Miracle-dz/UniFAR-FTUs huggingface dataset:
Reproducibility Challenge: Reformer

We attempt to reproduce the central claims of ICLR 2020 Paper "Reformer: The Efficient Transformer" (Kitaev et al. [2020]); that the techniques introduced enable performance on par with a traditional Transformer model while being much more memory-efficient and much faster on long sequences. This community effort reproduced claims around speed for long sequences and observed a reduction in memory usage. We could not match the performance of a traditional Transformer with Reformer. Finally, substantial coding effort was required, a lack of implementation documentation compounded this. We provide all code documentation in our GitHub1.
Analyzing the Punjabi Language Stemmers: A Critical Approach

Stemming is a procedure used to reduce the inflected words by removing affixes from them. It is widely used in Natural Language Processing systems developed for various languages like English, Hindi etc. Punjabi is a low resource availability language spoken in northern regions of India and Pakistan. In Pakistan, Shahmukhi script is used to write Punjabi. While in India, Gurmukhi script is used. Various approaches are used for stemming natural language words such as Brute Force approach, Rule based approach, Statistical approach etc. In Gurmukhi scripted Punjabi language stemming, either the Brute Force approach or Rule based approach or a combination of both of these approaches have been used till now. This paper presents a comparative analysis of various stemmers developed so far for stemming Gurmukhi scripted Punjabi language words based on their methodology and accuracy. It will motivate further research in developing more efficient stemmers for Punjabi language. It will benefit the researchers to compare and understand the used approaches and the problems faced in adopting each approach.
Digital Twins for Continuous Deployment in Model-Based Systems Engineering of Cyber-Physical Systems

Cyber-Physical Systems (CPS) are required to operate over a longer lifetime. As such, their requirements can change, requiring updates to the system to be be rolled out continuously (Continuous Deployment) throughout the system’s lifetime. The DevOps methodology provides a structured, quality assuring way to do so, as it integrates Development and Operations of a system in a continuous cycle. DevOps is generally applied in software development, however in the design of CPS, which follows a Model-Based Systems Engineering (MBSE) approach, it is not. This is because many challenges remain in the application of DevOps in MBSE. Our focus is on creating the foundations for continuous deployment of safety-critical CPS using digital twins of the CPS.k.
Elucidation of the structure of supramolecular polymorphs in peptide nanofibres using Raman spectroscopy

Peptide fibre formation via molecular self-assembly is a key step in a range of cellular processes and increasingly considered as an approach to produce supramolecular biomaterials. We previously demonstrated the self-assembly of to changes in the conformation of tyrosine side chains causing different electron withdrawing effects on the ring.
Research on Heterogeneous Enhanced Network Embedding for Cooperation Prediction

Scientific research collaboration has always been an important research content of information science, and collaboration prediction is an important issue in personalized information services. This article constructs an author-centered heterogeneous information fusion schema, and uses causal analysis to quantitatively study the influence of same institutions, co-word and citation on collaboration, compares the effects of different network embedding algorithms in collaboration prediction, and built heterogeneous information fused network embedding model for collaboration prediction. Take the field of stem cells as an empirical case, experiments show that the matrix decomposition based network embedding algorithms(like NetMF) are balance of performance and accuracy. Institutions, keywords and citations can improve the prediction effect of collaboration, and (same institutions > citations > co -words) among them. Multiple features fusion models are generally better than single information fusion, and the model of (collaboration + same institution + citations) performs outstanding in collaboration prediction.
ALESQP: AN AUGMENTED LAGRANGIAN 1 EQUALITY-CONSTRAINED SQP METHOD FOR 2 OPTIMIZATION WITH GENERAL CONSTRAINTS

We present a new algorithm for inﬁnite-dimensional optimization with general con-5 straints, called ALESQP. In short, ALESQP is an augmented Lagrangian method that penalizes 6 inequality constraints and solves equality-constrained nonlinear optimization subproblems at every 7 iteration. The subproblems are solved using a matrix-free trust-region sequential quadratic program-8 ming (SQP) method that takes advantage of iterative, i.e., inexact linear solvers and is suitable for 9 large-scale applications. A key feature of ALESQP is a constraint decomposition strategy that allows 10 it to exploit problem-speciﬁc variable scalings and inner products. 11 We analyze convergence of ALESQP under diﬀerent assumptions. We show that strong accumu-12 lation points are stationary. Consequently, in ﬁnite dimensions ALESQP converges to a stationary 13 point. In inﬁnite dimensions we establish that weak accumulation points are feasible in many practi-14 cal situations. Under additional assumptions we show that weak accumulation points are stationary. 15 We present several inﬁnite-dimensional examples where ALESQP shows remarkable discretization-16 independent performance in all of its iterative components, requiring a modest number of iterations to 17 meet constraint tolerances at the level of machine precision. Also, we demonstrate a fully matrix-free 18 solution of an inﬁnite-dimensional problem with nonlinear inequality constraints. 19
GAME THEORY BASED SCHEDULING OF WIND AND PSP SYSTEM

Abstract The power produced by wind energy is non-dispatchable as well as uncertain resulting in market imbalances due to the difference between actual and scheduled generation. A mixed integer type problem with game theory based min-max approach has been used to reduce these uncertainties. To provide the balance between the demand and generation, flexible operation of pumped storage plant (PSP) is used with the wind system. The main objective of the problem is to provide the stable operation of wind-PSP system with different level of uncertainties risk under the day ahead market. In this study, different type of PSP units have been considered to demonstrate the advantage of variable speed type PSP unit in the wind-PSP operation. It has been found that the risk was reduced by 40% for using variable speed type PSP unit as compared to fixed speed unit. The developed model can be useful in decision making process for using wind PSP system.
3D-COSTAR: User Guide

One of the major challenges that has to be addressed in order to make 2.5D technology commercially successful is overall cost optimization. A 2.5D-SIC consists of two or more active dies stacked on a passive silicon interposer that forms the interconnection between the active dies and to the external world. As is the case for any IC, TSV-based 2.5D-SICs must be tested in order to guarantee the outgoing product quality and reliability. Hence, test cost is indispensable. Inherent to their manufacturing process, 2.5D-SICs provide several test moments such as before stacking, during manufacturing of partial stacked IC, after the complete manufactured stack, etc. This results into a large space of test flows; each with its own cost. Determining the optimal and most efficient test flow requires the analysis of all test flows, as different design and/or manufacturing parameters may impact the cost differently. Therefore, an appropriate cost model is required. The cost model should be able to evaluate the cost of each test flow, while considering all relevant incurred costs in the production chain of the 2.5-SIC. In this document, we present 3D-COSTARthat is able to perform this. In order to use this tool, whether for reports, scientific publications or other documents, the following two publications must be referenced: • M. Taouil, S. Hamdioui, E.J. Marinissen, “Quality versus cost analysis for 3D Stacked ICs,” IEEE 32nd VLSI Test Symposium, pp.1-6, 13-17 April 2014. • M. Taouil, S. Hamdioui, E.J. Marinissen, S. Bhawmik, “Using 3D-COSTAR for 2.5D test cost optimization,” IEEE International 3D Systems Integration Conference, pp.1-8, 2-4 Oct. 2013
Process Control in Anaerobic Wastewater Treatment

The monitoring and control of anaerobic waste water treatment is discussed. In a first section optimum biological conditions were given. These are a nutritionally balanced waste water free from oxidants and toxicants, a temperature of 55-60 °C and a pH of 6.8-8.0. In a second section, parameters for monitoring and control were discussed. It was concluded that present system designs usually allow for the control of influent flow rate and temperature but not for the key parameter solids retention time (SRT). Process control in anaerobic waste water treatment, therefore, is more monitoring than control, a situation that is unsatisfactory.
Two‐period supply chain coordination strategies with ambidextrous sustainable innovations

This study considers a manufacturer with ambidextrous sustainable innovation capability selling products in environmentally conscious market through an independent retailer in a two-period game setting. We design a two-period game theoretic and dyadic supply chain (SC) model considering exploitative and exploratory nature of environmental innovations. We study five different contract types, viz. wholesale price contract, vertical Nash game structure, cost sharing contract, revenue sharing contract and two-part tariff contract. We demonstrate the impact of market sensitivity towards sustainable innovation and cost parameters on optimal level of decision parameters. The equilibrium results reveal that a suitably designed two-part tariff contract can be used to achieve coordination in a fragmented SC. The equilibrium results assist managers to optimise the SC based on the two-period contract model. The results obtained in this study can help the decision-makers to take decisions on investment in the ambidextrous sustainable innovation under different types of contract structures.
2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE 2021)

On 27 March 2021, the 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE 2021) was officially held in Nanchang, China. The Conference invited the IEEE Fellow Professor Guo Yong-xin from the National University of Singapore and the IET Fellow Professor Gao Liang from Huazhong University of Science and Technology of China, as well as other experts, to make the special speeches. The conference focused on the practical application of big data, the development of artificial intelligence and the innovation of Internet of things technology, and the conference provided a platform for academic exchanges among experts and, the experts present reported their own research progress and made prospects for the future development of big data such as application of big data in enterprise decision making, artificial intelligence such as intelligent endoscope in medicine, Internet of Things such as the Internet of Vehicles in urban transport.
A brief physiological note on helmeted guinea fowl (Numida meleagris) reared in intensive rearing system in Mizoram

A total of 12 helmeted guinea fowl (Numida meleagris) consisting of 3 males and 9 females were reared in intensive rearing system for one year. The birds were in the age group of 8-9 months. Physiological parameters viz. respiration rate, pulse rate and cloacal temperature were recorded in the early morning in the interval of two months. Blood samples were collected from wing vein for haematology in the interval of three months. The average body weight was 1.53±0.05 kg. Respiration rate, pulse rate and cloacal temperature were 70.71±4.85 breaths/min, 301.60±11.96 beats/min and 41.92±0.19 C respectively. Haemoglobin, total erythrocyte count, total leukocyte count and packed cell volume were 11.92±0.36 g/dl, 3.07±0.24 million/μl, 29.83±0.62 thousand/μl and 41.92±2.33% respectively. The study indicated normal physiological and haematological parameters of guinea fowl reared in the intensive rearing system. Physiological indices and haematological findings indicated adaptive mechanisms of helmeted guinea fowl to higher altitude.
Criteria for algorithmic fairness metric selection under different supervised classification scenarios

The research community, (supra-)national institutions, and regular users have noticed that Artificial Intelligence and Machine Learning algorithms can amplify existing inequity between groups. One way to limit this is to use group fairness metrics to measure inequity, optimise and select models. However, there are many different group fairness metrics. Here I combined a clustering of metrics (as done by Friedler et al. in their 2019 paper "A comparative study of fairness-enhancing interventions in machine learning" and by Miron et al. in their 2020 paper "Addressing multiple metrics of group fairness in data-driven decision making") and expert-driven recommendations (from a case study by Rodolfa et al., published in 2020: "Case study: Predictive Fairness to Reduce Misdemeanor Recidivism Through Social Service Interventions") to select fairness metrics. Although this clustering was not consistent, it enabled fairness metric selection and fostered general recommendations on the matter: an algorithm designer should extensively study their algorithm’s application context and explicitly justify their choices relative to fairness. So long as there is no absolute guide to metric selection, this should help nourish an ongoing and context-specific discussion on algorithmic fairness, within and outside of the research community.
On energy management optimization for microgrids enriched with renewable energy sources

The current electric power system is facing the challenges of environmental protection, increasing global electricity demand, high reliability requirement, cleanliness of energy, and planning restrictions. To evolve towards green and smart electric power system, centralized generating facilities are now being transformed into smaller and more distributed generations. As a consequence, the concept of microgrid emerges, where a microgrid can operate as a single controllable system and can be assumed as a cluster of loads and distributed energy resources, which may include many renewable energy sources and energy storage systems. The energy management of large numbers of distributed energy resources is needed for reliable operation of microgrid system. Therefore, energy management is the fundamental part of the microgrid operation for economical and sustainable development. In this regard, this thesis focuses on proposing energy management optimization models for optimal operation of microgrid system that include proposed practical Li-ion battery degradation cost model. These different energy management models include objective functions of operating cost of distributed generators, emission cost of conventional generation source, maximum utilization of renewable energy sources, battery degradation cost, demand response incentives, and load shedding penalization cost, with microgrid component and physical network constraints. A comprehensive conceptual seven layer model is also developed to provide standardized insights in implementing real transactive energy systems.
COMPUTER AIDED CANCER CELL DETECTION USING IMAGE PROCESSING TECHNIQUE – A REVIEW

In recent years the image processing mechanisms are widely used in several medical areas for improving earlier detection and treatment stages particularly in various types of cancer. Image processing is a method to perform some operations on an image in order to get an enhanced image or to extract some useful information from it. Cancer is a most dreadful disease in this era. Time is a major factor in cancer treatment because the mortality rate and spreading of cancer is high compared to other diseases. So more focus must be given on this area. The intention of this bibliographic review is to provide researchers deciding to work and easily understand the situation to find out better approach among them.
Comparison between Linear Regression, Support Vector Machines, and Neural Networks in Forecasting the Number of Foreign Tourist Visits to Indonesia

- The tourism sector is one of the largest foreign exchange contributors to the country's economic growth. One of the determining factors for tourism development is the number of foreign tourist arrivals. It is necessary to carry out development, planning strategies that appropriate, effective, and efficient to increase foreign tourist arrivals. Business Intelligence is one of the digital trends and the main digital marketing strategy for Digital Tourism by implementing data mining for data analysis, including forecasting. There are various kinds of forecasting methods, but the problem is in choosing the best method for the problem and the desired output. This study aims to compare the accuracy between Linear Regression, Support Vector Machine (SVM), and Neural Network methods to forecast the number of foreign tourist arrivals to Indonesia using RapidMiner Studio and parameter of Root Mean Square Error (RMSE) to compare the accuracy of each method. The results of this study indicate the RMSE value of the SVM has the smallest RMSE value compared to the other two methods. SVM is used to make a forecast for the next period which can be used as a basis for the government and the tourism industry in making decisions.
UvA-DARE ( Digital Academic Repository ) Childhood social anxiety : What ’ s next ? Exploring the role of cognitions , depression , parents , and peers

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[S OLUTION ] D ONUT P AXOS : A R ECONFIGURABLE C ONSENSUS P ROTOCOL

State machine replication protocols, like MultiPaxos and Raft, are at the heart of numerous distributed systems. To tolerate machine failures, these protocols must replace failed machines with new machines, a process known as reconﬁgu-ration. Reconﬁguration has become increasingly important over time as the need for frequent reconﬁguration has grown. Despite this, reconﬁguration has largely been neglected in the literature. In this paper, we present Donut Paxos and Donut MultiPaxos, a reconﬁgurable consensus and state machine replication protocol respectively. Our protocols can perform a reconﬁguration with little to no impact on the latency or throughput of command processing; they can perform a recon-ﬁguration in a few milliseconds; and they present a framework that can be generalized to other replication protocols in a way that previous reconﬁguration techniques can not. We provide proofs of correctness for the protocols and optimizations, and present empirical results from an open source implementation.
Reinforcement Learning Based Adaptive Walking Assistance Control of a Lower Limb Exoskeleton

Lower limb exoskeleton for people suffering from unilateral weakness or even hemiparesis has attracted considerable interest in recent years. For walking assistance in such scenarios, the exoskeleton is expected to help generate symmetrically coordinated motion with the unaffected side. However, control strategies remains a challenge as the exoskeleton system is an under-actuated system with some degree of freedoms remains passive and it includes human-machine interaction making it more complex than fully actuated robotic systems where classical PID controller can achieve satisfying performance. In this project, we propose a reinforcement learning based controller to provide walking assistance based on a modeled leaderfollower system where the task can be reformulated as a motion trajectory tracking problem. The proposed control method is validated in simulation platform with data obtained from a healthy subject imitating hemiplegia patients with a pre-developed set of single-side lower limb exoskeleton. A video demonstration of this project is: RL based adaptive walking assistance control of a lower limb exoskeleton
Savings poverty trap and introduction of car-stuck effect to reduce poverty

Poverty is the curse of any economy and it can diminish the growth and development of an economy. Due to massive poverty, the poverty trap has been created and if a country stuck in this trap then it is very tough to overcome until the government will take the proper policy. There are several types of poverty trap like healthbased poverty trap, technology-based poverty trap and here I have included a new type of poverty trap that is savings poverty trap and here due to the savings the poor have stuck in the trap. So, how this savings poverty trap has been created, it will be demonstrated in this paper and at the last portion the policy to extract the poor people from the trap will be discussed and it will be discussed by a new effect that is named as car-stuck effect by which it will be shown that without taking aid or grant from developed country or World Bank, IMF, the task is very difficult for government and this will be compared with a car which stuck in the massive mud.
Information Gains from using Short‐Dated Options for Measuring and Forecasting Volatility
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We study the gains from using short-dated options for volatility measurement and forecasting. Using option portfolios, we estimate nonparametrically spot volatility under weak assumptions for the underlying asset. This volatility estimator complements existing ones constructed from high-frequency returns. We show empirically, using the market index and Dow 30 stocks, that combining optimally return and option data can lead to nontrivial gains for volatility forecasting. These gains are due to “diversification” of the measurement error in the two volatility proxies. The information content of short-dated options, not spanned by the current spot volatility, is of limited relevance for volatility forecasting.
Formation reachability area as a data vector using a dynamic model for controlling information processes in the automated control system for moving objects

This article considers the reachability area formation as a data vector at different points in time. To solve this problem, a new dynamic model for information processes control was developed, which includes the processes of receiving, transmitting and processing information in the automated control system for moving objects. Mathworks Matlab was applied for developing this dynamic model. It is noted that the developed dynamic model can be modified and presented as a dynamic model for the modernization/planning/operation process. The article provides an algorithm for the reachability area formation as a data vector. The reachable area formation as a data vector is necessary to solve the task of assessing and analyzing the quality indicators of automated control system for moving objects, which will increase the efficiency and validity of control decisions related to the configuration (reconfiguration) of the structures of automated control system for moving objects in dynamically changing conditions. In this research reachability areas will be considered in the space of system-technical parameters, which is formed not on the basis of physical laws, but by the logic of data processing.