Reviewerly/exHarmony huggingface dataset:
Evaluating the Software Problem Representation on the Basis of Rationale Trees and User Story Maps: Premises of an Experiment

User stories describe system requirements from the users’ point of view using structured natural language. These artifacts mostly assist the development team in agile projects where sets of them express the expected features of the to-be software. Nevertheless, when faced with large user stories’ sets, further structuring needs to be introduced to make the software problem more understandable/clear. Indeed, the latter can hardly be understood on the basis of huge flat lists of sentences. The present paper describes the premises of an experiment destined to evaluate whether novice modelers can understand the requirements problem better through a visual representation (called Rationale Tree) built out of a user stories’ set. It does so by comparing, on the same objective, the Rationale Tree method to the industry-adopted User Story Mapping technique with a modeling exercise. This was performed with the participation of two student groups where one used the former technique while the other one employed the latter. The ultimate goal of this exercise is to identify the actual performance of the Rational Tree in delivering understandability to the requirements problem (when input is provided in the form of a complex user stories’ set) in order to identify possible bottlenecks and improvement opportunities within the method itself.
Improved Electric Wheelchair Controlled by Head Motion

The electric wheelchairs are designed to assist disabled people. However, for people with disabilities who have a higher degree of impairment, such as quadriplegics, who cannot move any of the body parts except the head, medical devices for them are usually very complex, rare and expensive. In this paper, we improved an electric wheelchair model that is controlled by the motion of the head by using a microcontroller system. The system includes microelectromechanical components: tilt angle sensors and mechanical mechanisms. A new head-motion recognition technique based on processing angular acceleration data. The process control of wheelchair motion has been achieved by using 6050 tilt angle sensor and Arduino family UNO R3 microcontroller. The signal of head movement direction is sent to the microcontroller. Depending on the orientation of the sensor, microcontroller will control wheels, such as turn left, turn right, moving forward or backward with the help of DC motors.
An efficient load balancing using seven stone game optimization in cloud computing

Abstract Cloud computing offers massive processing power to cloud client to solve the scientific, financial forecasting, and weather forecasting applications. The process of distributing to the load to the different cloud service providers is a complex problem. Cloud service providers have different types of virtual machines with different computing power types in multi‐layered architectures. Various optimization works have been proposed to tackle the load balancing problem in cloud service providers. Improving performance in load balancing is a cumbersome task. Seven stone game optimization (SSGO) is designed based on the south Indian seven stone game workflow. The proposed method's foremost ambition is to reduce makespan time and maximize cloud service providers' utilization. The proposed method was simulated, and results demonstrate that minimizes the makespan time and maximizes the resource utilization than the particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), and Tabu search (TS). The experimental results show that the SSGO provides 4% more resource utilization than PSO, 5% more than GA, and 7% more than SA and 10% more than TS.
Flexible Convolution in Scattering Transform and Neural Network

Convolution is an essential component of image processing and computer vision. There are two common approaches to implement convolution: spatial domain-based and frequency domain-based. Which approach is better for image processing? There is not one reasonable answer to all situations but depends on the applied context. The fastest method will depend on the task at hand. In this paper, we (i) evaluate the performance of two convolutional approaches in a 2D image. Then, we (ii) design some neural layers that its output structure parallels to the output of 2D scattering transform. Finally, we (iii) compare the classification results when applying scattering transform and our neural network.
A Memetic Algorithm for Evolving Deep Convolutional Neural Network in Image Classification

As evolutionary algorithms (EAs) are robust to the problem formulation and easy to use, there is a growing interest in designing EAs for automated neural architecture search in recent years. In particular, EvoCNN is a recently proposed evolutionary algorithm to automate the configuration of a deep Convolutional Neural Network (CNN) for image classification. Its efficacy has been confirmed against 22 existing algorithms for CNN configuration, on the widely used image classification tasks. However, despite the success enjoyed by this method, we note that there are several limitations existed in this method. For example, only chain structured network is considered for evolution. Further, there are many decision variables, which is computational expensive. In this paper, we embark a study on evolutionary neural architecture search by proposing a memetic algorithm (MA), with the aim of addressing the problems mentioned above. Particularly, first of all, besides evolving the chain structured network, local search is designed for multibranch network search. Next, to reduce the network parameters for optimization, we focus on the architecture search only on the convolutional layers. Moreover, based on a recent hypothesis in the literature, the network evaluation is conducted based on only the early training process in our proposed MA. To confirm the efficacy of the proposed method, comprehensive empirical studies are conducted against EvoCNN for NAS, on the commonly used image classification benchmarks.
Joint Slot Filling and Intent Detection in Spoken Language Understanding by Hybrid CNN-LSTM Model

We investigate the usage of hybrid convolutional and long- short-term memory neural networks for joint slot filling and intent detection in spoken language understanding. We propose a novel model that combines between convolutional neural networks, for their ability to detect complex features in the input sequences by applying filters to frames of these inputs, and recurrent neural networks taking in account the fact, that they can keep track of the long- and short- term dependencies in the input sequences. We choose to build a model for joint slot filling and intent detection, because we believe, that there is a strong relation between the intent and the semantic slots. A joint model can reflect this relation, figure it out and make use of it to enhance the prediction results. We use the Airline Travel Information System (ATIS) dataset to measure the performance of our model and compare it with the results of other models, as this dataset has become one of the most popular datasets for spoken language understanding problem.
PVplr: R Package Implementation of Multiple Filters and Algorithms for Time-series Performance Loss Rate Analysis

PVplr is an R package designed with the intent to offer a variety of options and side-by-side comparisons when modeling outdoor PV time-series data for performance loss rate (PLR) analysis. Built as a part of the IEA-PVPS task 13 study on the determination and uncertainty of PLR calculation, the package is designed to not to run a single method but to compare multiple commonly used methods on the same systems to determine system stability and identify biases in the analysis. The workflow is designed as a pipeline with steps of data cleaning, weather correction, time-series processing, and PLR determination, with multiple options at each step replicating commonly used methods or specific process created by other groups. Non-linear PLR evaluation capabilities have been added using piecewise linear modeling. Implementation of this pipeline in prior work has shown when 40 unique assumed linear PLR values are calculated for individual systems results can show significant variance depending on the steps chosen in data processing and modeling, highlighting the biases that can be induced by differences in analysis processes. Future updates to the package are planned that add more options to each step of the pipeline and allow python and R integration so python packages such as RdTools and PVlib-python can be used in conjunction with PVplr.
Toward Prediction of Student’s Guardian in the Secondary Schools for the Real Time

To ease of school management for the identification of student’s protector during his or her schooling and evaluate the performance of a student, the guardian predictive models are presented with the help of machine learning algorithms. For this, standard secondary dataset of two secondary schools was considered from Portugal belonging to the language course. The initial dataset consisted of 649 instances and 33 features. These features belong to the student’s academic, demography, family and personal features. The guardian feature has been considered as a class variable and others (significant) features assumed as predictors. In the orange platform, three machine learning algorithms, support vector machine (SVM), random forest (RF) and neural network (NN), were used with three testing techniques. On one hand, the SVM computed the highest prediction probabilities of 0.996 for other class and another hand, the NN gives the largest prediction probabilities such as 0.906 for father class and 0.889 for mother class. The NN attained the most guardian prediction accuracy of 89% and outperformed others. Also, leave-one-out method significantly enhanced the prediction accuracy of each learner except the SVM. Also, it proved the NN learner slower with prediction time (23 s) and makes the SVM as faster with time (14 s). This study may not only helpful to the school management but also support the social administration of the district or state. Using the model, it must be significant to predict the care-taker of the student.
Design of chaotic synchronization system combining projective, marginal and hybrid synchronization with application to image encryption

In this paper a chaotic synchronization system that combines projective, marginal and hybrid synchronization is designed. According to the second Lyapunov stability law, marginal projective hybrid synchronization scheme is designed based on two identical hyperchaotic systems. The achieved synchronization system is applied to image encryption, through chaotic masking.
A Study of Models Combination for Global Clear Sky Irradiance Models

Clear sky global horizontal irradiance (GHIcs) defines the theoretical maximum irradiance reaching a horizontal surface and are often derived from semi-empirical or physical based clear-sky models. In this study we demonstrate a naive model combination method of global clear sky models for improved estimation of GHIcs. To be specific, 10 best performing beam and diffuse clear sky models are each selected from pervious study [1] and are paired to create 97 combinations of global clear-sky models. By including 18 standalone global clear sky models (distinguish from individual beam and diffuse combined models), this study compares 115 global clear sky models under 100 worldwide solar measurement ground stations. After rigorous data quality control and clear sky detection, 18.7 million 1-min time data points (between 2015-01-01 and 2019-09-30) are used to evaluate all 115 models. Principal Component Analysis (PCA) ranking procedure is then employed to aggregate 12 error metrics and provides the overall ranking scores. The top 3 world-wide performing global clear-sky models are MMAC-v1_IQBAL-C, MMAC-v2_PSIREST and REST2-v5, i.e., not the combination of the best beam and diffuse models. Such results may be due to the over- or under- estimation of different beam and diffuse models. Another interesting finding is the REST2-v5 model which was also listed in the top 10 global clear sky models in previous study. In all, most combined models achieve greater performance (higher PCA ranking scores) than the original ones.
Mechanisation of Model-theoretic Conservative Extension for HOL with Ad-hoc Overloading

Definitions of new symbols merely abbreviate expressions in logical frameworks, and no new facts (regarding previously defined symbols) should hold because of a new definition. In Isabelle/HOL, definable symbols are types and constants. The latter may be ad-hoc overloaded, i.e. have different definitions for non-overlapping types. We prove that symbols that are independent of a new definition may keep their interpretation in a model extension. This work revises our earlier notion of model-theoretic conservative extension and generalises an earlier model construction. We obtain consistency of theories of definitions in higher-order logic (HOL) with ad-hoc overloading as a corollary. Our results are mechanised in the HOL4 theorem prover.
Multiple Dynamic Perimeter Surveillance by an Autonomous Robot

This paper proposes a method to perform surveillance tasks of dynamic perimeters using an autonomous mobile robot. In natural disaster scenarios in which the borderline of the hazard - like oil spills, wildfire, and radiation - can expand over time and pose a lot of danger, it is extremely helpful to efficiently perform a surveillance task in order to support evacuation missions and to aid the hazard dispel. Our proposal is based upon using an interpolation method to define the boundary of the perimeter that will be placed under surveillance, next a navigation strategy makes the robots converge to and circumnavigate the defined perimeter, finally, an optimization approach improves the coverage considering multiple perimeters. Results show that our perimeter representation is reliable in different scenarios, and the motion control approach allows for an efficient and minimized tracking error.
Quantum Fourier Operators and Their Application

The application of the quantum Fourier transform (QFT) within the field of quantum computation has been manifold. Shor’s algorithm, phase estimation and computing discrete logarithms are but a few classic examples of its use. These initial blueprints for quantum algorithms have sparked a cascade of tantalizing solutions to problems considered to be intractable on a classical computer. Therefore, two main threads of research have unfolded. First, novel applications and algorithms involving the QFT are continually being developed. Second, improvements in the algorithmic complexity of the QFT are also a sought after commodity. In this work, we review the structure of the QFT and its implementation. In order to put these concepts in their proper perspective, we provide a brief overview of quantum computation. Finally, we provide a permutation structure for putting the QFT within the context of universal computation.
A Study on Parameter Sensitivity Analysis of the Virus Spread Optimization

The virus spread optimization (VSO) is a radically new metaheuristic optimization algorithm to mimic the viral behavior and spread of viruses for continuous optimization. Due to the multiple search strategies design, the VSO achieves an excellent performance on a series of well-known benchmark functions in terms of the solution quality, convergence rate and stability. Yet the number of control parameters involved in the VSO algorithm is relatively larger than those of other popular metaheuristics such as genetic algorithm (GA) and particle swarm optimization (PSO). Besides, there is rarely any study on the possible impact of such parameters on the performance of the VSO as based on the default parameter settings when compared to those of other metaheuristics. In this work, the parameter sensitivity of the VSO is carefully examined by performing a suite of experiments. More importantly, the rules of thumb for the parameter tuning of the VSO is also considered. Essentially, this work reveals the impact of the parameters contributing to the success of the VSO to boost the research of this promising optimization algorithm.
Collaborative Team Construction in Open Multi-Agents System

Agents usually organize their behavior and cooperate to reach a common global goal by sharing constrained resources or perform complex tasks. Constructing an efficient collaborative team where agents are ready to collaborate without interests' conflict is fundamental for the success of any collaboration process. The construction of such teams was well discussed in closed Multi-Agents System (MAS). Nevertheless, it represents a real challenge in open MAS, where each agent joins and leaves the system unpredictably and frequently and has a set of goals to achieve concurrently (agent is self-interested). This study describes an entire decentralized method to the collaborative Team Construction Model (CTCM) for multi-goals agents in open MAS. CTCM defines a modified social reasoning model and a new decentralized team construction method. CTCM allows each agent to achieve its local and common goals concurrently in constrained resource open MAS by constructing collaborative teams. The agents share partial domain knowledge (to preserve privacy) and model their goal relations with other agents by applying modified social reasoning. The team construction method facilitates agents decision-making process in a decentralized way. The CTCM enables agents to adjust their self-interest level and adapt their readiness to construct effective collaborative teams.
A construction of MDS involutory matrices using MDS self-dual codes: a preliminary result

Abstract The Maximum Distance Separable (MDS) matrices have an important application in cryptography, for example see [1]. In order to reduce the implementation complexities, it is important to consider MDS matrices which are involutory. Some constructions of involutory MDS matrices can be found in [2, 3]. In this paper, we give some properties related to a construction of involutory MDS matrices using orthogonal matrices derived from Euclidean self-dual MDS codes. Then, we do a computer search to find involutory MDS matrices with small sizes using classical simulated annealing algorithm. The proposed construction is different compared to the ones in [2, 3].
Analysis of Tainted Transactions in the Bitcoin Blockchain Transaction Network

Blockchain technology, with its decentralised peer-to-peer network and cryptographic protocols, has led to a proliferation of cryptocurrencies, with Bitcoin at the forefront. The blockchain publicly records all Bitcoin transactions which can be used to build a dynamic and complex network to give a representation of the transactions in the underlying monetary system. Despite the cryptographic guarantees there exist inconsistencies and suspicious behavior in the chain. We reported on two such anomalies related to block mining in previous work. In this paper, we build a network using bitcoin transactions and apply techniques from network science to analyse its complex structure. We focus our analysis on sub-networks induced by the two sets of anomalies, and investigate how inequality in terms of wealth and anomaly fraction evolves from the blockchain’s origin. Thereby we present a novel way of using network science to detect and investigate cryptographic anomalies.
Automation of Demand Planning for IT Specialists Based on Ontological Modelling

The chapter addresses the issue of correspondence between the skills required by employers and the professional competencies of specialists in the IT field. The discrepancy between the said sets has been highlighted. An approach based on extracting skills from the natural language vacancies texts published on the job aggregators sites is proposed. The method allows for analyzing the required professional competences from the employers’ point of view to eliminate the identified differences. Possible ways of structuring the selected skills including ontological modeling and cluster analysis are described. An ontological model has been created to proceed with the hierarchical structuring of the professional competencies set. Skill groups have been formed based on domain knowledge, and cluster analysis has been applied to form workload sets. The method of dynamic cluster formation and skill attribution to a particular group within the domain is described. The applied aspects of the approaches are examined using data of Russian regions and federal states of Germany. The differences between a set of workloads and a skill set are determined. The strengths and weaknesses of the highlighted approaches are described. The automation method of demand planning for IT specialists based on an integrated model combining the described approaches above is suggested. Prospects for its further application are outlined.
Two-Stream 3D Convolution Attentional Network for Action Recognition

We propose a new method, which uses a two-stream 3D convolution network to capture rich spatial and temporal information, then process it with an attention module to capture long- and short-term dependency, to recognize action on the videos. By taking advantages of 3D convolutions, not only spatial information is obtained, but the movement information on the videos is also captured as temporal information. The main reason to consider long-term temporal dependency information is that it will be important to identify action on the videos. The bidirectional self-attention network uses forward/backward masks to encode temporal order information, and attention to handle our sequence on 3D convolution features. The experimental results indicate that the proposed method can be compared to state-of-the-art work in the HMDB-51 dataset with a less complex process while maintaining the performance. We employ a two-stream 3D network to capture spatial-temporal features, combined with self-attention to further capture temporal relationships.
Neither in the Programs Nor in the Data: Mining the Hidden Financial Knowledge with Knowledge Graphs and Reasoning

Vadalog is a logic-based reasoning language for modern AI solutions, in particular for Knowledge Graph (KG) systems. It is showing very effective applicability in the financial realm, with success stories in a vast range of scenarios, including: creditworthiness evaluation, analysis of company ownership and control, prevention of potential takeovers of strategic companies, prediction of hidden links between economic entities, detection of family businesses, smart anonymization of financial data, fraud detection and anti-money laundering. In this work, we first focus on the language itself, giving a self-contained and accessible introduction to Warded Datalog+/-, the formalism at the core of Vadalog, as well as to the Vadalog system, a state-of-the-art KG system. We show the essentials of logic-based reasoning in KGs and touch on recent advances where logical inference works in conjunction with the inductive methods of machine learning and data mining. Leveraging our experience with KGs in Banca d'Italia, we then focus on some relevant financial applications and explain how KGs enable the development of novel solutions, able to combine the knowledge mined from the data with the domain awareness of the business experts.