Reviewerly/exHarmony huggingface dataset:
Development of an Artificial Intelligence System to Design of Structures using Reinforcement Learning: Proof of Concept

Artificial intelligence (AI) has significantly progressed during the last several decades with the rapid advancement in computational capabilities. This advanced technology is currently being applied in various engineering fields, not just in computer science. Artificial neural network (ANN) are currently being widely used in mechanical/structural problems since ANN can be used as a surrogate model for modeling the physical system. However, these applications do not employ the highest AI level, in which the ANN can judge by itself as is the case of advanced AI algorithms such as for autonomous driving cars or a walking robot. Reinforcement Learning (RL) is an approach to machine learning that mimics human behavior, like how human beings solve a problem based on their experience. A human problem-solving process can be improved by earning a positive reward from good experiences (results). Also, the RL algorithm can determine which actions will cause worse result and provide a negative feedback. Therefore, such an algorithm can be applied to solve structural design problems where the engineers can efficiently resolve the issues and bring correct results through trial and error. In this study, an AI system with the RL algorithm is developed to design optimized truss structures (with continuous and discrete cross-section choices) under a set of given constraints. Also, we proposed a unique reward function system to consider the constraints in structural design problems. From a set of two examples, we confirmed that the proposed AI system could design truss structures and also evolve as it gains experience. Therefore, it is possible to develop an AI system that can learn from experience and design the structure by itself without little human intervention.
Utilizing binary code to improve usability of pressure-based authentication

Due to its invisibility feature, pressure is useful to enhance the security of authentication, especially preventing the shoulder surfing attack. However, users are more familiar with digital passwords than pressure-based passwords. In order to improve the usability of pressure-based authentication, this paper instantiates a pressure-based password (i.e., a sequence of pressures) to a decimal number. In addition, our approach features personalized pressure detection. The personalization further enhances security since an attacker must have a pressure habit that is consistent with the user. We conducted a series of user studies to compare the traditional four-digit password with our pressure-based password. The empirical result indicates that a pressure-based password is more resistant to the shoulder surfing attack than a four-digit password. However, it takes more time to input a pressure-based password on the first-time usage. The slowdown is caused by a modality change from vision to pressure. A field study that lasted for 10 days revealed that the side effect of modality change can be overcome through regular usages.
An Optical Flow Based Multi-Object Tracking Approach Using Sequential Convex Programming

Object tracking, as one of the open topics in the past few decades, has been widely applied in the field of video processing. Although there has been intensive research on this topic, some challenges still exist, such as occlusions, clutter and dynamic scenarios. In this paper, we propose a new multi-layer formulation for multi-object tracking, which incorporates the traditional optical flow constraints with a new product term. Then, a sequential convex programming (SCP) based method is presented to solve the resulting non-convex optimization problem. The proposed method can achieve a linear convergence rate, which is demonstrated by our numerical results. In order to illustrate the performance of our approach, it is implemented for composite videos, where two objects move in opposite directions. Different experimental settings based on noise-free, occlusions, clutter and dynamic scenarios show that the method can robustly recover multiple layers and the velocity of the objects. Moreover, the experiments indicate considerable potential for handling more complex scenarios.
Time Series Analysis of Fault Strain Accumulation Around Large Dam: The Case of Enguri Dam, Greater Caucasus

The 271-m-high Enguri arch dam, still one of the highest of this kind in operation in the world, was built in the canyon of the Enguri river (western Georgia) in the 1970s. It is located in a zone of high seismic hazard and is close to the Ingirishi strike-slip active fault. The strong seismic activity, as well as the great number of people living downstream of the dam, make this facility a potential source of a major catastrophe in Georgia. Thus, the Enguri Dam, with its 1 billion cubic meters water reservoir, should be under permanent monitoring. At the same time, this area is an amazing natural laboratory, where researchers can investigate both tectonic and geotechnical issues and the effects of load-unload processes at the artificial reservoir, i.e. the reaction to a controllable loading of the Earth crust. This is an important scientific topic, connected with the fundamental problem of reservoir-induced earthquakes as well as with environmental geotechnical problems, related to the security of major dams in general. The complexity analysis methods applied to time series of the fault strain monitoring data enables discriminating seismic events derived by the human activities and background seismicity. Such complexity analysis provides a new approach – Reservoir Synchronized Induced Seismicity, or RSIS – for a quantitative assessment of reservoir water level load on the (synchronized/ordered) temporal pattern of local seismicity. Besides, analysis of complexity in the fault strain monitoring data can provide also some early warning methods for signaling dangerous deviations of fault strains and dam tilts temporal behavior from the stable (background) pattern.
Automated Malaria Parasite Detection Using Artificial Neural Network

Malaria is still an infectious disease that causes high mortality in endemic regions. It is thought that it will maintain importance in the future, especially due to people travelling from African countries where malaria is endemic to its eradicated regions. Therefore, rapid and accurate diagnosis is a critical step in the effective treatment of malaria and reducing mortality rates. This paper provides a malaria diagnosis system using an artificial neural network approach with SURF (Speeded Up Robust Features) method that helps the clinicians to predict and locate infected cell with malaria on the sample thin blood smear image. The performance of the proposed neural network and local image feature extraction technique SURF were analyzed statistically and presented in this paper. The network was trained using only 45 infected thin blood smear images and was then tested with 200 (100 infected and 100 non-infected) unseen images. The experimental results showed that the proposed system identified the malaria parasite with 93% accuracy, 86% sensitivity and 100% specificity.
Investigating Crucial Factors of Agile Software Development Through Composite Approach

The major emphasis of Software Engineering (SE) discipline is to produce successful software systems. The success of software projects is estimated through quadruple measures including budget, cost, scope, and quality. To meet this aim of SE, several software development processes are presented in the literature. Such processes are categorized into two different methodologies which are known as traditional and agile software development methodologies. The issue with traditional software development methodologies is that they had not shown any remarkable progress towards the fundamental goal of SE. Consequently, software development organizations have started to adopt agile methodologies in the pursuit of successful software development. However, agile adoption does not come without challenges that vary from one context to another. Therefore, it is necessary to figure out the key factors of agile software development for successful project outcomes. In the wake of such need, this study investigated the Critical Success Factors (CSFs), categorized and prioritized them through a mixed-method approach. Such an approach was based on the detailed literature review and Delphi method accompanied with Multi-Criteria Decision Analysis (MCDA) technique. Twelve CSFs were revealed and categorized into people, organization and technical dimensions. Among these factors, ‘team capability’ was found the most significant factor where ‘culture’ was revealed as the least significant factor. The findings of the study would be promising for agile software development that is carried on in the local software industry.
On the Potential of Rocket League for Driving Team AI Development

Several Games have been used as test cases for new AI developments in the last years, e.g. the games of the Atari Learning Environment, Go, and also StarCraft. As many single player problems are treated successfully now, research is turning more to Team AI problems: AI/human and AI/AI coordinated decision making in mixed cooperative/competitive environments. OpenAI Five and RoboCup and related environments as MuJoCo soccer are pushing into this direction. Rocket League is an interesting alternative as it has quite different game mechanics compared to soccer, and an API with a number of different bots already exists. We summarize the current state, relate it to the situation in the fields that may provide useful approaches as e.g. sports analysis, and argue that it makes sense to do more research on Rocket League bot control and coordination.
RETRACTED: Simulation of sports cooperative learning model based on computer hardware system and dynamic image sampling

The motivation behind this article is to introduce significant teaching methods of physical education, tactical games, learning, and real education co-learning. Organized learning uses a hypothetical structure and the link between physical education, tactical games, learning, and collaborative learning. Intensive learning as an alternative to physical education, tactical games, learning, and an educational program rather than a collaborative learning system, as an educator concentrated on directing the educational curriculum where support is considered. Promote learning and training of teachers, it is all-encompassing social progress, going to school is impossible to mix physical and intellectual learning outcomes understudies. Including an excessive focus on education and the learning process is dynamic detection of the rotation. Holding on to the instructions and learning the actual assumptions of clear assumptions, Sports Education (SE) Tactical Games (TG), and Cooperative Leruiiirlg (CL) now act like a three-fold bench focus as learn. Use SE, TG, and CL educational outcomes to focus on the promotion of climate learning to consider as an alternative to filling Thor's coach Lu. The educator is deliberately busy with learning to move on the duty bench, truly significant, mistakes.
A Conceptual View for an Enhanced Cloud Software Life-Cycle Process (CSLCP) Model

Small to Medium-sized Enterprises (SMEs) benefit from the advantages of the cloud computing environment. These enterprises have limited resources. Consequently, SMEs require a structured software process model to develop a reliable and good quality cloud software. Existing cloud software process (CSP) models focused only on the processes of software development and ignored the other aspects of software production. In this paper, a conceptual view for Cloud Software Life Cycle Process (CSLCP) model is proposed. This model overcomes the deficiencies of existing CSP models. Also, it satisfies the development of a reliable and high quality cloud software. The CSLCP model is compatible with level two and three of the capability maturity model integration (CMMI). It extends the software process improvement (SPI) model, developed in Egypt for SMEs, to suit the cloud environment. The application of the CSLCP model in SMEs would improve their level of maturity
Multimodal Deep Learning Architecture for Identifying Victims of Online Death Games

Online death games are a fairly recent public health concern of the modern technology-driven world. Various dangerous online games like Blue WhaleMalhotra, Anshu ChallengeJindal, Rajni and MOMO challenge have grown popular through social networking sites where players or victims engage in self-harming activities, often leading to death. This problem domain has not been studied in depth till date and no known technology-based solutions exist to prevent the spread of such dangerous challenges. The prime objective of our research is to explore the use of deep learning and transfer learning techniques for content analysis of user-generated posts over various social networking sites and design an early warning system which can be used by healthcare authorities for timely identification of victims of these games so as to avoid any fatalities. In this paper, we first discuss in detail the numerous challenges in building required technology-driven solutions for this domain. Next we propose a multimodal deep learning-based system for identifying victims of online death games, using state-of-the-art feature generation techniques for two modalities in user's social media posts: image and text. To the best of our knowledge, our proposed system is the first technology-driven public healthcare administration tool for this this domain.
Standing on the Shoulders of Software Product Line Research for Testing Systems of Systems

The complex and dynamic nature of Systems of Systems (SoSs) poses many challenges on their validation and testing, but so far few effective test strategies exist to address them. On the other hand, extensive research has been conducted in the testing of Software Product Lines (SPLs), which present interesting convergence points with SoSs, as both disciplines aim at reducing development costs and time-to-market thanks to extensive reuse of existing artifacts. In this paper, we outline commonalities and differences between the SoS and SPL paradigms from the point of view of testing and investigate how existing methods and tools from SPL testing could be leveraged to address the challenges of SoS testing.
A bayesian network model for machine learning and cyber security

The phenomenal growth in the use of internet-based technologies has resulted in complexities in cyber security subjecting organizations to cyber-attacks. This research is purposed to develop a cyber-security system that uses the Bayesian Network structure and Machine Learning. The research determined the cyber-security framework appropriate for a developing nation; evaluated network detection and prevention systems that use Artificial Intelligence paradigms such as finite automata, neural networks, genetic algorithms, fuzzy logic, support vector machines, or diverse data-mining-based approaches; analyzed Bayesian Networks that can be represented as graphical models and are directional to represent cause-effect relationships; and developed a Bayesian Network model that can handle complexity in cybersecurity. The Pragmatism paradigm used in this research, as a philosophy is intricately related to the mixed-method approach, which is largely quantitative with the research design being a survey and an experiment, but supported by qualitative approaches where Focus Group discussions were held. The Artificial Intelligence paradigms evaluated include machine learning methods, autonomous robotic vehicles, artificial neural networks, and fuzzy logic. Alternative improved solutions discussed include the use of machine learning algorithms specifically Artificial Neural Networks (ANN), Decision Tree C4.5, Random Forests, and Support Vector Machines (SVM).
Mapping the Lithological Features and Ore-Controlling Structures Related to Ni–Cu Mineralization in the Eastern Tian Shan, NW China from ASTER Data

The study of lithological features and tectonic evolution related to mineralization in the eastern Tian Shan is crucial for understanding the ore-controlling mechanism. In this paper, the lithological features and ore-controlling structure of the Huangshan Ni–Cu ore belt in the eastern Tian Shan are documented using advanced spaceborne thermal emission and reflection radiometer (ASTER) multispectral data based on spectral image processing algorithms, mineral indices and directional filter technology. Our results show that the algorithms of b2/b1, b6/b7 and b4/b8 from ASTER visible and near-infrared (VNIR)- shortwave infrared (SWIR) bands and of mafic index (MI), carbonate index (CI) and silica index (SI) from thermal infrared (TIR) bands are helpful to extract regional pyroxenite, external foliated gabbro bearing Ni–Cu ore bodies as well as the country rocks in the study area. The detailed interpretations and analyses of the geometrical feature of fault system and intrusive facies suggest that the Ni–Cu metallogenic belts are related to Carboniferous arc intrusive rocks and Permian wrench tectonics locating at the intersection of EW- and NEE-striking dextral strike-slip fault system, and the emplacement at the releasing bends in the southern margin of Kanggur Fault obviously controlled by secondary faults orthogonal or oblique to the Kanggur Fault in the post-collision extensional environment. Therefore, the ASTER data-based approach to map lithological features and ore-controlling structures related to the Ni–Cu mineralization are well performed. Moreover, a 3D geodynamic sketch map proposes that the strike-slip movement of Kanggur Fault in Huangshan-Kanggur Shear Zone (HKSZ) during early Permian controlled the migration and emplacement of three mafic/ultramafic intrusions bearing Ni–Cu derived from partial mantle melting and also favored CO2-rich fluids leaking to the participation of metallogenic processes.
Sentiment classification of Sinhala content in social media

In this study, we focus on the classification of Sinhala social media sentiments into positive and negative classes for a particular domain (sports). We have employed machine learning algorithms and lexicon-based sentiment classification methods. We also consider a hybrid approach by constructing an ensemble classifier in which we combine Machine Learning and Lexicon based methods. For individual methods, machine learning algorithms performed best in terms of accuracy. The ensemble classifier was able to improve performance further.
Towards Evolving Security Requirements of Industrial Internet

Industrial Internet has been incrementally established in recent years to support smart manufacturing, which involves a collection of technology and value chain organization concepts. Along with the continuous development of the industrial Internet, an increasing number of threats have been exposed. In particular, the integration of traditional industrial settings and industrial Internet platforms leads to incompatible security countermeasures, making the industrial Internet vulnerable. This article comprehensively and deeply investigates the security challenges of the current industry Internet based on relevant research studies and realistic security scenarios. In response to our identified security challenges, we propose a data security transfer architecture solution for industrial platforms. Specifically, our proposal contains seven sub-modules, which enhances the industrial cloud platform with secure data security transfer technology to protect industrial data when it is connected to the cloud platforms and industrial equipment. To evaluate the utility of our proposal, we have specialized our security architecture solution to a realistic case of the industrial Internet.
Using Compression to Discover Interesting Behaviours in a Hybrid Braitenberg Vehicle

The simple rules that govern the interactions between the different components of a complex system often lead to interesting behaviours that are unexpected. The experiments described in this paper involved creating a variation of a traditional Braitenberg Vehicle, by placing sensors on ten different possible locations on a simulated vehicle, and incorporating an obstacle avoidance behaviour using a subsumption-like architecture, which resulted in different unusual and unexpected behaviours being produced. The vehicle was allowed to explore a simulated environment which contained a single bright light in the centre with walls on the border. By using a novel combination of the Prediction by Partial Matching compression algorithm and k-means clustering, interesting emergent behaviours were effectively discovered within a search space of over 10,000 simulations produced from a simple interaction of light and proximity sensors on a vehicle and a single light source. The clustering algorithm discovered five distinct behaviours: circling and spiralling behaviours; interesting behaviours creating intricate rose petal-like structures; behaviours that create simple rose petal-like structures; behaviours with large movements and low complexity; and behaviours with less movement. The novel algorithm demonstrated in this paper has useful potential in the science of complex systems and modelling to help expedite the systematic exploration of a substantial search space of simulations in order to discover interesting behaviours.
Stratification in Approximation Fixpoint Theory and Its Application to Active Integrity Constraints

Approximation fixpoint theory (AFT) is an algebraic study of fixpoints of lattice operators that unifies various knowledge representation formalisms. In AFT, stratification of operators has been studied, essentially resulting in a theory that specifies when certain types of fixpoints can be computed stratum per stratum. Recently, novel types of fixpoints related to groundedness have been introduced in AFT. In this article, we study how those fixpoints behave under stratified operators. One recent application domain of AFT is the field of active integrity constraints (AICs). We apply our extended stratification theory to AICs and find that existing notions of stratification in AICs are covered by this general algebraic definition of stratification. As a result, we obtain stratification results for a large variety of semantics for AICs.
FMCGP: frameshift mutation cartesian genetic programming

Abstract Cartesian Genetic Programming (CGP) is a variant of Genetic Programming (GP) with the individuals represented by a two-dimensional acyclic directed graph, which can flexibly encode many computing structures. In general, CGP only uses a point mutation operator and the genotype of an individual is of fixed size, which may lead to the lack of population diversity and then cause the premature convergence. To address this problem in CGP, we propose a Frameshift Mutation Cartesian Genetic Programming (FMCGP), which is inspired by the DNA mutation mechanism in biology and the frameshift mutation caused by insertion or deletion of nodes is introduced to CGP. The individual in FMCGP has variable-length genotype and the proposed frameshift mutation operator helps to generate more diverse offspring individuals by changing the compiling framework of genotype. FMCGP is evaluated on the symbolic regression problems and Even-parity problems. Experimental results show that FMCGP does not exhibit the bloat problem and the use of frameshift mutation improves the search performance of the standard CGP.