Reviewerly/exHarmony huggingface dataset:
Systematic Review Study of Decision Trees based Software Development Effort Estimation

The role of decision trees in software development effort estimation (SDEE) has received increased attention across several disciplines in recent years thanks to their power of predicting, their ease of use, and understanding.Furthermore, there are a large number of published studies that investigated the use of a decision tree (DT) techniques in SDEE.Nevertheless, in reviewing the literature, a systematic literature review (SLR) that assesses the evidence stated on DT techniques is still lacking.The main issues addressed in this paper have been divided into five parts: prediction accuracy, performance comparison, suitable conditions of prediction, the effect of the methods employed in association with DT techniques, and DT tools.To carry out this SLR, we performed an automatic search over five digital libraries for studies published between 1985 and 2019.In general, the results of this SLR revealed that most DT methods outperform many techniques and show an improvement in accuracy when combined with association rules (AR), fuzzy logic (FL), and bagging.Additionally, it has been observed a limited use of DT tools: it is therefore suggested for researchers to develop more DT tools to promote the industrial utilization of DT amongst professionals.
A new replica placement strategy based on multi-objective optimisation for HDFS

Distributed storage systems like the Hadoop distributed file system (HDFS) constitute the core infrastructure of cloud platforms which are well poised to deal with big-data. An optimised HDFS is critical for effective data management in terms of reduced file service time and access latency, improved file availability and system load balancing. Recognising that the file-replication strategy is key to an optimised HDFS, this paper focuses on the file-replica placement strategy while simultaneously considering storage and network load. Firstly, the conflicting relationship between storage and network load is analysed and a bi-objective optimisation model is built, following which a multi-objective optimisation memetic algorithm based on decomposition (MOMAD) and its improved version are used. Compared to the default strategy in HDFS, the file-replica placement strategies based on multi-objective optimisation provide more diverse solutions. And competitive performance could be obtained by the proposed algorithm.
Authentication system for e-certificate by using RSA’s digital signature

Online learning and teaching become the popular channel for all participants, because they can access the courses everywhere with the high-speed internet. E-certificate is being prepared for everyone who has participated or passed the requirements of the courses. Because of many benefits frome-certificate, it may become the demand for intruders to counterfeit the certificate. In this paper, Rivest-Shamir-Adleman (RSA)'s digital signature is chosen to signe-certificate in order to avoid being counterfeited by intruders. There are two applications to managee-certificate. The first application is the signing application to sign the sub image including only participant's name in e-certificate. In general, the file of digital signature is divided frome-certificate. That means, both of them must be selected to compare each other in checking application. In fact, the solution will be approved when each pixel of participant's name is equal to each part from the decrypted message at the same position. In experimental session, 40 e-certificatesare chosen for the implementation. The results reveal that the accuracy is 100% and both of signing and checking processes are completed rapidly fast, especially when signing application is applied with Chinese remainder theorem (CRT) or the special technique of CRT. Therefore, the proposed method is one of the best solutions to protect e-certificate from the forgery by intruders.
Classification and Regression via Integer Optimization for Neighborhood Change

This article applies a method we term “predictive clustering” to cluster neighborhoods. Much of the literature in this direction is based on groupings built using intrinsic characteristics of each observation. Our approach departs from this framework by delineating clusters based on how the neighborhood’s features respond to a particular outcome of interest (e.g., income change). To do so, we leverage a classification and regression via integer optimization (CRIO) method that groups neighborhoods according to their predictive characteristics and consistently outperforms traditional clustering methods along several metrics. The CRIO methodology contributes a novel methodological and conceptual capability to the literature on neighborhood dynamics that can provide useful insights for policymaking.
CsiIBS: A post-quantum identity-based signature scheme based on isogenies

Digital signatures are important cryptographic primitive for authentication. To resist quantum attacks, many post-quantum signature schemes have been proposed. Among them, isogeny-based signature schemes, such as SeaSign, rapid development in recent years along with the proposed CSIDH construction. In this paper, inspired by the Fiat-Shamir transform, we construct the first identity-based signature scheme based on isogenies from the isogeny-based identification scheme. Then, we analyze its security in the random oracle model under the hardness of the isogeny problem, and demonstrate that it achieves the required security properties. Finally, we evaluate the performance and give the corresponding computational and storage costs.
Self-organization technology of student’s mathematical activities based on intelligent management

Е. И. Смирнов, С
µ-toksia: An Efficient Abstract Argumentation Reasoner

We describe the µ-toksia argumentation reasoning system. The system supports a range of different reasoning tasks over both standard and dynamic abstract argumentation frameworks under essentially all central argumentation semantics, covering all tracks and reasoning tasks considered in the most recent International Competition on Computational Models of Argumentation (ICCMA 2019). µ-toksia ranked first in all reasoning tasks in the main track of ICCMA 2019, and has been shown to scale noticeably better on the dynamic track tasks than its current competitors. In this paper, we provide an overview of µ-toksia and its algorithmic and implementation-level details, and provide further empirical evidence beyond ICCMA 2019 on the efficiency of µ-toksia compared to related systems.
Bloch-like superoscillations and unidirectional motion of phase-driven quantum walkers

We study the dynamics of a quantum walker simultaneously subjected to time-independent and -dependent phases. Such dynamics emulates a charged quantum particle in a lattice subjected to a superposition of static and harmonic electric fields. With proper settings, we investigate the possibility to induce Bloch-like superoscillations, resulting from a close tuning of the frequency of the harmonic phase $\ensuremath{\omega}$ and that associated with the regular Bloch-like oscillations ${\ensuremath{\omega}}_{B}$. By exploring the frequency spectra of the wave-packet centroid, we are able to distinguish the regimes on which regular and super-Bloch oscillations are predominant. Furthermore, we show that under exact resonant conditions $\ensuremath{\omega}={\ensuremath{\omega}}_{B}$ unidirectional motion is established with the wave-packet average velocity being a function of the quantum coin operator parameter, the relative strengths of the static and harmonic terms, as well as the own phase of the harmonic field. We show that the average drift velocity can be well described within a continuous-time analogous model.
An Authorized Public Auditing Scheme for Dynamic Big Data Storage in Cloud Computing

Nowadays, to utilize the abundant resources of cloud computing, most enterprise users prefer to store their big data on cloud servers for sharing and utilization.However, storing data in remote cloud servers is out of user's control and exposes to lots of security problems such data availability, unauthorized access and data integrity, among which data integrity is a challenging and urgent task in cloud computing.Many auditing schemes have been proposed to check the integrity of data in cloud, but these schemes usually have some disadvantages.One is that these auditing schemes cannot check which block is corrupt when the data is not integrated.The other is that there's no efficient authenticated data structure helping to achieve accurate auditing when the data needs to update frequently.To solve the problems, we propose a public auditing scheme for dynamic big data storage in cloud computing.Firstly, we design a dynamic index table, in which no elements need to be moved in insertion or deletion update operations.Secondly, when data in cloud is not integrated, the third-party auditor can detect which block is corrupt.Finally, an authorization is employed between the third party and cloud servers to prevent denial of service attack.The theoretical analysis and the simulation results demonstrate that our scheme is more secure and efficient.
An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection

Finding an optimal set of discriminative features is still a crucial but challenging task in biomedical science. The complexity of the task is intensified when any of the two scenarios arise: a highly dimensioned dataset and a small sample-sized dataset. The first scenario poses a big challenge to existing machine learning approaches since the search space for identifying the most relevant feature subset is so diverse to be explored quickly while utilizing minimal computational resources. On the other hand, the second aspect poses a challenge of too few samples to learn from. Though many hybrid metaheuristic approaches (i.e., combining multiple search algorithms) have been proposed in the literature to address these challenges with very attractive performance compared to their counterpart standard standalone metaheuristics, more superior hybrid approaches can be achieved if the individual metaheuristics within the proposed hybrid algorithms are improved prior to the hybridization. Motivated by this, we propose a new hybrid Excited- (E-) Adaptive Cuckoo Search- (ACS-) Intensification Dedicated Grey Wolf Optimization (IDGWO), i.e., EACSIDGWO. EACSIDGWO is an algorithm where the step size of ACS and the nonlinear control strategy of parameter <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mover accent="true"><mml:mi>a</mml:mi><mml:mo stretchy="true">→</mml:mo></mml:mover></mml:math> of the IDGWO are innovatively made adaptive via the concept of the complete voltage and current responses of a direct current (DC) excited resistor-capacitor (RC) circuit. Since the population has a higher diversity at early stages of the proposed EACSIDGWO algorithm, both the ACS and IDGWO are jointly involved in local exploitation. On the other hand, to enhance mature convergence at latter stages of the proposed algorithm, the role of ACS is switched to global exploration while the IDGWO is still left conducting the local exploitation. To prove that the proposed algorithm is superior in providing a good learning from fewer instances and an optimal feature selection from information-rich biomedical data, all these while maintaining a high classification accuracy of the data, the EACSIDGWO is employed to solve the feature selection problem. The EACSIDGWO as a feature selector is tested on six standard biomedical datasets from the University of California at Irvine (UCI) repository. The experimental results are compared with the state-of-the-art feature selection techniques, including binary ant-colony optimization (BACO), binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), and extended binary cuckoo search algorithm (EBCSA). These results reveal that the EACSIDGWO has comprehensive superiority in tackling the feature selection problem, which proves the capability of the proposed algorithm in solving real-world complex problems. Furthermore, the superiority of the proposed algorithm is proved via various numerical techniques like ranking methods and statistical analysis.
An An Approach to Measure the Death Impact of Covid-19 in Jakarta using Autoregressive Integrated Moving Average (ARIMA)

Coronavirus disease 2019 (COVID-19) is a pandemic in more than 200 countries around the world. As the fourth most populous nation in the world, Indonesia is predicted to face a big threat to this pandemic particularly Jakarta as the epicenter of the virus in Indonesia. However, the nature of COVID-19 that can easily spread and also many undetected cases that do not present symptoms make it more difficult to determine the real mortality effects of COVID-19.The deaths in Jakarta from the new coronavirus may be higher than officially reported. To overcome this issue, this paper will provide an approach to measure the death impact of COVID-19 using the Autoregressive Integrated Moving Average model (ARIMA). The model will predict the ‘what if’ normal condition of the number of funerals in Jakarta compared to the real situation in March 2020 as an approach of the actual effect of COVID-19 in Jakarta. This research revealed a discrepancy of 450-1070 funerals in March 2020 that could not be predicted by the ARIMA model. This funeral gap, a forecast error, could be an approach to the potential number of possible death impacts of COVID-19 in Jakarta that should be significantly higher than the report. The people should be more conscious and alert of COVID-19 situation.
On the Reversibility of Actions in Planning

Checking whether action effects can be undone is an important question for determining, for instance, whether a planning task has dead-ends. In this paper, we investigate the reversibility of actions, that is, when the effects of an action can be reverted by applying other actions, in order to return to the original state. We propose a broad notion of reversibility that generalizes previously defined versions and investigate interesting properties and relevant restrictions. In particular, we propose the concept of uniform reversibility that guarantees that an action can be reverted independently of the state in which the action was applied, using a so-called reverse plan. In addition, we perform an in-depth investigation of the computational complexity of deciding action reversibility. We show that reversibility checking with polynomial-length reverse plans is harder than polynomial-length planning and that, in case of unrestricted plan length, the PSPACE-hardness of planning is inherited. In order to deal with the high complexity of solving these tasks, we then propose several incomplete algorithms that may be used to compute reverse plans for a relevant subset of states.
Comparing Weak Admissibility Semantics to their Dung-style Counterparts -- Reduct, Modularization, and Strong Equivalence in Abstract Argumentation

Semantics based on weak admissibility were recently introduced to overcome a problem with self-defeating arguments that has not been solved for more than 25 years. The recursive definition of weak admissibility mainly relies on the notion of a reduct regarding a set E which only contains arguments which are neither in E, nor attacked by E. At first glance the reduct seems to be tailored for the weaker versions of Dung-style semantics only. In this paper we show that standard Dung semantics can be naturally reformulated using the reduct revealing that this concept is already implicit. We further identify a new abstract principle for semantics, so-called modularization describing how to obtain further extensions given an initial one. Its importance for the study of abstract argumentation semantics is shown by its ability to alternatively characterize classical and non-classical semantics. Moreover, we tackle the notion of strong equivalence via characterizing kernels and give a complete classification of the weak versions regarding well-known properties and postulates known from the literature.
Noise-Aware and Light-Weight VLSI Design of Bilateral Filter for Robust and Fast Image Denoising in Mobile Systems

The range kernel of bilateral filter degrades image quality unintentionally in real environments because the pixel intensity varies randomly due to the noise that is generated in image sensors. Furthermore, the range kernel increases the complexity due to the comparisons with neighboring pixels and the multiplications with the corresponding weights. In this paper, we propose a noise-aware range kernel, which estimates noise using an intensity difference-based image noise model and dynamically adjusts weights according to the estimated noise, in order to alleviate the quality degradation of bilateral filters by noise. In addition, to significantly reduce the complexity, an approximation scheme is introduced, which converts the proposed noise-aware range kernel into a binary kernel while using the statistical hypothesis test method. Finally, blue a fully parallelized and pipelined very-large-scale integration (VLSI) architecture of a noise-aware bilateral filter (NABF) that is based on the proposed binary range kernel is presented, which was successfully implemented in field-programmable gate array (FPGA). The experimental results show that the proposed NABF is more robust to noise than the conventional bilateral filter under various noise conditions. Furthermore, the proposed VLSI design of the NABF achieves 10.5 and 95.7 times higher throughput and uses 63.6-97.5% less internal memory than state-of-the-art bilateral filter designs.
On the Geochemistry and Mineralogy of the Anthropocene

Demand (BOD 5 ) by capturing particulate organic matter but does not reduce the soluble fraction of raw influent organics.There is a current industry trend toward the use of screens and filter devices for enhanced capture/diversion of raw solids, which can achieve removals of up to 60-80 percent of particulate organics, but similarly do not reduce the soluble fraction [4].Biosorption can be used to remove the soluble fraction.It is used in various industries for the removal of heavy metals [5] and organic pollutants [6], and is a fundamental cellular-level step in the activated sludge process prior to biochemical oxidation.Primary biosorption is a physico-chemical process in which Waste Activated Sludge (WAS), the sorbent, is mixed with Raw Wastewater (RW) in a small contactor where the particulate and soluble organic matters sorb onto/into the AS flocs/cells.The amount of sorption should be dependent on operational/physiological factors such as Dissolved Oxygen (DO) concentration, contact time, WASto-RW ratio, floc structure, sludge age/type, and/or the amount/ characteristics of Extracellular Polymeric Substances (EPS).Research has found that the colloidal fraction of organic matter is targeted significantly more during biosorption than the particulate and truly soluble fractions [7].It is assumed that this is because soluble matter diffuses into the cells while colloidal matter sticks to
Adding filters to improve reservoir computer performance

Reservoir computers are a type of neuromorphic computer that may be built with analog hardware, potentially creating powerful computers that are small, light and consume little power. Typically a reservoir computer is build by connecting together a set of nonlinear nodes into a network; connecting the nonlinear nodes may be difficult or expensive, however. This work shows how a reservoir computer may be expanded by adding functions to its output. The particular functions described here are linear filters, but other functions are possible. The design and construction of linear filters are well known, and such filters may be easily implemented in hardware such as field programmable gate arrays (FPGA’s). The effect of adding filters on the reservoir computer performance is simulated for a signal fitting problem, a prediction problem and a signal classification problem.
DatalogMTL over the Integer Timeline

We study DatalogMTL—an extension of Datalog with metric temporal operators—under integer semantics, where the temporal domain of both interpretations and temporal operators consists of integer time points only. This is in contrast to the standard semantics, which is defined over the rational timeline. DatalogMTL under integer semantics is an interesting KR language: on the one hand, one can often assume the integer timeline in applications; on the other hand, it captures prominent temporal extensions of Datalog such as Datalog1S. We show that the choice of integer semantics leads to more favourable computational properties. We first show that reasoning over integers is at most as hard as reasoning over rationals for DatalogMTL and its natural fragments. Then, we investigate fragments of DatalogMTL where adopting the integer semantics makes reasoning easier. In particular, we show that complexity drops from P-hard to NC1-complete for the propositional fragment (where all object variables are grounded), and from TC0-hard to ACC0 for the linear fragment where the past diamond operator is the only metric operator allowed in rule bodies. Thus, reasoning in such fragments is both tractable and highly parallelisable, which suggests their appropriateness for data-intensive applications.
Direct solution for fixed source location using well-posed TDOA and FDOA measurements

Based on the time differences of arrival (TDOA) and frequency differences of arrival (FDOA) measurements of the given planar stationary radiation source, the joint TDOA/FDOA location algorithm which solves the location of the target directly is proposed. Compared with weighted least squares (WLS) methods, the proposed algorithm is also suitable for well-posed conditions, and gets rid of the dependence on the constraints of Earth's surface. First of all, the solution formulas are expressed by the radial range. Then substitute it into the equation of the radial range to figure out the radial range between the target and the reference station. Finally use the solution expression of the target location to estimate the location of the target accurately. The proposed algorithm solves the problem that WLS methods have a large positioning error when the number of observation stations is not over-determined. Simulation results show the effectiveness of the proposed algorithm, including effectively increasing the positioning accuracy and reducing the number of observatories.