Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Channel Allocation Schemes in Wimax

Today many cellular networks are working in parallel. For instance Global System for Mobile Communication (GSM), Universal Mobile Telecommunication System (UMTS) and Worldwide Interoperability for Microwave Access (WiMAX) etc. All of these technologies are based on cellular networks. Cellular networks are comprised of cells. Cells in the cellular network are allocated frequency channels from the available bandwidth. These frequency channels are responsible for communication between the mobile users. Number of available channels in a cell is limited and due to this limitation if traffic in the cell is high users may face call terminations and may be blocked by the cell completely. GSM is the most popular cellular network yet used in almost 200 countries of the world [29]. Due to lack of high data rates GSM is not able to support wireless broadband users. WiMAX is currently under development and is a new technology in 3G systems (near to 4G). It has support for wireless broadband users, both fixed and mobile. For fixed users (Mainly Office and Home) disconnectivity in the connection is intolerable. Such users are needed to be allocated permanent channels, so they never face disconnectivity due to unavailability of channels in the cell. For this purpose in our thesis work we have performed analysis on channels allocation schemes namely Non Prioritized Scheme (NPS) and Reserved Channel Scheme (RCS) in WiMAX scenario, in context of permanent channel allocation. These schemes were previously used for those cellular networks which had no need for permanent channel allocation in the cell. We have performed simulations on these schemes in MATLAB and have compared their results in terms of Blocking Probability (Pb), Probability of handover failure (Ph), Probability of forced termination (Pft) and Probability of not completed calls (Pnc). Here call represents any kind of job (Mobile or Fixed).
A framework for design, modeling, and identification of compliant biomimetic swimmers

Research interests in fish-like devices are generally driven by the notion that through eons of evolution fish have developed optimal mechanisms for efficient propulsion and high degrees of maneuverability. Engineered fish-like devices have been developed in hope of mimicking the capabilities of their biological counterparts, but success has been marginal. This thesis considers a unique class of underactuated biomimetic swimmers with compliant bodies that swim by exploiting their structural dynamics. Practical matters surrounding the design and modeling of these swimmers are addressed and explicit references are made to fish morphology and swimming behaviours with the aim of linking biological and engineering design elements, a deficiency in existing literature. A hybrid modeling scheme is presented drawing upon conventional engineering primitives and experimental data. Both a hardware prototype swimmer and a unique motion capture system were developed to demonstrate the described methods. Experimental and simulated results are compared. Thesis Supervisor: Kamal Youcef-Toumi Title: Professor of Mechanical Engineering
The Impact of Innovation in Small Industrial Operations to Increased Competitiveness： Empirical Evidence

The present research work in the Small and Medium Enterprise (SME) and industrial sector explains the impact of the relationship among innovation in the SME operations of Aguascalientes to find the most competitive one; to this end, a questionnaire was intended for managers where the results have been analyzed using the statistical package Structural Equation Modeling Programs (EQS) support which through structural equations has responded to the objective. In this sense, it can be concluded that innovation has a positive relationship with the operations and in turn, this relationship shows a positive impact on the competitiveness of SMEs in Aguascalientes. For this study, a sample of 150 companies has been used.
A scalable SOI technology for three successive generations: 0.18, 0.13 and 0.1 /spl mu/m for low-voltage and low-power applications

Optimized 0.18 /spl mu/m gate length NMOSFET and PMOSFET SOI devices have been demonstrated with high electrical performances for low-voltage and low-power applications. The electrical results show that this SOI design can be easily scaled down for the next generations and no heavy ion implant (In or Sb) is required.
Wave Journal Bearing. Part 1: Analysis

A wave journal bearing concept features a waved inner bearing diameter of the non-rotating bearing side and it is an alternative to the plain journal bearing. The wave journal bearing has a significantly increased load capacity in comparison to the plain journal bearing operating at the same eccentricity. It also offers greater stability than the plain circular bearing under all operating conditions. The wave bearing's design is relatively simple and allows the shaft to rotate in either direction. Three wave bearings are sensitive to the direction of an applied stationary side load. Increasing the number of waves reduces the wave bearing's sensitivity to the direction of the applied load relative to the wave. However, the range in which the bearing performance can be varied decreases as the number of waves increases. Therefore, both the number and the amplitude of the waves must be properly selected to optimize the wave bearing design for a specific application. It is concluded that the stiffness of an air journal bearing, due to hydrodynamic effect, could be doubled and made to run stably by using a six or eight wave geometry with a wave amplitude approximately half of the bearing radial clearance.
The effect of culture in containing a pandemic: the case of COVID-19

Abstract The COVID-19 pandemic affected every country. Countries where people wore a mask, maintained social distance, and frequently washed their hands were more likely to contain the pandemic. In this article, we investigate how Hofstede’s cultural dimensions influenced the success that countries had containing this pandemic. We find that countries with higher uncertainty avoidance and indulgence had significantly higher rates of COVID-19 cases. However, there is no evidence that masculinity-femininity, power distance, long-term orientation, or individualism significantly affected people’s response to the pandemic.
Impact of gender diversity in boardroom on risk-return profile of Indian corporates

Purpose The existing literature point that the presence of women directors in a firm reduces its risk. However, the relation between boardroom gender diversity and a firm’s return is widely disputed leading to no concrete answer. Some studies mention that women directors have a positive impact on firm performance, whereas, on the other hand, some findings suggest that women directors reduce financial performance. This paper aims to study the relationship of firm risk and return with boardroom gender diversity and the net impact on firm performance in the Indian context. This study uses not only traditional measures of risk and return but also the third measure of risk-adjusted returns to postulate its findings. Design/methodology/approach Based upon the data of the top 100 of the Bombay Stock Exchange-500 firms for the period FY 2009–2010 to FY 2018–2019, this study applied fixed effect panel regression and random effects Tobit regression to examine the effect of board gender diversity on firm performance. Findings The study concludes that firms with women directors on board have lower risk and lower returns. It also results in a higher risk-adjusted return, creating a positive impact on a firm’s performance. Originality/value The paper contributes to the existing literature on corporate governance by considering return, risk and risk-adjusted returns in single research to have a holistic measure of firm performance. It provides empirical evidence from one of the largest emerging economies, India where the female director and independent female director have been introduced recently.
The Online Recruitment System ORSEE 2.0 - A Guide for the Organization of Experiments in Economics

We discuss several issues regarding the organization of economic laboratory experiments such as subject pool, recruitment, scheduling, and show how we solved them with the help of the Online Recruitment System for Economic Experiments (ORSEE) version 2.0. With this integrated software experimenters have a free, convenient, and very powerful tool to organize their experiments and sessions in a standardized way. Key features are: PHP/MySQL application, multiple language/ laboratory/ subject pool/ experimenters/ experiment types/ experiment classes support, attribute query selection, random recruitment, experiment calendar, automated reputation system, automated invitation and rule based reminder mailing, subjects manage their own account, overview about registration state, user rights management, pdf output and mailing, complete logging and statistics, and customizable layout. In version 2.0 the software has been completely reprogrammed in PHP. Several new features have been added. A test system has been installed in order to visually support the reader while reading the manual (www.orsee.org).
AN ASSESSMENT OF JOHN DEERE DEALERSHIP SERVICE PERSONNEL NEEDS AND SERVICE MANAGERS' PERCEPTIONS OF THE JOHN DEERE AG TECH PROGRAM

John Deere Corporation sponsors 16 schools within the United States that are designed to educate students to become future agricultural equipment technicians. At the time of this study, the schools’ enrollments were at a less-than desirable level, 80% capacity. However, the company was receiving feedback that dealerships needed technicians. The need to determine the disparity between the need for technicians and the level of enrollment led to the pursuit of this study. The researcher sampled 306 John Deere agricultural service managers across the continental United States to determine: (a) if the dealerships truly needed technicians, (b) the methods dealerships were using for locating technicians, and (c) the service managers’ perceptions of the John Deere (JD) Ag Tech program. The study also analyzed the relationships between the service managers’ perceptions of the JD Ag Tech program and five independent variables: (a) number of technicians employed at John Deere dealerships, (b) number of stores dealer-organizations own, (c) distance between John Deere dealerships and the closest JD Ag Tech school, (d) number of JD Ag Tech students John Deere dealerships have sponsored, and (e) service managers’ age. The results of the study indicated that John Deere service managers did plan to hire technicians over the next 12 months as well as over the next three years. However, it was also determined that 60% of the service managers were only somewhat knowledgeable or unfamiliar with the JD Ag Tech Program. For the service managers that had experienced sponsoring JD Ag Tech students, those service managers were satisfied with the JD Ag Tech graduates. The researcher’s recommendations focused upon two areas. The first was to improve the marketing of the JD Ag Tech program to insure that all John Deere service managers become familiar with the program. The second recommendation was to focus on improving the “student” component of the partnership, by recruiting the students earlier in their high school careers, and having the partners work together to target high caliber students. AN ASSESSMENT OF JOHN DEERE DEALERSHIP SERVICE PERSONNEL NEEDS AND SERVICE MANAGERS' PERCEPTIONS OF THE JOHN DEERE AG TECH PROGRAM
Comparison of superresolution algorithms with different array geometries for radio direction finding

Abstract : The objective of this thesis is to investigate and evaluate the effectiveness of modern estimation methods with different array geometries as they apply to the problem of bearing estimation. The algorithms were selected from those that apply to multidimensional direction finding, and include MUSIC, PHD, minimum norm, and Capon's beam-former. These four techniques based on their high resolution capability, and their ability to deal with three-dimensional non-uniform arrays and to estimate both azimuth and elevation angle of arrival (AQA). Computer simulations were run linear arrays, circular array, and combinations of the two. The test conditions included: (1) two closely spaced emitters, and (2) various levels of additive white Gaussian noise.
Byzantine Agreement with Less Communication

The development of reliable distributed systems often relies on Byzantine Agreement (BA) [26]. In this problem, a set of correct processes aim to reach a common decision, despite the presence of malicious ones. BA has been around for four decades, yet practical use-cases for it in large-scale systems have emerged only in the last decade. One major application for BA is cryptocurrencies. For example, Bitcoin [30], the rst cryptocurrency, requires a large set of users to agree on the state of the blockchain. Since Bitcoin is a real currency with real value, the need to protect it against Byzantine users is crucial. Following Bitcoin, many other blockchains and FinTech platforms have emerged, e.g., [6,21,30,36]. Consequently, an efficient implementation, in terms of communication, has become one of the main foci of BA solutions.
Lingua Custodia’s Participation at the WMT 2021 Machine Translation Using Terminologies Shared Task

This paper describes Lingua Custodia’s submission to the WMT21 shared task on machine translation using terminologies. We consider three directions, namely English to French, Russian, and Chinese. We rely on a Transformer-based architecture as a building block, and we explore a method which introduces two main changes to the standard procedure to handle terminologies. The first one consists in augmenting the training data in such a way as to encourage the model to learn a copy behavior when it encounters terminology constraint terms. The second change is constraint token masking, whose purpose is to ease copy behavior learning and to improve model generalization. Empirical results show that our method satisfies most terminology constraints while maintaining high translation quality.
A process for improving the definition and alignment of intangible project outcomes and project outputs: Reflections on recent project management research

It is becoming increasingly apparent that many organisations are at last taking an holistic view of the meaning of project success. Traditional project management theory has stressed the `iron triangle? of success? achieving time, cost, and quality (fitness for use) outcomes. In more recent times however, notions of value adding, project outcomes and benefits that are more intangible and difficult to formulate have emerged. Improved definition of intangible outcomes is now the subject of leading edge research in performance measurement and project management. In this paper, we report upon a study that has developed a process for the improved definition and alignment of project outcomes (both tangible and intangible) and tangible project outputs, which can be used as the basis for defining and assessing project success. The process was developed iteratively through a series of action research cycles, with each cycle focusing on one of five (5) case study projects. The process was recently tested in a small number of concurrent workshops conducted at a senior management conference of the Victorian state police force, in Australia. Police officers? responses to a brief survey were gathered before and after the workshops. Survey results presented in this paper indicate strong support for the process and its usefulness in managing projects where project success is assessed according to the achievement of a combination of tangible and intangible outcomes.
An Evaluation of WebAssembly and eBPF as Offloading Mechanisms in the Context of Computational Storage

As the volume of data that needs to be processed continues to increase, we also see renewed interests in near-data processing in the form of computational storage, with eBPF (extended Berkeley Packet Filter) being proposed as a vehicle for computation offloading. However, discussions in this regard have so far ignored viable alternatives, and no convincing analysis has been provided. As such, we qualitatively and quantitatively evaluated eBPF against WebAssembly, a seemingly similar technology, in the context of computation offloading. This report presents our findings.
Spatial regionalization as optimal data compression

The process of aggregating areal units into contiguous clusters, known as regionalization, is central to the analysis of spatial data. Regionalization provides a means to reduce the eﬀect of noise or outliers in sampled data, identify socioeconomically homogeneous areas for policy development, and simplify the visualization of data in maps among many other applications. Most existing regionalization methods require a substantial amount of manual input, such as the number of desired regions or a similarity measure among regional populations, which may be desirable for some applications but does not allow us to extract the natural regions deﬁned solely by the data itself. Here we view the problem of regionalization as one of data compression. We deﬁne the optimal partition of spatial units with corresponding distributional data as the one that minimizes the description length required to transmit the data, and develop an eﬃcient, parameter-free greedy optimization algo-rithm to identify this partition. We demonstrate that our method is capable of recovering planted spatial clusters in noisy synthetic distributional data, and that it identiﬁes meaningful ethnoracial boundaries in real demographic data. Using our description length formulation, we ﬁnd that the information contained in spatial ethnoracial data in metropolitan areas across the U.S. has become more diﬃcult to compress over the period from 1980 to 2010, which reﬂects the rising complexity of urban segregation patterns of these metros. We identify the increasing overall diversity of these met-ros as a major contributor to this lower data compressibility, while the spatial scale of ethnoracial clustering does not appear to be a signiﬁcant factor.
A Linear Model for Distributed Flexibility Markets and DLMPs: A Comparison with the SOCP Formulation

This paper examines the performance trade-offs between an introduced linear flexibility market model for congestion management and a benchmark second-order cone programming (SOCP) formulation. The linear market model incorporates voltage magnitudes and reactive powers, while providing a simpler formulation than the SOCP model, which enables its practical implementation. The paper provides a structured comparison of the two formulations relying on developed deterministic and statistical Monte Carlo case analyses using two distribution test systems (the Matpower 69-bus and 141-bus systems). The case analyses show that with the increasing spread of offered flexibility throughout the system, the linear formulation increasingly preserves the reliability of the computed system variables as compared to the SOCP formulation, while more lenient imposed voltage limits can improve the approximation of prices and power flows at the expense of a less accurate computation of voltage magnitudes.
A Survey of Machine Learning Algorithms for Detecting Malware in IoT Firmware

This work explores the use of machine learning techniques on an Internet-of-Things firmware dataset to detect malicious attempts to infect edge devices or subsequently corrupt an entire network. Firmware updates are uncommon in IoT devices; hence, they abound with vulnerabilities. Attacks against such devices can go unnoticed, and users can become a weak point in security. Malware can cause DDoS attacks and even spy on sensitive areas like peoples' homes. To help mitigate this threat, this paper employs a number of machine learning algorithms to classify IoT firmware and the best performing models are reported. In a general comparison, the top three algorithms are Gradient Boosting, Logistic Regression, and Random Forest classifiers. Deep learning approaches including Convolutional and Fully Connected Neural Networks with both experimental and proven successful architectures are also explored.
Automated SpectroPhotometric Image REDuction (ASPIRED) – A Python-based spectral data reduction toolkit

We provide a suite of publicly available spectral data reduction software to facilitate rapid scientiﬁc products from time-domain observations. The Automated SpectroPhotometric REDuction (ASPIRED) toolkit is aimed to be as general as possible with high ﬂexibility such that it can work with diﬀerent instruments. The default settings support typical low-resolution long-slit spectrometer conﬁgurations, whilst it also oﬀers a ﬂexible set of functions for users to reﬁne and tailor-make automated pipelines to an instrument’s individual characteristics. Automation can provide immediate data reduction to allow adaptive observing strategies, which is particularly important in Time Domain Astronomy. ASPIRED is entirely Python-based and is independent of iraf.
Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval

Interactive and non-interactive model are the two de-facto standard frameworks in vector-based cross-lingual information retrieval (V-CLIR), which embed queries and documents in synchronous and asynchronous fashions, respectively. From the retrieval accuracy and computational efficiency perspectives, each model has its own superiority and shortcoming. In this paper, we propose a novel framework to leverage the advantages of these two paradigms. Concretely, we introduce semi-interactive mechanism, which builds our model upon non-interactive architecture but encodes each document together with its associated multilingual queries. Accordingly, cross-lingual features can be better learned like an interactive model. Besides, we further transfer knowledge from a well-trained interactive model to ours by reusing its word embeddings and adopting knowledge distillation. Our model is initialized from a multilingual pre-trained language model M-BERT, and evaluated on two open-resource CLIR datasets derived from Wikipedia and an in-house dataset collected from a real-world search engine. Extensive analyses reveal that our methods significantly boost the retrieval accuracy while maintaining the computational efficiency.
Smooth Imitation Learning via Smooth Costs and Smooth Policies

Imitation learning (IL) is a popular approach in the continuous control setting as among other reasons it circumvents the problems of reward mis-specification and exploration in reinforcement learning (RL). In IL from demonstrations, an important challenge is to obtain agent policies that are smooth with respect to the inputs. Learning through imitation a policy that is smooth as a function of a large state-action (s-a) space (typical of high dimensional continuous control environments) can be challenging. We take a first step towards tackling this issue by using smoothness inducing regularizers on both the policy and the cost models of adversarial imitation learning. Our regularizers work by ensuring that the cost function changes in a controlled manner as a function of s-a space; and the agent policy is well behaved with respect to the state space. We call our new smooth IL algorithm Smooth Policy and Cost Imitation Learning (SPaCIL, pronounced “Special”). We introduce a novel metric to quantify the smoothness of the learned policies. We demonstrate SPaCIL’s superior performance on continuous control tasks from MuJoCo. The algorithm not just outperforms the state-of-the-art IL algorithm on our proposed smoothness metric, but, enjoys added benefits of faster learning and substantially higher average return.