Miracle-dz/UniFAR-FTUs huggingface dataset:
Representation Learning in Linear Factor Models

A promise of representation learning—an active research area in machine learning—is that algorithms will, one day, learn to extract the most useful information from modern data sources, such as videos, images, or text. In this work, we analyze recent theoretical developments in the representation learning literature through the lens of a linear Gaussian factor model. First, we derive sufficient representations —defined as functions of covariates that, upon conditioning, render the outcome variable and covariates independent. Then, we study the theoretical properties of these representations and establish their asymptotic invariance ; which means the dependence of the representations on the factors’ measurement error vanishes as the dimension of the covariates goes to infinity. Finally, we use a decision-theoretic approach to understand the extent to which representations are useful for solving downstream tasks . We show that the conditional mean of the outcome variable given covariates is an asymptotically invariant and sufficient representation that can solve any task efficiently, not only prediction
Distributed Settlement of Frequency Regulation Based on a Battery Energy Storage System

Battery energy storage systems (BESS) have wide applicability for frequency regulation services in power systems, owing to their fast response and flexibility. In this paper, a distributed method for frequency regulation based on the BESS is proposed, where the method includes two layers. The upper layer is a communication network composed of agents, which is used to transmit and process information, whilst the bottom layer comprises the power system with the BESS, which provides a frequency regulation service for the system. Furthermore, a set of fully distributed control laws for the BESS are derived from the proposed distributed method, where economic power dispatch and frequency recovery are simultaneously achieved. Finally, simulations were conducted to evaluate the effectiveness of the proposed method. The results show that the system frequency regulation and economic power dispatch are achieved after considering the limits of the battery state of charge and communication delays.
Effect of Inventory Management System on Operational Performance in Manufacturing Firms: Study of May and Baker Manufacturing Industry Nig Ltd, Lagos

-- Inventory management has been a problem to many business organizations in Nigeria. Hence, it has become imperative to identify and enlighten management on the proper use of inventories. How inventory management systems affects operational performance in manufacturing firms is the primarily concern of this study. To further investigate the study and accurately analyze the result, three hypotheses were formulated. A descriptive research design random sampling techniques were employed where the sample size comprised of a total of sixty (60) staff were randomly selected from May and Baker Manufacturing Nigeria Limited in Lagos State. A non-parametric statistics of chi-square was employed to test for the relevant hypotheses. The outcome of this study reveals that, there is significance relationship between poor inventory management system and organizational performance. This study conclude that, failure to maintain a proper, adequate and accurate inventory control management will results in fall in profit and performance of May and Baker Nigeria Limited and vice-versa. It was recommended that, the organization should avoid the dangers that are inherent in keeping too little or too much of stock. There is also the need for organizations to train their personnel in the area of inventory control management. In general, the findings that emerged from this study have indicated that organizations stand to gain a lot from effective inventory control management system. Some of this benefit include optimal use of resources, cost reduction, improved profitability, reduction of waste, transparency and accountability, easy storage and retrieval of stock amongst others.
Using Language Models to Convert Between Natural Language and Game Commands

Dungeons and Dragons is a popular tabletop role-playing game that has been adapted to on-line play. In this paper, we look at enhancing a Discord Bot called Avrae that is developed by D&D Beyond to help with online play. Avrae enables users to manage gameplay through Unix-like commands. We explore using language models to automatically translate player dialogue into Avrae’s commands. We use GPT-3’s few shot learning and fine tuning capabilities and achieve 64% accuracy. We also explore the reverse direction, where commands are rendered as descriptive text, suggesting that it may eventually be possible to combine Avrae and LMs to create a system that is capable of role playing alongside players.
A Study to Determine the Elements of Hotel Service Quality based on Online Reviews

Service quality improvement in the hospitality industry has been the focus of academic attention. Online reviews posted by travelers after their stay are of great significance to hotel operators because these reviews represent direct feedback from travelers after their experience. In this paper, an in ‐ depth study on service quality improvement of hotels based on online reviews is done by pre ‐ processing online reviews combined with LDA model to obtain service quality elements; at the same time, an emotion dictionary is established and the final satisfaction rating of travelers is determined by analyzing the emotion words combined with the weight of service quality elements; finally, the correctness of this paper's method is verified through case studies. The method proposed in this paper is important for service quality improvement in the hotel industry and provides reference for hotel operators.
A Set of Low-power Microcontroller-based Modules Used for Testing of Small Energy Measurement Methods

Estimation of energy consumption of low-power devices is not widely discussed in the scientific literature, but this topic is getting more and more popular. Evaluation process of newly developed methods and instrumentation requires carefull treatment. In order to fulfill this niche, the set of micro-power microcontroller based devices was proposed. The devices represent typical energy consuption profiles and allow experimental verification of newly developed methods for energy estimation. Streszczenie. Estymacja poboru energii układów mikromocowych nie jest zbyt obszernie opisywana w literaturze, choć temat ten staje się coraz bardziej popularny. Testowanie metod i urządzeń pomiarowych małych energii wymaga zastosowania odpowiedniego podejścia, w tym celu zaproponowano i przebadano zestaw urządzeń reprezentujących typowe profile poboru energii i pozwalających praktycznie weryfikować metody estymacji poboru małych energii. (Zestaw niskomocowych układów mikrokontrolerowych do testowania metod pomiaru małych energii).
Tackling Project Management Competence Research

The vast amount of previous research on project management competence does not provide a basis for educational needs. Analyzing previous research poses two challenges: the lack of a uniform list of competences, necessitating a taxonomy, and the use of importance as a criterion, favoring general important competences. Criticality is introduced as the competence a project manager adds to the team. Validation research using criticality and the taxonomy among experienced Dutch project managers is more comprehensive and provides a lesser focus on general important competences than previous research. Criticality focuses more on the essence of the profession.
Deterministic Point Cloud Registration via Novel Transformation Decomposition

Given a set of putative 3D-3D point correspondences, we aim to remove outliers and estimate rigid transformation with 6 degrees of freedom (DOF). Simultaneously estimating these 6 DOF is time-consuming due to high-dimensional parameter space. To solve this problem, it is common to decompose 6 DOF, i.e. independently compute 3-DOF rotation and 3-DOF translation. However, high non-linearity of 3-DOF rotation still limits the algorithm efficiency, especially when the number of correspondences is large. In contrast, we propose to decompose 6 DOF into $(2+1)$ and $(1+2)\ DOF$. Specifically, $(2+1)DOF$ represent 2-DOF rotation axis and 1-DOF displacement along this rotation axis. $(1+2)\ DOF$ indicate 1-DOF rotation angle and 2-DOF displacement orthogonal to the above rotation axis. To compute these DOF, we design a novel two-stage strategy based on inlier set maximization. By leveraging branch and bound, we first search for $(2+1)\ DOF$, and then the remaining $(1+2)\ DOF$. Thanks to the proposed transformation decomposition and two-stage search strategy, our method is deterministic and leads to low computational complexity. We extensively compare our method with state-of-the-art approaches. Our method is more accurate and robust than the approaches that provide similar efficiency to ours. Our method is more efficient than the approaches whose accuracy and robustness are comparable to ours.
Validating the Salience of Haptic Icons for Automotive Mid-Air Haptic Gesture Interfaces

Mid-air haptic technology has enabled a new research arena in spatial interaction to emerge. Various efforts have attempted to pair mid-air haptics with gestural input for In-Vehicle Infotainment Systems but have not explored the higher reaches of semiotic information transfer during these interactions. Building on a participatory design study in which participants’ metaphors for seven infotainment features were “haptified”, twenty-five participants were recruited to evaluate the perceptual efficacy of the sixteen resulting mid-air haptic icons. Four perception metrics (Instant Identification, Recognition Rate, Instant Articulatory Directness and Distinguishability) were collected through stimulus matching and descriptive tasks. The haptic icons which offered the best saliency (i.e. received the highest cumulative scores for all four metrics) were subsequently selected to represent their respective features in a prototype Mid-Air Haptic Gesture Interface, which will be evaluated in further, ongoing work. The study highlights an important stage in the development and deployment of semiotic mid-air haptics for gestural interfaces.
Identifying the Priority of the Defects in Software Projects using Statistical Techniques

: Defects are part of any process irrespective ofpeople, domain, technology and the life cycle. Software development that involves 100 percent human resource defects can occur. There could be certain amount of defect types found during the life cycle of a project and the defects could come during maintenance. In this paper a methodology is proposed to identify the most impacting defect types to focus on the possible X factors that will impact the output variable. Also to identify the causes for the impacting defect types based on which the recommendations can be made to reduce the overall defects and there by deliver defect free product on schedule. Grouping of the defects is done based on the Tukey Method and Fisher Test .
Integrating Transformer and Paraphrase Rules for Sentence Simplification. — Source link

Sentence simpliﬁcation aims to reduce the complexity of a sentence while retaining its original meaning. Current models for sentence simpliﬁcation adopted ideas from machine translation studies and implicitly learned simpliﬁcation mapping rules from normal-simple sentence pairs. In this paper, we explore a novel model based on a multi-layer and multi-head attention architecture and we pro-pose two innovative approaches to integrate the Simple PPDB (A Paraphrase Database for Simpliﬁcation), an external paraphrase knowledge base for simpliﬁcation that covers a wide range of real-world simpliﬁcation rules. The experiments show that the integration provides two major beneﬁts: (1) the integrated model outperforms multiple state-of-the-art baseline models for sentence sim-pliﬁcation in the literature (2) through analysis of the rule utilization, the model seeks to select more accurate simpliﬁcation rules. The code and models used in the paper are available at https://github.com/ Sanqiang/text_simplification .
A Literature Survey on Challenges and Issues on Mobile Ad Hoc Networks

-This paper tells about the security issues and their current solutions in the mobile ad hoc network. There are numerous security threats that disturb the development of vulnerable nature of the mobile ad hoc network. Firstly, we analyze the main vulnerabilities in the mobile ad hoc networks, which have made it much easier to suffer from attacks than the traditional wired network. After this we discussed the security criteria of the mobile ad hoc network and present the main attack types that exist in it. Finally we survey the current security solutions for the mobile ad hoc network.
Dragonfly Algorithm-based Detection Technique for Man-In-The-Middle Attack in Fog Computing Environment: A Conceptual Framework

— Fog computing is a recent model of computing, in a distributed way that extends the cloud computing operation to the network edges. Fog computing enables storage execution and tasks processing, which relies on the cooperation of users and resource sharing among various devices. The fog being the new shift to cloud computing addresses some critical challenges associated with cloud model by providing notable advantages which are location sensitivity, latency minimization, geographical accessibility, wireless connectivity, mobility support and improved data streaming. Nevertheless, fog computing concept is never an option for replacing cloud computing model. In spite of the attractive solutions found in fog computing, it also inherited some security problems from the cloud. Most of the proposed techniques to solve security issues in fog computing could not completely addressed the security challenges due to the limitation of the various techniques. A fog computing security approach that is based on man- in-the middle attack using Dragonfly algorithm (DA) detection algorithm is conceptualized here. This paper is a framework for detection of MITM attacks that exist between the fog nodes and the cloud and vice versa using swarm intelligence optimization techniques called the DA algorithm which is be implemented on the platform of ifogsim simulator.
Incorporating a Nodal Reactive Power Pricing Scheme Into the DisCo’s Short-Term Operation

Distribution networks have undergone fundamental changes driven mainly by the rapid penetration of distributed energy resources. The distributed power sources connected to the distribution wires (often through power inverters) can provide not just active power, but also reactive power support to the grid. Reactive power management is a key aspect that may substantially affect the efficiency and the quality of supply of distribution networks and, as a consequence, the operation of a distribution company (DisCo). This work proposes the integration of an enhanced version of a practical and transparent nodal reactive power pricing scheme into a precise and complete short-term operation model of a DisCo. This model explicitly considers distribution-network-related technical constraints such as dispatchable and intermittent distributed generation units, voltage-sensitive loads, time-varying retail prices, stationary batteries, step voltage regulators, shunt capacitor banks, and demand-related constraints. The resulting model is characterized as a large-scale highly nonlinear and nonconvex program with continuous, binary, and discrete variables. To solve such problem, a pseudo-dynamic Tabu Search–based algorithm is proposed and effectively tested.
Extracting Human-Level Knowledge from Big Numerical Spatial Data

"Big data" has been thrust into the public consciousness as of late, carrying with it sometimes extravagant promises of innovation and beneﬁts for everyone. Certainly there appears to be no shortage of data in sight, yet following through with the promise and potential of big data poses a number of challenges. One signiﬁcant roadblock to making use of big data is the time and expertise required to perform analysis and derive products from the data. The more the process of extracting spatial knowledge based on human-level concepts, e.g., lakes, valleys, and relations, e.g., near, after, can be automated through the use of computational data analysis, the more time there is for humans to ask important questions and draw conclusions from the data. Here a methodology for extracting human-level knowledge from spatial data is presented alongside an automated means of encoding the derived products into Linked Data for use by a question answering system. The methodology is extensible since it provides a spatial framework that can be used to aggregate any new additional knowledge that may become available as new techniques for extracting knowledge from spatial data is made available.
Performance Evaluation of User Class Based Call Admission Control Techniques in Next Generation Wireless Networks

— The users in the next generation wireless networks will be offered with abundant services by the network operators. The objective of the network operators would be to enhance revenue by accommodating maximum number of users, while at the same time they need to ensure that the users are given with the quality of service promised. Call admission control is the process of accepting or rejecting the call in the system and has a direct control on the number of users in the system. The design of call admission controller will be of utmost importance to the network operators as increase in the number of users in the system will increase the revenue generated. In this paper, the working of five different user class based models for call admission control are explained and evaluated. The simulation results for system call blocking probability versus utilization rate of different models are presented.
Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory

Specific information about types of appliances and their use in a specific time window could help determining in details the electrical energy consumption information. However, conventional main power meters fail to provide any specific information. One of the best ways to solve these problems is through non-intrusive load monitoring, which is cheaper and easier to implement than other methods. However, developing a classifier for deducing what kind of appliances are used at home is a difficult assignment, because the system should identify the appliance as fast as possible with a higher degree of certainty. To achieve all these requirements, a convolution neural network implemented on hardware was used to identify the appliance through the voltage and current (V-I) trajectory. For the implementation on hardware, a field programmable gate array (FPGA) was used to exploit processing parallelism in order to achieve optimal performance. To validate the design, a publicly available Plug Load Appliance Identification Dataset (PLAID), constituted by 11 different appliances, has been used. The overall average F-score achieved using this classifier is 78.16% for the PLAID 1 dataset. The convolution neural network implemented on hardware has a processing time of approximately 5.7 ms and a power consumption of 1.868 W.
Does the Invisible Hand Efficiently Guide Entry and Exit? Evidence from a Vegetable Market Experiment in India

What accounts for the ubiquity of small vendors operating side-by-side in the urban centers of developing countries? Why don’t competitive forces drive some vendors out of the market? We ran an experiment in Kolkata vegetable markets in which we induced (via subsidizing) some vendors to sell additional produce. The vendors earned higher profits, even when excluding the value of the subsidy. Nevertheless, after the subsidies ended vendors largely stopped selling the additional produce. Our results are consistent with collusion and inertial business practices suppressing competition and efficient market exit.
A CUBIC B-SPLINE COLLOCATION METHOD FOR THE EW EQUATION

A numerical solution of the Equal Width (EW) equation based on a collocation method incorporated cubic B-splines is investigated. Accuracy of the proposed method is shown numerically by calculating conservation laws, L2 and L∞ norms on studying migration of a single solitary wave. It is shown that the collocation scheme for solutions of the EW equation gives rise to smaller errors and is quite easy to implement. The development of the undular bore and generation of the waves are studied for the EW equation. The linearized stability of the proposed method is derived by using Von Neumann stability analysis.
on The Effect of Distance Education and Technologies Online towards Self-efficacy Motivation for Online Learning

This research aimed to investigate the effect of distance education during the COVID-19 pandemic on psychological counselor candidates’ self-efficacy perceptions towards online technologies and their motivation for online learning. Single group pretest-posttest model, a quantitative method, was used in the research. The study participants were third-year students at the Faculty of Education, Psychological Counseling and Guidance Department of a state university in the fall semester of 2020-2021. Demographic Information Form, Online Technologies Self-Efficacy Scale and Online Motivation Scale were used as data collection tools. As a result of the research, it was observed that the psychological counselor candidates’ self-efficacy for online technologies and online learning motivation scores were above the average. It was concluded that the distance education used during the COVID-19 pandemic caused a statistically significant difference in psychological counselor candidates’ self-efficacy towards online technologies, but did not cause a difference in their online learning motivation. There was no significant difference in self-efficacy according to gender and place of residence. However, while there was no significant difference in motivation according to gender, a significant difference was observed in regards to the place of residence in favor of citycenter. It is important to present psychological counselor candidates’ perceptions and motivations in regards to distance education, which is an ongoing process at the moment, for their professional development.
Extracting randomness: how and why. A survey

Extractors are Boolean functions that allow, in some precise sense, extraction of randomness from somewhat random distributions. Extractors, and the closely related "Dispersers", exhibit some of the most "random-like" properties of explicitly constructed combinatorial structures. In turn, extractors and dispersers have many applications in "removing randomness" in various settings and in making randomized constructions explicit. This manuscript surveys extractors and dispersers: what they are, how they can be designed, and some of their applications. The work described is due to of a long list of research papers by various authors-most notably by David Zuckerman.
LQR Anti-rollover Control of Active Suspension in Vehicle

In order to improve the ability of anti-rollover for vehicle, the paper established a 4 DOF vehicle rollover dynamics model, and proposed a LQR control strategy based on active suspensions for vehicle. Under the Matlab/Simulink, the simulations and the results show that the presented control scheme not only can reduce the roll angle and roll angular velocity, but also can improve the rollover stability of the vehicle, and reduce the possibility of vehicle rollover accident.