Miracle-dz/UniFAR-FTUs huggingface dataset:
Building energy index: A case study of three government office buildings in Malaysia

This paper presents the findings of a study to investigate energy consumption for three selected government office buildings in Malaysia and to determine the Building Energy Index (BEI) for each building.The main purpose of this study is to review how much the buildings utilized energy and then to identify factors affecting energy usage. To obtain basic information and data, the analysis was performed based on the data gathered, on-site survey and qualitative assessment on the building and its systems.Building Energy Index (BEI) is used for comparing energy consumption in buildings for one year and measured in kilowatts hours divided by the gross floor area of the building in square meters.Based on BEI MS 1525:2007 Standard of 136 kWh/m2/year, there is only one of selected building has achieved BEI below than requirement standard for energy efficient building in Malaysia.In the end, factors that affecting inefficient energy consumption will provide researcher to propose appropriate energy saving approaches in future study.At the meanwhile for a long term solutions and consistent energy savings, it can be achieve by implementing energy efficient design with sustainable technologies in the early stage of construction to maintain the best environment of workplace while minimizing the energy cost.
HOW ISO QUALITY MANAGEMENT STANDARDS CHANGE

This study examines the level of excellence of two companies, using Business Improvement Matrix (BIM), which is based on European Foundation of Quality Management’s Model of Excellence. It compares those organizations and shows how their quality management systems make an effect on BIM results. The researched companies are genXone S.A. , which is in the process of implementing a quality management system compliant with ISO 13485: 2016, and Centrum Genetyki Medycznej GENESIS Sp. z o.o. , certified with ISO 9001: 2015 since 2018. Both of them belong to sequencing industry in Poland, using new generation sequencing. This study shows that the company that has implemented its quality management system and has fully adopted its standards achieved a higher level of excellence than the company that just started the process of implementation of their quality management system.
Economics of Official Development Assistance: Views from Japan and East Asia * 1)

In this paper, we discuss how development assistance has been and should be designed in achieving development goals from views of Japan and East Asia. In pursuing successful ODA under the ODA governance structure, there are three necessary conditions: first, the aid allocation should be consistent with the global poverty targeting framework; second, ODA as a mode of capital inflows should be the one facilitating the recipients’ efforts and growth directly and indirectly; and third, aid governance should be designed to minimize transaction costs arising from aid proliferation. In achieving these three conditions, experiences of Japan and East Asia in providing ODA in an apolitical way to align international agendas, in utilizing loans to develop infrastructure, and in achieving borrowed technology-driven industrialization with rapid human capital accumulation, will provide important insights into setting future global development policies. Also, the “ODA Trinity”, i.e., complementarities between ODA, FDI, and economic growth, are the key in the next generation’s ODA policies. Finally, ODA can play an important role in facilitating regional cooperation in disaster management, because it is imperative to develop formal mechanisms across countries to diversify aggregate disaster risks.
Product division among teams

This thesis provides an understanding of how small companies change their strategies while growing. Small companies usually have lack of standard processes and there is a lot of focus towards the customers and the markets but as they start to grow there emerges a need to adopt standardization. As many small companies consider flexibility as their competitive advantage they don’t want to compromise on their flexibility towards the customers while pursuing standardization, so the strategies of such companies have to be adjusted keeping this fact into consideration. In this study we used a small manufacturing company, Rancold AB as a case study to carry on our analysis. As the company was facing certain challenges while growing, an improvement project was initiated by the company and we were asked to provide improvement suggestions. During our analysis we found out that as the companies start to grow there is a shift in focus from markets towards the resources and the owner-manager wants to better utilize the available resources to improve the efficiency. For many companies in the manufacturing industry, growth implies an increase in the number of orders; so to manage this increase in volume of work they have to make changes in their manufacturing process, as was observed in the case company. When growing, companies also start aiming to achieve competitive advantage by developing new order winners and qualifiers. In the case of Rancold AB, this was done by promoting shorter lead times and better quality. To support this they have to make changes in their manufacturing strategy and shift from Make-to-order towards Assemble-to-order strategy. To introduce new standards without compromising required flexibility, we concluded that external flexibility towards the customers should remain unchanged while integrating internal flexibility in the new standard ways of working.
Short Effects of rosemary essential oil and zinc on performance, egg quality traits, and some serum metabolites in laying hens

This study was conducted to evaluate the effects of dietary supplementation of rosemary essential oil (REO) and zinc (as zinc sulfate) on performance, egg quality traits, and some serum metabolites in laying hens. Lohmann LSL-Lite laying hens (44-weeks-old) were randomly allocated into four treatments, each comprising of six replicates of six birds. One group served as the control and was fed a corn-soybean meal based diet without REO or zinc supplement. The other groups received the control diet supplemented with 200 mg/kg REO, 40 mg/kg zinc, or both REO and zinc, respectively. Supplementation of REO and zinc to the diet had no significant ( P > 0.05) effects on egg production, egg weight, egg mass, feed intake, feed conversion ratio, body weight and production of abnormal eggs. Compared with the control, egg shape index was increased ( P < 0.05) in laying hens fed with REO. Birds fed REO had lower ( P < 0.05) serum total cholesterol level compared with the control. However, this effect was not observed by feeding both zinc and REO. These results indicated that the dietary inclusion of REO had a potential to improve the egg quality traits and serum cholesterol level in laying hens.
Honey sample collection methods influence pollen composition in determining true nectar-foraging bee plants

This study investigated the suitability of honey sample collection methods for determining the botanical origin of honey through palynological analysis. We used three methods to collect honey samples in three different modes viz. extracted honey using a honey extractor, squeezed honey and pipetted honey (collected by micropipette/dropper from honey cells only) during 2017 to 2019 in West Bengal, India. We considered two native honey bee species ( Apis dorsata and Apis florea ) and one introduced bee species ( Apis mellifera ). Pollen composition differed significantly, both quantitatively and qualitatively, among the honey samples of the different methods. The number of pollen grains in extracted honey and squeezed honey was significantly higher than that of pipetted honey. Furthermore, some pollen types of nectar deficient, but polleniferous plants ( viz. Capparis zeylanica , Echinochloa frumentacea , Papaver somniferum , Poaceae type, Nelumbo nucifera , Solanum melongena , and Solanum sisymbriifolium ), were also present in extracted and squeezed honeys. We concluded that some pollen grains present in extracted and squeezed honey samples came from stored pollen loads or bee bread in the hive. Hence, the pollen spectrum for pipetted honey samples was more accurate in depicting the bees foraging on nectariferous plants.
Barrier Factors and Potential Solutions for Indonesian SMEs

Facts that Indonesian SMEs have a great potency related with the ability to survive in crisis times do not lead to products capability. Moreover, several factors became reasons why the image of Indonesian SMEs does not emerge significantly; inability in competing in global market and the outcome will be obtained in measurement form consisting several barrier factors faced by SMEs. The methodology adopted in this research is critical literature reviews, quantitative and qualitative data collection through questionnaires to 200 respondents and in-depth interviews with each of them. The research showed there are ten major barriers faced by SMEs: competition barriers; financial access; price of energy; technology; inefficient production cost; economic factors; management skill; process; limitation of sales; and raw material. Most SMEs agreed that government should dedicate more efforts in certain actions to eliminate SMEs barriers, like security standards improvement, development programmes, good financing policy, rational energy policy, continuous performance evaluation, commitment of corruption termination, and many supporting program needed by SMEs. Hereinafter, this research could contribute government programmes for empowering SMEs as the pillars of Indonesian economy.
Wanting it all: the challenges of managing talent in young generation of transition economies

Purpose: Our purpose is to identify challenges of talent management in the young generation of transition economies, based on a study of employee and employer obligations as elements of anticipatory psychological contracts (APC). We aim to analyze how APC differ between transitional and non-transitional countries and also if there are differences between transitional countries. Design/methodology/approach: We used a quantitative research design and conducted a survey using the PC Inventory among business students in Poland and Slovenia and the UK (as a control group). Findings: We found that APC in transitional countries differ significantly from the control group, with Polish and Slovenian APCs being more transactional and less relational than in the UK. Also, there are several differences between Poland and Slovenia, suggesting that CEE transitional countries cannot be considered a single region in this respect. Practical implications: We identified challenges related to talent management in transitional countries based on APC characteristics and proposed several ways in which employers and educators could help to build more realistic expectations and thus helping young talents with their transition from education to labor market. By increasing the understanding of APC employers can improve their talent management practices for the young talents. Originality/value: Our study offers unique insights into APC of the young generation in transitional countries, with regard to both employee and employer obligations. The three types of APC were studied along with particular dimensions of APC. We linked talent management to the APC characteristics. Based on our results, we propose that socio-economic context as well as national culture should be considered as antecedents of APC formation and given more attention in both psychological contract and talent management research.
Life satisfaction for gifted adults: Its relation to gender, age, religiosity, and income Life satisfaction for gifted adults: Its relation to gender, age, religiosity, and income.

The study's main objective is revealing the level of life satisfaction for gifted adults and its relation to gender, age, religiosity, income. The researcher used a descriptive approach with its two analytical and relational aspects. The study participants consisted of 80 (48 males and 32 females) members of the Jubilee School graduates from the first fifteen cohorts (Jubilee school is a special school for gifted and talented students in Jordan). To achieve the study goals and answer its questions, the researcher used the Satisfaction with Life Scale (SLS) developed by Diener et al. (1985). The finding shows that gifted adults were highly satisfied with their lives, with their living conditions, with their semi-ideal life, and they were highly satisfied that they have gotten the important things they want in life. The results also indicate no significant differences in the degree of life satisfaction among the gifted due to gender, religiosity, and income. However, there were significant differences between gifted adults' scores of life satisfaction due to age. The study concluded that gender, religion, and income have little influence on gifted adults' life satisfaction. However, further research is required to bridge the literature gap concerning different life aspects amongst gifted adults, potentially impacting their life satisfaction.
“A DARK NIGHT OF THE SOUL” : SEXUALITY, SUBJECTIVITY AND AUTOBIOGRAPHICAL MODES IN MARIAN ENGEL’S THE GLASSY SEA 1

Marian Engel’s novel The Glassy Sea (1978) features a woman in search of a creative and experiential voice through a complex process of individuation. This coming to terms with her self is doubly complicated, since the protagonist, the Protestant nun Marguerite Hebert, is confronted with secular and religious patriarchal hierarchies that displace the key to her individuation, the mastery of her body and sexuality. This paper explores the triangulation set up by an autobiographical mode, the confessional letter, the mastery of female sexuality and its role in the coming to terms with Hebert’s gendered self. The former acts as the vehicle to tackle the many contradictions inherent in the protagonist life, while it also screens a retrospective autobiography that deploys a controversial approach to Hebert’s sexual drives. This challenges the ahistorical existence of the typical nun consecrated to its present, reconstructs the nun’s past and envisions her future. Marguerite’s writing subverts the Protestant way to perfection and makes room for a lay path to self-recognition.
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning

Gradient-based Meta-RL (GMRL) refers to methods that maintain two-level optimisation procedures wherein the outer-loop meta-learner guides the inner-loop gradient-based reinforcement learner to achieve fast adaptations. In this paper, we develop a unified framework that describes variations of GMRL algorithms and points out that existing stochastic meta-gradient estimators adopted by GMRL are actually \textbf{biased}. Such meta-gradient bias comes from two sources: 1) the compositional bias incurred by the two-level problem structure, which has an upper bound of $\mathcal{O}\big(K\alpha^{K}\hat{\sigma}_{\text{In}}|\tau|^{-0.5}\big)$ \emph{w.r. t. } inner-loop update step $K$, learning rate $\alpha$, estimate variance $\hat{\sigma}^{2}_{\text{In}}$ and sample size $|\tau|$, and 2) the multi-step Hessian estimation bias $\hat{\Delta}_{H}$ due to the use of autodiff, which has a polynomial impact $\mathcal{O}\big((K-1)(\hat{\Delta}_{H})^{K-1}\big)$ on the meta-gradient bias. We study tabular MDPs empirically and offer quantitative evidence that testifies our theoretical findings on existing stochastic meta-gradient estimators. Furthermore, we conduct experiments on Iterated Prisoner's Dilemma and Atari games to show how other methods such as off-policy learning and low-bias estimator can help fix the gradient bias for GMRL algorithms in general.
OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving

Accurate sensor calibration is a prerequisite for multi-sensor perception and localization systems for autonomous vehicles. The intrinsic parameter calibration of the sensor is to obtain the mapping relationship inside the sensor, and the extrinsic parameter calibration is to transform two or more sensors into a unified spatial coordinate system. Most sensors need to be calibrated after installation to ensure the accuracy of sensor measurements. To this end, we present OpenCalib, a calibration toolbox that contains a rich set of various sensor calibration methods. OpenCalib covers manual calibration tools, automatic calibration tools, factory calibration tools, and online calibration tools for different application scenarios. At the same time, to evaluate the calibration accuracy and subsequently improve the accuracy of the calibration algorithm, we released a corresponding benchmark dataset. This paper introduces various features and calibration methods of this toolbox. To our knowledge, this is the first open-sourced calibration codebase containing the full set of autonomous-driving-related calibration approaches in this area. We wish that the toolbox could be helpful to autonomous driving researchers. We have open-sourced our code on GitHub to benefit the community. Code is available at https://github.com/PJLab-ADG/SensorsCalibration.
Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation

Masked image modeling (MIM) learns representations with remarkably good fine-tuning performances, overshadowing previous prevalent pre-training approaches such as image classification, instance contrastive learning, and image-text alignment. In this paper, we show that the inferior fine-tuning performance of these pre-training approaches can be significantly improved by a simple post-processing in the form of feature distillation (FD). The feature distillation converts the old representations to new representations that have a few desirable properties just like those representations produced by MIM. These properties, which we aggregately refer to as optimization friendliness, are identified and analyzed by a set of attention- and optimization-related diagnosis tools. With these properties, the new representations show strong fine-tuning performance. Specifically, the contrastive self-supervised learning methods are made as competitive in fine-tuning as the state-of-the-art masked image modeling (MIM) algorithms. The CLIP models' fine-tuning performance is also significantly improved, with a CLIP ViT-L model reaching 89.0% top-1 accuracy on ImageNet-1K classification. On the 3-billion-parameter SwinV2-G model, the fine-tuning accuracy is improved by +1.5 mIoU / +1.1 mAP to 61.4 mIoU / 64.2 mAP on ADE20K semantic segmentation and COCO object detection, respectively, creating new records on both benchmarks. More importantly, our work provides a way for the future research to focus more effort on the generality and scalability of the learnt representations without being pre-occupied with optimization friendliness since it can be enhanced rather easily. The code will be available at https://github.com/SwinTransformer/Feature-Distillation.
Asymptotically constant-free and polynomial-degree-robust a posteriori estimates for space discretizations of the wave equation

We derive an equilibrated a posteriori error estimator for the space (semi) discretization of the scalar wave equation by finite elements. In the idealized setting where time discretization is ignored and the simulation time is large, we provide fully-guaranteed upper bounds that are asymptotically constant-free and show that the proposed estimator is efficient and polynomial-degree-robust, meaning that the efficiency constant does not deteriorate as the approximation order is increased. To the best of our knowledge, this work is the first to derive provably efficient error estimates for the wave equation. We also explain, without analysis, how the estimator is adapted to cover time discretization by an explicit time integration scheme. Numerical examples illustrate the theory and suggest that it is sharp.
A Look at Improving Robustness in Visual- inertial SLAM by Moment Matching

The fusion of camera sensor and inertial data is a leading method for ego-motion tracking in autonomous and smart devices. State estimation techniques that rely on nonlinear filtering are a strong paradigm for solving the associated information fusion task. The de facto inference method in this space is the celebrated extended Kalman filter (EKF), which relies on first-order linearizations of both the dynamical and measurement model. This paper takes a critical look at the practical implications and limitations posed by the EKF, especially under faulty visual feature associations and the presence of strong confounding noise. As an alternative, we revisit the assumed density formulation of Bayesian filtering and employ a moment matching (unscented Kalman filtering) approach to both visual-inertial odometry and visual SLAM. Our results highlight important aspects in robustness both in dynamics propagation and visual measurement updates, and we show state-of-the-art results on EuRoC MAV drone data benchmark.
Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies

Autonomous cars are well known for being vulnerable to adversarial attacks that can compromise the safety of the car and pose danger to other road users. To effectively defend against adversaries, it is required to not only test autonomous cars for finding driving errors but to improve the robustness of the cars to these errors. To this end, in this paper, we propose a two-step methodology for autonomous cars that consists of (i) finding failure states in autonomous cars by training the adversarial driving agent, and (ii) improving the robustness of autonomous cars by retraining them with effective adversarial inputs. Our methodology supports testing autonomous cars in a multi-agent environment, where we train and compare adversarial car policy on two custom reward functions to test the driving control decision of autonomous cars. We run experiments in a vision-based high-fidelity urban driving simulated environment. Our results show that adversarial testing can be used for finding erroneous autonomous driving behavior, followed by adversarial training for improving the robustness of deep reinforcement learning-based autonomous driving policies. We demonstrate that the autonomous cars retrained using the effective adversarial inputs noticeably increase the performance of their driving policies in terms of reduced collision and offroad steering errors.
Distributed Gaussian Process Based Cooperative Visual Pursuit Control for Drone Networks

In this paper, we propose a control law for camera-equipped drone networks to pursue a target rigid body with unknown motion based on distributed Gaussian process. First, we consider the situation where each drone has its own dataset, and learned the unknown target motion in a distributed manner. Second, we propose a control law using the distributed Gaussian processes, and show that the estimation and control errors are ultimately bounded. Furthermore, the effectiveness of the proposed method is verified by using simulations and experiments with actual drones.
Quantum Augmented Dual Attack

We present a quantum augmented variant of the dual lattice attack on the Learning with Errors (LWE) problem, using classical memory with quantum random access (QRACM). Applying our results to lattice parameters from the literature, we find that our algorithm outperforms previous algorithms, assuming unit cost access to a QRACM. On a technical level, we show how to obtain a quantum speedup on the search for Fast Fourier Transform (FFT) coefficients above a given threshold by leveraging the relative sparseness of the FFT and using quantum amplitude estimation. We also discuss the applicability of the Quantum Fourier Transform in this context. Furthermore, we give a more rigorous analysis of the classical and quantum expected complexity of guessing part of the secret vector where coefficients follow a discrete Gaussian (mod \(q\)).