Reviewerly/exHarmony huggingface dataset:
Why Multilayer Networks Instead of Simple Graphs? Modeling Effectiveness and Analysis Flexibility and Efficiency!

We are on the cusp of analyzing a variety of data being collected in every walk of life - social, biological, health-care, corporate, climate, to name a few. We are also in search for models and analytical techniques that can accommodate more complex and increasingly large size data (scalability). Our ability to analyze large complex, disparate data for a broad set of analysis objectives differentiates big data analytics from mining which is narrow in scope. Hence, flexibility of analysis (different from scalability) is important. Concomitantly, efficiency is important due to large number of analysis needs. Our ultimate goal is to go from vertical analysis of data individually (corresponding to one of the 4 V's) to holistically (also termed fusion-based) analyze that corresponds to all or a subset of V's! In order to accomplish the above, we are always in search for more effective models to represent data and different analysis techniques that support flexibility of analysis, efficiency, and scalability. We want to use techniques that have worked well – whether it is for modeling, efficiency or scalability. We also want to extend these techniques and/or develop new and improved ones to accommodate more complex, diverse, and larger size data. The goal of this paper is to provide the reader an understanding of data analysis approaches using graphs. Our thesis is that there are several ways in which a graph representation can be used – both for modeling and analysis. We will take the reader through the evolution of graph usage and relevance leading to the current state of the use of multilayer Networks (MLNs) or multiplexes for modeling and analysis. Graphs are not new, but how they are used for big data analytics is going through a transformation which is important to understand. The hope is that the reader understands the path that has led us to this juncture and how graph usage is extended!
Put Your Head on a Blockchain? A Few Notes on the Emergence of Blockchain Technologies

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Cloud-Enabled Domain-Based Software Development

A cloud-based software development framework is presented that does not require programmer capabilities. The development starts with a graphical modelling of the process model, defining the top-level flow for the application. Such a flow coordinates the functional units that are components or services linked to the process again through graphical means such as drag and drop. VariabilityVariability affects all processes and functional constituents, being the principal specification requires for the application under development. The idea has been partially implemented in a commercial setting and is in its assessment phase. This framework needs to be domain-specific for successful deployment of user ideas without programming-level input. As a platform, the suggested environment allows the setting up of different development environments for different domains. A user community can construct new domains by defining reference architectures, process models and other assets for the application developers. Consequently, there is a possibility of a market place shaping up where such assets can be offered and consumed, subject to an administration for security and optionally commercial purposes. Open, free or paid marketplaces can be created based on administrative policies.
An Improved Wavelet Threshold Function And Its Application In Image Edge Detection

In order to filter out image noise better and make it have better clarity, continuity and anti-noise performance in image edge extraction. Firstly, this paper constructs a new threshold function, compared with the traditional soft and hard threshold function and some existing improved methods, the threshold function has better adjustability and it is also continuous and almost smooth everywhere. When dealing with the wavelet coefficients, the real information on them can be retained more, and the noise can be effectively filtered at the same time. The simulation experiment shows that the image processed by the new threshold function has a high PSNR and a small MSE, which can be closer to the original image. Finally, the improved threshold function de-noising algorithm and the dyadic wavelet transform modulus maximum edge detection algorithm are combined to apply to image edge detection. By combining the advantages of the two algorithms, so that we can get clearer and more continuous image edges, and the contour is more complete.
Addressing Security and Privacy Issues of Load Balancing Using Hybrid Algorithm

In today’s world, the need and urge for use of cloud become more popular among the public users. The cloud provides services like freeware to the end-users. The resources that the cloud users use will be in form of shared pool. If any resources are requested by the end-users, they are provided in a shared pool. Nowadays, the resources are requested only in dynamic basis. Upon the requisition by the user, the resources are provided to them. From these shared pools of resources, the cluster head or master node is selected by using Advanced Ant Colony optimization algorithm. The status of each and individual nodes should be known to neighbor nodes and master nodes; these can be achieved by using “Heartbeat messages”. The status and movement of an individual node can be known by using these messages. The services requested by end-user and they are provided to them in very secure manner using DMZ (De-militarized zone) technique. The DMZ provides very higher security, that is, three layers of security, with different algorithms at each layer. In this paper, we address data leakage security issues and dynamic load balancing issues.
Analysis of the Impact of Poisoned Data within Twitter Classification Models

Many social-networks today face growing problems of group polarization, radicaliza-tion, and fake news. These issues are being exacerbated by the phenomenon of bots, which are becoming better at mimicking real people and are able to spread fake news faster within social-networks. Methods exist for detecting these social-media bots, but they may be vulnerable to manipulation. One way this might be done is through what is called a poisoning attack, where the data used to train a model is altered with the goal of reducing the models accuracy. The goal of this research is to study how poisoning attacks may be applied to models for detecting bots on Twitter. The results show that by introducing mislabeled data- points into a such a models training data, attackers can reduce its accuracy by up to twenty percent. The possibility of more effective poisoning techniques exists, and remains a topic for future research.
Statistical Analysis and Automatic Recognition of Grammatical Errors in Teaching Chinese as a Second Language

Foreigners make various grammatical errors when learning Chinese due to the negative transfer of their mother tongue, learning strategies, etc. At present, the research on grammatical errors mainly focuses on a certain word or a certain kind of errors, resulting in a lack of comprehensive understanding. In this paper, a statistical analysis on large-scale data sets of grammatical errors made by second language learners is conducted, including words with grammatical errors and their quantities. The statistical analysis gives people a more comprehensive understanding of grammatical errors and have certain guiding significance for teaching Chinese as a second language (TCSL). Because of the large proportion of grammatical errors of “的[de](of)”, the usages of “的[de](of)” are integrated into automatic recognition of Chinese grammatical errors. Experimental results show that the performance is overall improved.
Incremental Throughput Allocation of Heterogeneous Storage With No Disruptions in Dynamic Setting

Solid-state drives (SSDs) have been added into storage systems for improving their performance, which will bring the heterogeneity into the storage medium. The throughput is one of the essential resources in heterogeneous storage systems, and how to allocate the throughput plays a crucial role in user performance. There are many types of research on the throughput allocation of heterogeneous storage systems. However, the throughput allocation of heterogeneous storage is facing new challenges in a dynamic setting, where users are not present in the system simultaneously, and enter the system dynamically. Drawing on economic gametheory, researchers have proposed many methods to tackle dynamic throughput allocation issues for heterogeneous storages, cross out enjoying Sharing Incentive (SI), Envy Freeness (EF), and Pareto Optimality (PO). However, they either relax constraints of fairness property to cause the allocation with weak fairness or interrupt some users present in the system to give up a piece of their allocations for new users entering the system, which will degrade these donors' performance. Moreover, all of existing methods will cause lower resource utilization due to constraints of users' dominant share equality. In this article, we propose a dynamic throughout allocation method based on gradual increase (DAGI), which can adapt to various workloads to make a fair allocation with a maximum resource utilization. Without relaxing constraints of fairness properties, when new users enter the system, DAGI can make a dynamic allocation with strong fairness by appropriately postponing the allocation of surplus throughputs, so this can provide an opportunity that DAGI can guarantee the final allocation with strong fairness when allocating remaining throughputs after all users are present in the system. Meanwhile, DAGI can gradually increase user allocation without reduction, which will not interrupt any users present in the system. Furthermore, DAGI can conduct a dynamic throughput allocation based on users' local bottleneck resources, which can adapt to various workloads of users to improve resource utilization. Extensive experiments are conducted to prove the effectiveness of DAGI. The experimental results show that DAGI can achieve higher resource utilization and performance than existing methods, and can satisfy desirable game-theoretic properties with guaranteeing the strong fairness. In addition, DAGI gradually increases the allocation of each user without interrupting any user to reduce its allocation to degrade its performance.
Information Infrastructures and the Future of Ecological Citizenship in the Anthropocene

In the last two decades, the concept of ecological citizenship has become a recurrent theme in both popular and academic discussions. Discussions around the prospects of, and limitations to, ecological citizenship have mostly focused on the idea of political agency and the civic responsibility of individuals in relation to their environments, with an emphasis on environmental justice and sustainability. However, the current scholarship has yet to adequately characterize its conceptual bases and empirical applications from an information perspective. Therefore, this paper provides an overview of citizenship studies and infrastructure studies for developing more nuanced understanding(s) of epistemological models for ecological citizenship in our networked world. Drawing on the literature on information infrastructure, this paper then proposes a conceptual framework to understand ecological citizenship as constituted both discursively and techno-materially through neoliberal, anthropocentric informational infrastructures.
Learnability of Interestingness with Semantic Similarity and Reasoning in the Knowledge Base of Decision Support Systems

The evolution of deep learning blended with GPU/TPU has elicited faster computation and assimilation of Big Data at a rapid pace with the exponential learning rate of models. Mobile technologies and cloud-based services are yielding massive data irrespective of geographic location at a rapid pace. Integrating the available plethora of data to find a semantic similarity while providing a rapid response without compromising on the quantity and quality of data is a prime concern. Learning from semantic similarity, utility algorithms turn this data into machine perceivable information, through learnability and utilization of Senticnet. The retainability of knowledge still has its own set of specific needs in terms of different machine learning and artificial intelligence algorithms. Utilization of the semantic similarity for ontology-based learning with interoperability helps preserve privacy for decoding the control attributes. The aspect of learning may further extend for rapidly generated sensor data through things and mobile devices.
Contradictory Information: Better Than Nothing? The Paradox of the Two Firefighters

Prominent philosophers (Bar-Hillel and Carnap, Popper, Floridi) have argued that contradictions contain either too much or too little information to be useful. We dispute this with what we call the “Paradox of the Two Firefighters.” Suppose you are awakened in your hotel room by a fire alarm. You open the door. You see three possible ways out: left, right, straight ahead. You see two firefighters. One says there is exactly one safe route and it is to your left. The other says there is exactly one safe route and it is to your right. While the two firemen are giving you contradictory information, they are also both giving you the perhaps useful information that there is a safe way out and it is not straight ahead. We give two analyses. The first uses the “Opinion Tetrahedron,” introduced by Dunn as a generalization of Audun Jøsang’s “Opinion Triangle.” The Opinion Tetrahedron in effect embeds the values of the “Belnap-Dunn 4-valued Logic” (Truth, Falsity, Neither, Both) into a context of subjective probability generalized to allow for degrees of belief, disbelief, and two kinds of uncertainty—that in which the reasoner has too little information (ignorance) and that in which the reasoner has too much information (conflict). Jøsang had only a single value for uncertainty. We also present an alternative solution, again based on subjective probability but of a more standard type. This solution builds upon “linear opinion pooling.” Kiefer had already developed apparatus for assessing risk using expert opinion, and this influences the second solution. Finally, we discuss how these solutions might apply to “Big Data” and the World Wide Web.
Cooperative Model of Evolutionary Algorithms and Real-World Problems

A cooperative model of efficient evolutionary algorithms is proposed and studied when solving 22 real-world problems of the CEC 2011 benchmark suite. Four adaptive algorithms are chosen for this model, namely the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) and three variants of adaptive Differential Evolution (CoBiDE, jSO, and IDEbd). Five different combinations of cooperating algorithms are tested to obtain the best results. Although the two algorithms use constant population size, the proposed model employs an efficient linear population-size reduction mechanism. The best performing Cooperative Model of Evolutionary Algorithms (CMEAL) employs two EAs, and it outperforms the original algorithms in 10 out of 22 real-world problems.
Real Time Static Gesture Detection Using Deep Learning

Sign gesture recognition is an important problem in human-computer interaction with significant societal influence. However, it is a very complex task, since sign gestures are naturally deformable objects. Gesture recognition contains unsolved problems for the last two decades, such as low accuracy or low speed, and despite many proposed methods, no perfect result has been found to explain these unsolved problems. In this paper, we propose a deep learning approach to translating sign gesture language into text. In this study, we have introduced a self-generated image data set for American Sign language (ASL). This dataset is a collection of 36 characters containing A to Z alphabets and 0 to 9 number digits. The proposed system can recognize static gestures. This system can learn and classify specific sign gestures of any person. A convolutional neural network (CNN) algorithm is proposed for classifying ASL images to text. An accuracy of 99% on the alphabet gestures and 100% accuracy on digits was achieved. This is the best accuracy compared to existing systems.
Translating Alloy and extensions to classical B

In this article, we introduce a denotational translation of the specification language Alloy to classical B. Our translation closely follows the Alloy grammar. Each construct is translated into a semantically equivalent component of the B language. In addition to basic Alloy constructs, our approach supports integers, sequences and orderings. The translation is fully automated and our implementation can be used in ProB. We evaluate the usefulness by applying AtelierB and ProB to translated models, showing benefits for proof and constraint solving with integers and higher-order quantification.
In-flight Kinematic Model Parameter Estimation and Adaptive Path Planning for Unmanned Aircraft

In real-world applications, unmanned aircraft may be subject to short-term configuration changes such as new payloads, additional batteries or antennas. Furthermore, degradation of materials, system failures, icing and weather conditions may impact performance. As a consequence, performance parameters may often be unknown or subject to uncertainty. To handle such performance uncertainties within an autonomous system, the system must exhibit a high degree of self-awareness and be able to react and adapt to false assumptions and inaccurate parameter values. Within this work, an adaptive path planning approach based on in-flight estimation of kinematic model parameters is presented. In order to achieve this, the parameters of the kinematic model used for path planning are continuously monitored and the flight path is re-planned correspondingly in order to fully utilize the aircraft's turn and climbing performance. At the same time the path tracking error and required safety distance along the flight path are minimized. Simulation results for multiple scenarios indicate that the approach improves performance and safety in the presence of uncertain or changing kinematic model parameters.
Research on improvement of high utility pattern mining algorithm over data streams

Abstract Aiming at the problem that the existing algorithms for high utility pattern mining over data streams based on sliding window have multiple datasets scans or redundant items, an efficient HUIGRT algorithm for mining high utility patterns over data streams based on global revision header table is proposed in this paper. First, the global revision header table and the utility tree are constructed. The global revision header table is used to store the items and transaction utility of the current data domain that need to be processed, and the utility tree is used to store all of the utility information on the item sets in the transactions to avoid multiple datasets scans. Then, this algorithm can mine all high utility patterns using the global revision header table and the utility tree. Finally, the redundant items are deleted by revising the global revision header table, meanwhile the utility tree is updated to fill in new data. This paper compares the algorithm with the existing high efficiency algorithm HUPMS and HUM-UT on the three datasets with different sparse: Mushroom, T10.I4.D100K and Retail. The results show that the space-time performance of HUIGRT algorithm is better than the two other algorithms.
Chance-Constrained Approach to Optimal Path Planning for Urban UAS

In this paper, the problem of optimal path planning for Unmanned Aerial Systems (UAS) in presence of uncertain keep-out zones is presented. Constrained optimal control problems are heavily reliant on the initial guess path provided to the optimal control solver, which affects both the computation time and the optimality of the solution. The constrained Delaunay triangulation mesh approach used in the finite element analysis is leveraged for the initial guess path generation. The chance constraints are transformed to deterministic counterparts using the inverse cumulative distribution function of the random parameter and an optimal solution is obtained. By varying the allowable risk, a family of risk-aware solutions is generated to aid the decision making process.
Trajectory-based Agile Multi UAV Coordination through Time Synchronisation

In this work, we propose a multi-UAV coordination methodology with trajectory synchronization between the coordination, planning, and control layers. We use probabilistic trajectory mappings (PTM) to find collision-free trajectories through the obstacles. For trajectory tracking, we apply the differential flatness modeling, allowing us to directly map the desired output trajectory, which is parameterized with cubic B-Spline, into the required input sequence. The local support property of B-Splines enables to re-plan of the trajectories without deteriorating entire the shape of the path and the continuity over it. Through this fashion, the trajectory tracking controller generates a required input sequence for given velocity references over the B-Spline sequences, which can be performed by the vehicle. The coordination between the multi-UAV is provided through time synchronization between the trajectories, which are parameterized with B-Splines knot scales and their projected derivatives. We performed experiments in a realistic environment under a motion capture system with multiple nano-size aerial vehicles. The results are given to show the success of the presented methodology for challenging problems such as passing through a small virtual circle in the air for given time sequences.
Cryptographic Algorithms to Mitigate the Risks of Database in the Management of a Smart City

It was analyzed the information of databases, encryption algorithms, and information security that are the main components of a Smart City. The problem is the lack of security of public or secret data that is stored in a repository, where there is access to the citizen, local government, and central government. The objective is to define a security prototype and adopt a cryptographic algorithm to mitigate the risks of the database in the management of a Smart City. It was used the deductive method and exploratory research to analyze the information of the reference articles. It turned out a general prototype of security; an algorithm to implement a database for Smart City; an algorithm of data protection expressed in flowcharts. It was concluded that information protected with an encryption algorithm, is a support to be more efficient, reduce costs, reduce the environmental footprint and improve the management of a Smart City.