Miracle-dz/UniFAR-FTUs huggingface dataset:
The Zero Emissions Commitment Model Intercomparison Project (ZECMIP) contribution to C4MIP: quantifying committed climate changes following zero carbon emissions

Abstract. The amount of additional future temperature change following a complete cessation of CO2 emissions is a measure of the unrealized warming to which we are committed due to CO2 already emitted to the atmosphere. This “zero emissions commitment” (ZEC) is also an important quantity when estimating the remaining carbon budget – a limit on the total amount of CO2 emissions consistent with limiting global mean temperature at a particular level. In the recent IPCC Special Report on Global Warming of 1.5 ∘C, the carbon budget framework used to calculate the remaining carbon budget for 1.5 ∘C included the assumption that the ZEC due to CO2 emissions is negligible and close to zero. Previous research has shown significant uncertainty even in the sign of the ZEC. To close this knowledge gap, we propose the Zero Emissions Commitment Model Intercomparison Project (ZECMIP), which will quantify the amount of unrealized temperature change that occurs after CO2 emissions cease and investigate the geophysical drivers behind this climate response. Quantitative information on ZEC is a key gap in our knowledge, and one that will not be addressed by currently planned CMIP6 simulations, yet it is crucial for verifying whether carbon budgets need to be adjusted to account for any unrealized temperature change resulting from past CO2 emissions. We request only one top-priority simulation from comprehensive general circulation Earth system models (ESMs) and Earth system models of intermediate complexity (EMICs) – a branch from the 1 % CO2 run with CO2 emissions set to zero at the point of 1000 PgC of total CO2 emissions in the simulation – with the possibility for additional simulations, if resources allow. ZECMIP is part of CMIP6, under joint sponsorship by C4MIP and CDRMIP, with associated experiment names to enable data submissions to the Earth System Grid Federation. All data will be published and made freely available.
ADAPTIVE MULTIUSER DETECTION ALGORITHM BASED ON SUBSPACE TRACKING

MultiUser Detection (MUD) [1] is the key technology against interference in CDMA communication system. In real CDMA communication system, correlation of signals exists, which is named Multiple Access Interference (MAI). MAI produced by several users is small, but with the increase of users or the power of signal, MAI becomes the central problem in CDMA communication system. According to classical direct-sequence spread-spectrum theory, traditional detector filters signal of each user according to its signature waveform, which results in bad performance against MAI. Based on traditional detection technique, the multiuser detection detects desire user by utilizing all information causing MAI fully. Thus MUD has fine performance against MAI, solves the near-far effect, and reduces the requirement for precision of controlling power. So spectral resources can be utilized more effectively, which improves the communication capacity notably. Over the past more than ten years, various detection techniques have been proposed. The main multiuser detector conclude optimum detector [2], linear decorrelating detector [3], MMSE linear multiuser detector [4], multistage interference cancellation [5], decision-feedback detector [6], and detector based on neural network, etc. Reference [7] has proposed an adaptive multiuser detecor based on subspace tracking, which adopts PASTd algorithm to track the subspace. We found that with a random initialization, the convergence is fairly slow. The reason is that approximate estimation of subspace cumulate errors. Reference [7] calculates an initial matrix using SVD, then tracks the subspace with PASTd, which increase the complexity. The paper introduces the SP-1 (Subspace Projection) [8] tracking subspace, which reduces calculation complexity of the algorithm. And performance is equivalent to that of PASTd using initial matrix. The signal subspace estimation is achieved by SP-1 followed by a new demodulating vector which modifies the MMSE detector. 2. Signal Model Consider a synchronous DS-CDMA communication system in additive white Gaussian noise, which is shared by K simultaneous users. At the receiver, chip-matched filtering followed by chip rate sampling yields a N-vector of chip-matched filer output samples within a symbol interval T, which is
Integrated Model Development in Information Technology Adoption

Electronic services have become an important part in the implementation of electronic governance. Government electronic services are an important tool for effective cooperation in any country between government, authorities, citizens, organizations, and so on. The purpose of this research is to identify and collect, analyze and compile various factors that influence the implementation process of electronic government services. The use of e-government is very dependent on how the user, trust in using and adopting the service. Some barriers to adopting e-government services such as lack of trust are considered as one of the main obstacles. In addition, citizens ‘trust in e-government and social influence were found to be significant predictors of citizens’ behavioral intentions to use e-government services. This study contributes by providing a conceptual model that is useful for studying citizen trust and the behavior of using e-government services in Indonesia.
Resolving a common vulnerability in secret sharing scheme–based data outsourcing schemes

Every day, the amount of generated data from different resources are increasing significantly and posing new serious challenges in terms of data storage and maintaining. As a solution, outsourcing data to a public cloud server with high storage and processing capacity sounds reasonable in comparison with storing in local data storage. Therefore, the database as a service (DaaS) paradigm has gained much popularity over the last decade since the introduction of cloud services. Storing confidential data into a third party arises some concerns regarding security and privacy of the data. One popular approach to address these problems in data outsourcing is utilizing secret sharing and distributing sensitive data among several databases. In this paper, we introduce a flaw that exhibits a deficiency relating to current schemes. This flaw indicates an intrinsic vulnerability to such methods and necessitates a reconsideration of these schemes. Moreover, a new way will be developed to resolve this problem in the previous schemes, which can be employed in all current schemes to mitigate the mentioned concern. Finally, a comprehensive implementation demonstrates that the proposed method is scalable to meet increasing demands in large databases.
Profit Maximization in 5G+ Networks with Heterogeneous Aerial and Ground Base Stations

In this paper, we propose a novel framework for 5G and beyond (5G+) heterogeneous wireless networks consisting of macro aerial base stations (MABSs), small aerial base stations (SABSs), and ground base stations (GBSs) with two types of access technologies: power domain non-orthogonal multiple access (PD-NOMA) and orthogonal frequency-division multiple access (OFDMA). We aim to maximize the total network profit under some practical network constraints, e.g., NOMA and OFDMA limitations, transmit power (TP) maximum limits, and isolation of the virtualized wireless network. We formulate the resource allocation problem encompassing joint TP allocation, ABS altitude determination, user association, and sub-carrier allocation parameters. Our optimization problem is mixed integer non-linear programming (MINLP) with high computational complexity. To propose a practical approach with reduced computational complexity, we use an alternate method where the main optimization is broken down into three sub-problems with lower computational complexity. We do this by adopting successive convex approximation (SCA), geometric programming (GP), and mesh adaptive direct search (MADS) to solve each of the resulting problems, and find power allocation, altitudes of ABSs, and assignment parameters, respectively. Simulation results reveal that our proposed scenario can improve the overall network profit by up to 47 percent compared to the case where the TPs and ABS altitudes are fixed. Besides, finding the ABS altitude with fixed TPs can improve the network profit by 20 percent compared to the power allocation case with fixed ABS altitudes. Our proposed heterogeneous approach improves the network profit by up to 18, 16, 15, and 10 percent in suburban, urban, dense urban, and high-rise urban environments, respectively, compared to the cases with homogeneous ABSs.
Image Retrieval using GA Optimized Gabor Filter

Objective: A Hybrid content based image retrieval method is proposed in this paper. This method extracts color, tuned texture and shape features of the images in three successive phases. Methodology: In proposed system, color features are extracted using color histogram method in the first phase. The tuned texture features are extracted by employing GA optimized Gabor filters in second phase. Finally, shape features are extracted using the polygonal fitting algorithm. The best match output images of each phase are given as input images to the next phase to obtain ‘S’ best match images out of ‘N’ database images. Findings: The novelty of proposed system is that it employs a tunable filter that is tuned with the query image dynamically. The tuning of Gabor filter is implemented using GA in second phase. The proposed method shows improved retrieval rate in terms of average recall and average precision compared to the existing systems. The computation complexity is also found to be less than other existing methods. Applications: It can be employed in numerous fields such as medical, satellite, multimedia, and surveillance imaging systems, etc. where the retrieval of related images from huge databases is critical task for analysis.
A Julia package for bilevel optimization problems

Mathematical optimization is the discipline dealing with the determination of the best (or almost best) decision with respect to a specific cost function and to a set of constraints on the decision. Bilevel optimization is a class of mathematical optimization problems with the optimality conditions of a lower-level problem embedded in the constraints. BilevelOptimization.jl is a toolbox built on top of the JuMP.jl ecosystem for mathematical optimization (Dunning, Huchette, & Lubin, 2017). Bilevel optimization is used to tackle various problems in areas such as power systems, security applications, network design or market equilibria. See Dempe (2018) for an overview of applications and recent formulations and theoretical progress.
An integrated M-S-QUAL and importance-performance analysis approach for assessing service quality of mobile commerce application

In this globalization era, the Internet has arguably influenced the daily business of every sector, especially service sector. As a consequence, most of the current services have been changed from the conventional way with an emergence of the Internet to the electronic commerce (e-commerce). With the convenience of using handled devices, such as Smartphone and tablets, the emergence of mobile commerce (m-commerce) application becomes a demand. As time goes by, an intense competition among the mobile service providers is inevitable, thus, all providers seek to improve their service qualities in order to achieve customer satisfaction. The present research attempted to assess the service quality of the existing m-commerce using the M-S-QUAL scale and combine it with the importance-performance analysis (IPA) model. A case study to exhibit the applicability of the proposed methods was conducted in one of the most popular m-commerce application in Indonesia. This study is expected to provide valuable insights for the managers concerning with the attributes of the service quality that reflect the customers’ perception in order to do continuous improvement.In this globalization era, the Internet has arguably influenced the daily business of every sector, especially service sector. As a consequence, most of the current services have been changed from the conventional way with an emergence of the Internet to the electronic commerce (e-commerce). With the convenience of using handled devices, such as Smartphone and tablets, the emergence of mobile commerce (m-commerce) application becomes a demand. As time goes by, an intense competition among the mobile service providers is inevitable, thus, all providers seek to improve their service qualities in order to achieve customer satisfaction. The present research attempted to assess the service quality of the existing m-commerce using the M-S-QUAL scale and combine it with the importance-performance analysis (IPA) model. A case study to exhibit the applicability of the proposed methods was conducted in one of the most popular m-commerce application in Indonesia. This study is expected to provide valuable insights f.
Facilitate Access ability and Provide Security for Handicap Person Computers

Simple Human computer interaction (HCI) is typically easily used by normal people to interact with computer like mouse, keyboard, and . etc. However for user suffering from difficulties in hand moving or amputees this HCI barely impossible to use to interact with computer, for this reasons this paper aims to suggestion HCI interface for disability user as well as the normal user to control computer with security system without need to use their fingers. HCI interface in this paper track computer user arms movements using Kinect sensor and processing this movements to move the computer cursor on the computer. This interface has security methodology to control computer files access by tracking the user activity in the computer.
Impact of graph-based features on Bitcoin prices

Predicting the trends in Bitcoin market prices is a very challenging task due to the many uncertainties and variables influencing the market value. The market is susceptible to quick changes, causing seemingly random fluctuations in the Bitcoin price. Due to the chaotic and highly volatile nature of Bitcoin behavior, investments come with high risk. To minimize the risk involved, knowledge of the Bitcoin price movement in the future is desirable. Different studies have shown that Machine Learning algorithms can predict, to varying degrees, the price fluctuations of Bitcoin. However, most researches do not explore the relationship between the price and other features outside the transaction network, such as market capitalization, Bitcoin mining speed, or entity behavior. Also, most of the features are extracted from the network level, which means obtaining the number of transactions, users, Bitcoins mined, etc. In this research, we focus on additional features, such as features outside the transaction network and node-based features inside the transaction network, which could improve the price prediction of Bitcoin. The investigated features are the “fairness and goodness” measure and the “1-ARW-betweenness cen- trality” measure. Fairness and goodness are entity behavior measures. The goodness of a Bitcoin address captures how much this address is liked/trusted by other addresses, while the fairness of a Bitcoin address captures how fair the address is in rating other addresses’ likeability or trust level. The 1-ARW-betweenness centrality is a feature based on absorbing random walks. The feature captures the extent to which a Bitcoin address has control over the money flow between different addresses. A benchmark, based on the machine learning algorithm Random Forest with commonly used features, is used to test the impact of the additional features. The Random Forest tries to predict the sign (up-down movement) of the price per day, using data from the two previous days. Comparing this benchmark with a similar model, but then including the additional features, will gain more information about how these additional features influence the Bitcoin price.
A Reconfigurable Cross-Connected Wireless-Power Transceiver for Bidirectional Device-to-Device Wireless Charging

This paper presents a reconfigurable cross-connected (CC) wireless power transceiver (TRX) operating at 6.78 MHz and implemented for bidirectional device-to-device (D2D) charging with high efficiency and small system volume. We propose, for the first time, a CC topology applied to a differential class-D power amplifier for significant switching loss reduction. Two delay-locked loops (DLLs) for each power NMOS transistor form the reconfigurable controller, realizing the adaptive deadtime control in the transmitter (TX) mode and the off-delay compensation in the receiver (RX) mode, leading to high power conversion efficiency and safe operation. To realize the inductive load for the TX mode in the whole coupling range at the resonant frequency, we use an on-chip tunable capacitor. Furthermore, we also study the power link efficiency in the D2D direct charging system and verify that the near-maximum power link efficiency can be inherently achieved in this scenario. The proposed wireless power TRX, fabricated in a 0.35- $\mu \text{m}$ CMOS process with 5-V devices, measured a peak D2D total efficiency of 77.2% when the output power is 0.7 W. The maximum charging power is 2.74 W with a total efficiency of 62.7% and the maximum transmission distance is 24 mm, both measured.
Comparing Programming Languages used in AWS Lambda for Serverless Architecture

Author Title Number of Pages Date Sachin Shrestha Comparing Programming Language used in AWS Lambda for Serverless Architecture 45 pages + 1 appendices 19 June 2019 Degree Bachelor of Engineering Degree Programme Information Technology Professional Major Software Engineering Instructors Janne Salonen, Head of Department (ICT) at Helsinki Metropolia University of Applied Sciences Natalia Garnova, Team lead of activation team at Zervant Oy The primary purpose of this thesis was to explore AWS Lambda service and its features provided by Amazon.com, Inc. The benefits and importance of this service which adapts the principles of serverless computing are discussed. This service has been used in case company to manage infrastructure that hosts and executes codes. The task of such service includes sizing the available provision and scale multiple servers while managing operating system updates, apply security patches and then monitor all the resources for performance and availability. The secondary purpose is to research in detail about the native programming languages supported by AWS Lambda and their respective implementation methods. They are popular languages in technological field and widely implemented everywhere. These languages are compared under the characteristics of availability of compilers and tools, reusability, efficiency, familiarity, reliability and readability. The specification of each language is studied, and the project report is constructed by providing same test case for all natively supported language separately multiple times. The analytics result of individual performance under project case was documented and observed to form a general conclusion. It was observed that there is subtle difference in performance among the language when tested in common environment. In terms of speed of execution, interpreted languages performed better than compiled language. While performing the cold start process, C# was slower than other languages, but the difference was in milliseconds. Java had the largest package size compared to others. This thesis can be useful to get detailed information about AWS Lambda service and know why it is an important realization of serverless architecture. This study also discusses the benefits of serverless architecture over traditional systems on the grounds of efficiency, performance and cost of production and maintenance. The options to integrate AWS Lambda with other services provided by AWS is the main reason for its popularity. The language can be chosen depending upon the requirement of application service model and developer’s preference.
Algorithm for Automated Selection and Verification of Instrument Current Transformers in the Electrical Substations CAD System

One of the most labor-intensive stages of the design of a substation is the selection and testing of electrical equipment. This is due to the need to perform large volumes of uniform calculations. Therefore, the urgent task is to develop computer-aided design systems that allow performing such calculations. This paper is devoted to the development of an algorithm for automated testing of measuring current transformers. The algorithm proposed by the authors makes it possible to automate the execution of calculations related to the verification of the working conditions of measuring transformers in heavy and emergency modes. The algorithm also allows you to test current transformers for the secondary load. The developed algorithm takes into account not only the nominal parameters of the measuring transformer, but also its design, connection scheme, voltage class and type of connection on which it will be used.
Exploring the use of light threads to improve the instruction level parallelism

One of the main components of any general-purpose machine is the microprocessor, this component can be found at the heart of every machine: from standard servers and high performance computing nodes to portable mobile platforms. Its main task is to correctly execute pro- grams as fast as it can, having the production cost and consumption as border conditions. The research in processor architectures centers in optimizing the pro- cessor design according to the specied functionality and having into account present and future technologies. This optimization can be based on dierent parameters: performance, consumption, production cost, sur- face. In this work, we propose a novel mechanism combining software and hardware that allows to improve the Instruction Level Parallelism using simple cores and light threads. A modied processor is implemented using a simulation tool and two examples are presented: sorting an array and ltering a matrix. In both cases, promising results are obtained.
Device-free human localization and activity recognition for supporting the independent living of the elderly

Given the continuous growth of the aging population, the cost of health care, and the preference that the elderly want to live independently and safely at their own homes, the demand on developing an innovative living-assistive system to facilitate the independent living for the elderly is becoming increasingly urgent. This novel system is envisioned to be device-free, intelligent, and maintenance-free as well as deployable in a residential environment. The key to realizing such envisioned system is to study low cost sensor technologies that are practical for device-free human indoor localization and activity recognition, particularly under a clustered residential home. By exploring the latest, low-cost and unobtrusive RFID sensor technology, this thesis intends to design a new device-free system for better supporting the independent living of the elderly. Arising from this live-assistive system, this thesis specifically targets the following six research problems. Firstly, to deal with severe missing readings of passive RFID tags, this thesis proposes a novel tensor-based low-rank sensor reading recovery method, in which we formulate RFID sensor data as a high-dimensional tensor that can naturally preserve sensors’ spatial and temporal information. Secondly, by purely using passive RFID hardware, we build a novel data-driven device-free localization and tracking system. We formulate human localization problem as finding a location with the maximum posterior probability given the observed RSSIs (Received Signal Strength Indicator) from passive RFID tags. For tracking a moving target, we mathematically model the task as searching a location sequence with the most likelihood under a Hidden Markov Model (HMM) framework. Thirdly, to tackle