Reviewerly/exHarmony huggingface dataset:
Intellectual Property Rights Protection in Cyberspace

Development and advancement in information technologies have paved the path for many challenges for the intellectual property rights holders. There are several forms of cybercrimes such as pornography, stalking, cyber fraud, cyber terrorism, etc., that are affecting people, hurdling e-commerce, challenging law, and disturbing the channel of information and communication. No doubt that cybercrimes are offences where the computer is the means of the commission of the offence as well as a target of the offence. Apparently, such offences are generated through electronic means where mens rea has no role to play. This unruly horse is creating several problems in the world of intellectual property, which has the capacity to affect global commerce. This chapter will focus on Indian case laws to showcase the interface between IPR and cyberspace. Also the dealing of issues like cybersqatting, cyberbullying, cyber theft will be discussed in order to check the competency of IPR.
Analysis of Sentence Boundary of the Host's Spoken Language Based on Semantic Orientation Pointwise Mutual Information Algorithm

Detecting dependencies in spontaneous speech is much more challenging than in written texts. The biggest problem is the unclear boundary of sentences. In this paper, an algorithm that relies on the semantic orientation pointwise mutual information is proposed to achieve an F-measure of 84.9, which has improved the accuracy of sentence boundary detection. Based on the sentence boundaries detected automatically, the accuracy of dependency structure analysis is also improved from 75.2% to 77.2%. Through the interactive use of dependency structures and sentence boundaries detected automatically, the nine-tenths of dependency structure analysis and sentence boundary detection has also been improved.
Clustering Data Using Techniques of Image Processing Erode and Dilate to Avoid the Use of Euclidean Distance

Clustering is one of the most popular methods of machine learning. The process of clustering involves the division of a set of abstract objects into a certain number of groups which integrated with objects of similar characteristics. Therefore, a cluster integrates objects which are similar to them, but dissimilar to the elements that belong to the rest of the clusters. Several clustering methods have proposed in the literature with different performance levels. All these techniques use as similarity criterion the Euclidean distance among cluster elements. However, there exist diverse scenarios where the Euclidean distance cannot be utilized appropriately to separate the elements in groups. Under such conditions, traditional cluster methods cannot directly apply. On the other hand, the operations of dilate and erode are a set of non-linear operators that modify the shape of a data group in the feature space, to obtain a monolithic object. Although morphological operations have demonstrated its importance in several engineering fields as image processing, its use as a clustering technique has been practically overlooked. In this work, an alternative clustering algorithm is proposed to group elements without considering the distance as a similarity criterion. In our approach, the data were separated into different groups by considering morphological operations. Under this scheme, the procedure allows the integration of data points, which present a spatial connection. Since the proposed algorithm does not use the distance in its functioning, it solves complex clustering problems which traditional clustering algorithms cannot.
Privacy Preservation in ROI of Medical Images Using LSB Manipulation

In the current era of digitization, the medical images are getting transmitted over the communication channel for diagnosis. During this process, it can be modified accidentally or intentionally. This occurs because of the insecure Internet network, which ultimately affects the sent image information. Thus, it results in wrong diagnostic decisions. Here, the basic purpose is to secure image transmission, without changing the sensitive areas of images that carry medical information. One of the possible watermarking methods to avoid erroneous diagnosis and checking the reliability of the received medical image is region-based analysis using ROI. The ROI of medical image is irregularly figured which contains important information. This paper tries to analyze these issues and attempts to overcome such problems by recovering secure medical images.
Extended PrefixSpan for Efficient Sequential Pattern Mining in a Game-based Learning Environment

This paper proposed an extended version of PrefixSpan as a better sequential pattern mining for a game-based learning environment (GBLE). The extended version of PrefixSpan evolved on integrating time interval constraints, clustering valued actions and extracting the closed sequences. These three concepts were derived after a previous work showed limitations of PrefixSpan in generating sequence patterns that can be used in tutoring services of a GBLE. The extended PrefixSpan underwent two phases of evaluation, performance evaluation and analyzing the quality of generated sequence patterns. The evaluation results showed that the extended versions provided a significant improvement in terms of execution time and the number of generated sequence patterns. Lastly, it shows significant improvement in the quality of sequence patterns generated as shown in better tutoring service it provided after integrating it to the GBLE.
A Novel Spatial Layout Representation for Object Recognition

Most successful approaches on image classification apply the Bag-of-Words (BoW) approach in the context of category-level image classification. To incorporate spatial image information in the BoW model, Spatial Pyramids (SPs) are used. However, spatial pyramids are rigid in nature and are based on predefined grid configurations. As a consequence, they often fail to coincide with the underlying spatial structure of images from different categories which may negatively affect the classification accuracy. This study proposes a technique that addresses this problem by presenting a selective Spatial Pyramid (SP) representation for automatically learning the most appropriate shape for each category. The proposed approach provides an image representation by inferring the constituent geometrical parts. As a result, the image representation retains the descriptive spatial information to yield a structural description of the image. From large scale experiments on the Pascal VOC2007 and Caltech101, it can be derived that SPs which are obtained by selective search outperforms the standard SPs.
Elixir: An Agent for Supporting Elasticity in Docker Swarm

Elixir is an autonomous agent that extends the capabilities of Docker Swarm in certain ways: (a) it supports monitoring of resources in real-time, (b) introduces a scheduler for balancing the workload among swarm nodes and, most importantly, (c) supports automatic scaling-up (or down) of worker nodes at run-time with no downtime (i.e. without stopping the system). The proposed scaling strategy is reactive and works by responding to static (pre-defined) thresholds applying on resource metrics (e.g. RAM, CPU). The experimental results demonstrate that Docker Swarm with Elixir responds to the increasing (or decreasing) resource demands of each application leading to significantly faster response times compared to its non-auto scaled counterpart, while maintaining optimal allocation of resources per application.
Stemming algorithm for Kazakh Language using rule-based approach

This paper considers construction of Kazakh language words. Kazakh language is the type of Turkic languages or alternatively called - agglutinative language where words are formed by attaching the finite sequence of suffixes. Kazakh morphology investigation was the main work done in construction of rule-based stemming algorithm. Stemmer algorithm will be useful in development of sentiment analysis for Kazakh language, because of its rareness and competency. Stemming algorithm works with aim to cut ending combinations. The results checked by using Machine Learning algorithms using annotated dataset, which were retrieved from KazCorpus dataset [7].
Analysis and design of transparent smart contract based on blockchain technology for supply chain in “Gasol flour” industry

Abstract The current contract system that has been running in a supply chain is not necessarily going well. Particularly, this contract was attributed by unfairness distribution of margins in each stakeholder, which causes one of the stakeholders took place as disadvantaged ones. This challenges potentially overcame by industry 4.0 paradigm constructing a smart contract system based on blockchain technology. This study aims to analyze the task of each stakeholder to achieve a transparent smart contract system and design system for a transparent smart contract that is able to provide a fair and transparent contract for each stakeholder involved in the supply chain of “Gasol flour” industry. By the existence of a smart contract, each stakeholder visibly and determine what contracts to establish and to create with other parties a transparently. The relief method used in this research provides input in the form of features needed to make a contract system in the supply chain of “Gasol flour” industry. Furthermore, these features are selected to obtain an output in the form of contract items to display in the distributed smart contract system. The selected items are input to run IF-THEN logic on the smart contract system using the M5P Tree algorithm method. The main results of this study are to obtain features that have the most influence on a transparent smart contract system and the rule of the selected feature so that a system is able to provide a fair contract for each stakeholder established in the supply chain of “Gasol flour” industry.
Meta-learning of Textual Representations

Recent progress in AutoML has lead to state-of-the-art methods (e.g., AutoSKLearn) that can be readily used by non-experts to approach any supervised learning problem. Whereas these methods are quite effective, they are still limited in the sense that they work for tabular (matrix formatted) data only. This paper describes one step forward in trying to automate the design of supervised learning methods in the context of text mining. We introduce a meta learning methodology for automatically obtaining a representation for text mining tasks starting from raw text. We report experiments considering 60 different textual representations and more than 80 text mining datasets associated to a wide variety of tasks. Experimental results show the proposed methodology is a promising solution to obtain highly effective off the shell text classification pipelines.
Design and Implementation of Deep Learning Based Pupil Tracking Technology for Application of Visible-Light Wearable Eye Tracker

In this paper, a deep-learning based pupil tracking technique is developed for the application of visible-light wearable eye trackers. By applying You only look once (YOLO) based deep learning object detection technology, the proposed pupil tracking method estimates and predicts the centers of pupils effectively in the visible-light mode. By testing the pupil tracking performance with the developed inference model, the precision is up to 80%, and the recall is close to 83%. Besides, the average horizontal and vertical pupil tracking errors of the deep-learning based design are only 4 pixels, which are much less than those of the previous ellipse fitting based design at the same visible-light conditions.
Automatic Generation of Survey Paper Based on Template Tree

With rapid expansion of scientific papers, making a survey from a large collection of papers on a given research issue or domain becomes more and more important for researchers. This paper proposes a template-based framework for automatically generating survey paper. It allows users to compose a template tree as a syllabus for the required survey. Each tree node corresponds to a section to be composed in the survey therefore the whole tree defines the section structure of the survey. The template consists of two types of nodes, dimension node and topic node, which filter contents of papers. A recursive procedure along the survey generation template tree paths is conducted to process documents, rank sentences, and compose sections. We apply the approach to generating the survey of the reference papers of a survey paper and compare the result with the survey paper. Experiments show improvement over several baseline methods.
Multi-agent Reinforcement Learning with Biased Experience Sharing in Swarm-robotics Domain

Reinforcement learning has been widely applied to solve a diverse set of robot learning tasks. Experience sharing has become an important technique for collaborative multi-robot systems in non-deterministic environments. However, one of the main problems for multi-agent reinforcement learning (MARL) is the large state-action space, which leads to low convergence and even learning failures. This problem is especially true for multi-robot learning with limited communication and mobility. In this paper, a Bilateral Biased Neighbors-Sharing Cooperative Reinforcement Learning (BBNS-CRL) method is presented to accelerate learning process by integrating the neighboring robots' knowledge with local knowledge. The BBNS-CRL method can be applied in collaborative task with multiple homogeneous robots, which have limited communication distance and dynamic neighboring relationship. The value function is shared between two adjacent robots with a biased sharing method. Simulation results on a cooperative multi-robot foraging task show that a better learning convergence can be achieved by adopting BBNS-CRL method.
To Shift Tasks or To Shift Energy by ESDs? An Economical Scheduling for Cloud Data Center

In this paper, we propose a novel scheduling scheme to minimize the electricity cost for cloud data center by making the best of time-varying electricity prices of the power grid. In our model, the tasks may have different arrival time, execution time and deadlines, so that the energy cost can be reduced through task shifting within their deadlines. In addition, the energy cost can also be reduced through energy shifting when its price is low using energy storage devices (ESDs). However, it is challenging to decide whether to shift tasks or to shift energy using ESDs considering the fluctuating prices of the electricity. Note that, there are energy losses during charging, discharging and self-discharging of the ESDs, which can not be ignored. We solve the problem in two steps: 1) schedule the tasks based on the electricity prices within a certain time; 2) model the scheduling of energy into a constraint optimization problem and solve it using CPLEX. Experiment results show that our scheduling can significantly reduce electricity cost for a cloud data center.
The Road to Quantum Computational Supremacy

We present an idiosyncratic view of the race for quantum computational supremacy. Google’s approach and IBM challenge are examined. An unexpected side effect of the race is the significant progress in designing fast classical algorithms. Quantum supremacy, if achieved, won’t make classical computing obsolete.
Efficient and Secure Data Access Control in the Cloud Environment

Cloud computing is a highly demanding zone in the present IT enterprise. The key characteristics are adaptability, productivity, pay-per-utilize, and cost viability. Access control and information security are the significant issues in cloud computing. Various access control strategies are present. The major problems of storing data in expandable access control and access rights from users are not used in this scheme. This chapter presents a generic survey on scalable and secure access control systems and schemes in cloud computing with a key focus on cloud security. Research gaps in the existing literature on cloud security are presented.
Handwriting Feature Extraction via Legendre-Sobolev Matrix Representation

The two-dimensional nature of mathematical formulas and the large set of mathematical characters with different variations in style and size form the main challenges in mathematical handwriting recognition. To address these difficulties, the way handwritten data is represented and the methods to compute certain features from the chosen representation are important questions. To this aim, we treat handwritten characters as approximated parametrized coordinate curves in Legendre-Sobolev bases. This representation empowers us to study the geometrical features of handwritten characters as a whole. These geometrical features are used to find to baselines, bounding boxes, loops, and cusps appearing in handwritten characters. Computing these features relies on mathematical operations such as derivatives, roots, and gcds of polynomials. This paper studies the problem of computing these operations without the conversion to the monomial basis, seeking improvement in speed and accuracy.
Virtual Reality Interface for Visual Art with a Five-Fingered Haptic Controller

In this study, we designed an interface that uses a haptic controller that can control five fingers in virtual reality (VR) and studied the user interface for visual art VR application. The haptic controller interface consists of a haptic controller interface and application module and game engine module. The interface module of the controller measures the pressure sensors and controls the movement of the linear actuator and vibration feedback. The game engine module facilitates the physics-based interaction between the haptic controller and virtual object in the Unity game engine. We examined the artists' actions, as performed with the five fingers. Subsequently, using the proposed haptic controller in three modes, spray, brush, and sculpt, we defined the user interface for visual art application.
TF-BiLSTMS2S: A Chinese Text Summarization Model

In this paper, a method of text summarization based on deep learning is proposed. The method combines the topic information into the Bidirectional LSTM sequence to sequence model and provide thematic and context alignment information into the deep learning architecture to better deal with the long-term dependencies of text summarization, so as to avoid missing the topic words and the relationship between words in the syntactic structure of source text, help the model generate more coherent, informative, and more relevant summary of the topic information. This method is called a Bidirectional LSTM Sequence-to-Sequence Model based on Topic Fusion (TF-BiLSTMS2S). In the experiments, we use the Large-scale Chinese Short Text Summarization Dataset (LCSTS) to evaluate the model, and the ROUGE index was used to evaluate the results. The experimental results show that the proposed model is effective and feasible for abstractive text summarization.
Efficiently Detecting Web Spambots in a Temporally Annotated Sequence

Web spambots are becoming more advanced, utilizing techniques that can defeat existing spam detection algorithms. These techniques include performing a series of malicious actions with variable time delays, repeating the same series of malicious actions multiple times, and interleaving legitimate (decoy) and malicious actions. Existing methods that are based on string pattern matching are not able to detect spambots that use these techniques. In response, we define a new problem to detect spambots utilizing the aforementioned techniques and propose an efficient algorithm to solve it. Given a dictionary of temporally annotated sequences $$\hat{S}$$ modeling spambot actions, each associated with a time window, a long, temporally annotated sequence T modeling a user action log, and parameters f and k, our problem seeks to detect each sequence in $$\hat{S}$$ that occurs in T at least f times within its associated time window, and with at most k mismatches. Our algorithm solves the problem exactly, it requires linear time and space, and it employs advanced data structures and the Kangaroo method, to deal with the problem efficiently.
Constructing Movie Domain Knowledge Graph Based on LOD

Domain-specific knowledge graphs can represent complex domain knowledge in a structured format and have achieved great success in practical applications. Recently, knowledge graphs have been widely used in recommender systems because of their ability to integrate various recommendation models and deal with data sparseness and cold-start problems. In this paper, we propose an approach to extract movie related information from Linked Open Data (LOD) and construct the knowledge graph of movie domain. And the Neo4j graph database, which is characterized by friendly user interface and quick inquiry, is used to visualize the knowledge graph.
Apply A* Search for Robot Path Finding

Path planning for robot can be useful, it can save time consumed for a robot to move to destination or get stuck by an obstacle. If the robot knows where the obstacles are and knows which way is clear to move to, it can help the robot get to the destination faster and more precise. The objective of this project is to create an algorithm for a robot to plan a path from one position to the destination. This project has four approaches for creating path planning for robot, direction angle algorithm, packet forwarding like algorithm, A* search algorithm, and A* without using GPS algorithm. The later approaches will improve disadvantages in former approaches. This project is created in simulation program called Webots. Result from the last approach can make a robot plan a path from one position to the destination and avoid obstacles when they are detected. However, it is still not very accurate in the positioning system because the robot cannot get its exact position.
Building-Specific Recognition Algorithms Based on Image Sequence

Building extraction, recognition and corresponding three-dimensional modeling in image is an active research field, which is mainly applied to map feature extraction, urban population survey and so on. It has important significance in military and civil areas. Aiming at the shortcoming of the classical invariant feature extraction algorithm, a building recognition algorithm based on local feature and shape contour matching is proposed. Firstly, according to the fact that the whole building is susceptible to rotation and inclination, the local feature points of known buildings are extracted, and their orientation, position and angle are determined. At the same time, the similarity criterion and mapping function of arbitrary shape contour matching are formulated according to the scale change information of buildings in different states, and the building recognition under different illumination and scales is realized. The simulation results show that the method can quickly and accurately adapt to the construction in different environments. The accuracy of this method fluctuates around 97%, while the average accuracy of 20 experiments is 96.56%. The average accuracy of the SFBR method is 94.66%. Compared with the SFBR and SIFT methods, the average accuracy of the method is improved by nearly 2%.