates it by an order-$K$ Bernstein polynomial approximation and designs its spectral property by setting the coefficients of the Bernstein basis. Moreover, we can learn the coefficients (and the corresponding filter weights) based on observed graphs and their associated signals and thus achieve the BernNet specialized for the data. Our experiments demonstrate that BernNet can learn arbitrary spectral filters, including complicated band-rejection and comb filters, and it achieves superior performance in real-world graph modeling tasks.
[Anime-Style Mod Collection] – Autumn Edition [1.7.1.1]

Introduction After Days Full Of Fierce Battles, You Are Relaxing In Your Tank’s Garage And Suddenly Realized That The Game’s Factor Such As ...
A Comprehensive multi criteria model to rank Solar Power Plant Sites using soft computing techniques

Every nation today depends on various forms of non-renewable sources of energy like oil, coal, petroleum and natural gas for their daily needs, developmental project or to support progressing economy. These fossil fuels are limited and will be exhausted soon. Renewable energy sources like solar, wind, and water are clean, unlimited, and economical. Therefore, its share of total energy consumption has been increasing rapidly in the present scenario.  As selecting a site of SPP comprises of important factor like environment, local terrain and local weather issues, large investment in installing the plant becomes a serious matter. Multi-Criteria Decision Making (MCDM) is an approach that acknowledge one in choosing the best sites among proposed or available options.Thus, this paper gives a detailed study of optimal ranking of SPP site using Analytical Hierarchy Process (AHP) and Multiple Layer Perceptron (MLP) neural network trained with Back Propagation (BP) algorithm. Three SPP sites of India were considered and various important criteria like local weather, geographical location, and environmental factors are included in our study as SPP site selection is a multi-criteria problem. AHP is a method depending on expert’s opinion to calculate the priority scales. It analyzes complex decision with the use of psychology and math’s. AHP is further compared to MLP-BP neural network for precise ranking of the site.
Australia (Tasmania) Map V5.3 -Fixed- [1.36.X]

Non-standalone Map of Tasmania and Australia. Compatible with many cards: TSM, Promods, Rusmap, Eldorado and many others. The map is replete with ...
Considerations for neuromorphic supercomputing in semiconducting and superconducting optoelectronic hardware

Any large-scale spiking neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic neuromorphic platforms that leverage the complementary properties of optics and electronics. Starting from the conjecture that future large-scale neuromorphic systems will utilize integrated photonics and fiber optics for communication in conjunction with analog electronics for computation, we consider two possible paths towards achieving this vision. The first is a semiconductor platform based on analog CMOS circuits and waveguide-integrated photodiodes. The second is a superconducting approach that utilizes Josephson junctions and waveguide-integrated superconducting single-photon detectors. We discuss available devices, assess scaling potential, and provide a list of key metrics and demonstrations for each platform. Both platforms hold potential, but their development will diverge in important respects. Semiconductor systems benefit from a robust fabrication ecosystem and can build on extensive progress made in purely electronic neuromorphic computing but will require III-V light source integration with electronics at an unprecedented scale, further advances in ultra-low capacitance photodiodes, and success from emerging memory technologies. Superconducting systems place near theoretically minimum burdens on light sources (a tremendous boon to one of the most speculative aspects of either platform) and provide new opportunities for integrated, high-endurance synaptic memory. However, superconducting optoelectronic systems will also contend with interfacing low-voltage electronic circuits to semiconductor light sources, the serial biasing of superconducting devices on an unprecedented scale, a less mature fabrication ecosystem, and cryogenic infrastructure.
ANTECEDENT FACTORS AND CONSEQUENCES OF JOB SATISFACTION IN THE JORDANIAN HOTEL INDUSTRY

The hospitality industry has been expanding globally, and it is a highly competitive industry. It is often regarded as one of Jordan's most important economic fields. The hotel business is service-oriented, which means that people are involved in the delivery of services. In this context, satisfied employees delivering high service job performance meeting customers expectation is crucial in the hotel sector to remain competitive.Success key elements are crucial in the improvement and preservation of competitiveness in the hotel sector. This paper investigated the impact of leadership style (LS), organisation structure (OS) and job satisfaction (JS) would improve employees job performance among five-star hotels employees in Jordan with quality of work life (QWL) as mediator. Questionnaires were distributed to 430 five-star hotel employees to obtain the study data. The level of relationships among the constructs (OS, LS, JS, QWL and JP) were analyzed using structural equation modelling. The results show a positive relationship of OS and LS with JP, while QWL shows fully mediating role on the relationship between JS and JP. Similar studies should be carried out on other sectors in Jordan and in other settings using different perspectives.
The effects of Referent Specificity and Utterance Contribution on pronoun resolution

Two experiments explore how pronoun resolution is influenced by a) properties of discourse referents, specifically whether they are underspecified and in need of description, and b) the contribution of the pronoun-containing utterance, specifically whether it provides a description or specifies an event. We find that these factors interact, such that when an underspecified referent is in focus, reading is facilitated for description continuations, but when a specified referent is in focus, reading is facilitated in event continuations when the specified referent continues as the topic. This study reveals one of the complex interactions that underlies pronoun resolution.