Reviewerly/exHarmony huggingface dataset:
Efficient Language Identification for All-Language Internet News

The rapid development of language science and computing technology, especially the popularization of broadband Internet, has caused the explosion of all-language news to spread and communicate faster and faster. Among multi-modal news such as text, image, audio, and video, text news still accounts for the largest proportion of Internet news. In the face of more than 7,000 existing human languages, efficiently identifying the language of text news has become the most basic natural language processing technology, which can select accurate language processing methods for subsequent in-depth content processing and network public opinion analysis. Based on the idea of N-Gram, we designed and implemented a set of language identification methods suitable for all-language Internet news from two aspects: language training and language identification, and applied it to actual text news preprocessing. The language identification results of all-language Internet news show that our method has good recognition accuracy and efficiency.
Innovation in Product and Service by Applying TRIZ

Today in a quickly changing, profoundly violent, and questionable financial climate, organizations and associations should ponder systems of an orderly and steady mix of advancement inside their business structures, as a significant condition for supportable turn of events. Today TRIZ is rapidly getting as a vivacious framework for portraying and settling inconvenient issues that stay as limits to the item, cycle, administration, and business improvement. TRIZ is used to create inventive arrangements in a combination of corporate settings under a variety of conditions. TRIZ instruments might be utilized in developing items just as in improving and planning new services. This paper presents two cases for applying the Theory of Inventive Problem Solving (TRIZ), initial one for enhancing and working on an item and the second one for planning another and better assistance and hence can assist different associations with discovering thoughts and answers for improving and advancing their items and administrations.
Domain-specific Evaluation Dataset Generator for Multilingual Text Analysis

Web enables to retrieve concise information about specific entities including people, organizations, movies and their features. Additionally, large amount of Web resources generally lies on a unstructured form and it tackles to find critical information for specific entities. Text analysis approaches such as Named Entity Recognizer and Entity Linking aim to identify entities and link them to relevant entities in the given knowledge base. To evaluate these approaches, there are a vast amount of general purpose benchmark datasets. However, it is difficult to evaluate domain-specific approaches due to lack of evaluation datasets for specific domains. This study presents WeDGeM that is a multilingual evaluation set generator for specific domains exploiting Wikipedia category pages and DBpedia hierarchy. Also, Wikipedia disambiguation pages are used to adjust the ambiguity level of the generated texts. Based on this generated test data, a use case for well-known Entity Linking systems supporting Turkish texts are evaluated in the movie domain.
Campus Network Examination System Based on B/S Structure

The network examination system based on Web technology has greatly promoted the development of standardized examination. Based on the introduction of the B/S structure, this paper studies the information management mode of the campus network, the design and implementation of the campus network examination system based on the B/S structure, as well as the key technologies of the design, which provides the conditions for ensuring the fairness, security, flexibility and objectivity of the examination.
Belief revision as a problem of scientific epistemology

The fundamental question that must be answered by any theory of knowledge that claims to be adequate is the question of how it is possible to change our knowledge. The very fact of change undoubtedly takes place, and the problem is to theoretically explicate this fact. The methodological significance of this issue is due to the fact that changing knowledge means nothing more than its development, namely, the question of the ways and means of developing our knowledge is of central importance both for the logic and methodology of science, and for general epistemology. This work is of a review character, and aims to draw the reader’s attention to a new promising direction in the modern theory of knowledge called “belief revision”.
Dac-Pre: Practical Anonymous Data Access Scheme Control with Proxy Re-Encryption For Implantable Medical Devices

Data access control is one of the most essential key components of ensuring data security. The data access control is a selective restriction of data access, and it usually comprises two main security components: authentication and authorization. In this paper, we first propose an efficient data access control scheme for implantable medical devices. The protocol ensures anonymous data access control through a signcryption scheme with proxy reencryption (DAC-PRE) and supports the anonymity behavior of the user. Then the security of the scheme is proven in the random oracle model. Finally, We analyze the proposed protocol, and the results show that our scheme has very a low cost with respect to the computation time, communication overhead, as well as energy consumption.
Standard Analysis of Document Control as Information According to ISO 27001 2013 in PT XYZ

Documentation is part of a series of information systems. PT XYZ has a lot of information from various areas of the organization that need to be documented in the form of softcopy or hardcopy. Information security in documentation requires the application of standards in document control so that the information held can be stored and maintained properly. The application of information security in documentation can comply with ISO 27001; 2013 clause 7.5 regarding documented information. PT XYZ strives to apply these standards accordingly so that the documentation in information storage can be maintained properly. According to our results, information documents must conform to information security management system requirements in terms of confidentiality, transparency, and availability in terms of access to documentation, record owner liability, and document destruction.
The Increasing Inevitability of IoT in Remote Disaster Monitoring Applications

Disaster is a term used for an unusual event that has a negative impact on life, environment, and materials. This event is more unwanted than unusual as most of the time this is caused due to reasons, we remain unaware of. Disasters could be natural or man-made like floods, earthquakes, volcanic eruptions, accidents, gas leakage, forest fire, etc. Since the disaster occurs due to reasons that are unknown and is an untimely phenomenon, we have a lesser scope of avoiding it. The use of emerging technologies, early warnings, immediate incidence response, and post-recovery activities can well be employed to mitigate the losses that the disaster would lead to. One such emerging technology is the Internet of things (IoT). The sensor nodes used in an IoT architecture independently are capable of sensing the environment and gathering data. This data can be further sent to the sink node/base station which has the responsibility of reporting the data ahead of the network, for initiating actuation on the basis of the data gathered and analyzing the data. This can help in monitoring for the purpose of early warning of disasters. The actuations can help provide immediate incidence response. Furthermore, upon the integration of data analytics with IoT, the data generated with the help of sensors can be used for predictive analysis and inference drawing in order to enable early disaster prediction and mitigation of losses that might occur due to the same. Thus, the potential of IoT is to provide rescue, response, mitigation, and preparedness to manage a disaster. This chapter explains the role of IoT in disaster management along with proposing a generic model having distinct layers through well-defined functionality. The chapter also explains the integration of cloud and IoT that could improve the efficiency of IoT applications in disaster management.
Evolution of 3D images from 2D images

To present a model, which will reshape the raw 3D specific single-view images by learning the deformable object categories using unsupervised techniques. Using Convolutional autoencoders, each raw image will be taken as input, then this will be compressed by considering factors such as depth, albedo, viewpoint and illumination of a particular input. Autoencoders are basically used for efficient data codings in an unsupervised manner. These components can disentangled without any labels or supervisor based on the symmetric structure of the object. This model shows the reasoning about illumination which allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading. Furthermore, this model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model. This model can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. On benchmarks, model will be demonstrated with superior accuracy when compared to another method that uses supervision at the level of 2D image correspondences.
Mediating Community in Bukit Brown

This chapter seeks to illustrate the authors’ initiative of deploying mobile communication technologies through the ‘iBBC’ app to locate and reference tombstones of prominent historical personalities in Singapore’s Bukit Brown Cemetery. The densely vegetated, 80-year-old former Chinese municipal cemetery filled with more than a hundred thousand graves has been largely neglected, and the traditional Chinese inscriptions written on many of the tombstones are inscrutable to many contemporary visitors. As part of the process of digital interventions, iBBC helps visitors obtain encyclopedic information immediately on-site by using Augmented Reality (AR) to recognize selected tomb monuments. Such interventions are critical in sensitizing the public to the cemetery’s cultural heritage.
Network Anomaly Detection Method Based on Joint Optimization of GAN and Classifier in Few-shot Scenarios

Research into network anomaly detection methods in few-shot scenarios is of great practical significance, since there are relatively rare network anomaly events compared with normal ones. Sample augmentation based on GAN (Generative Adversarial network) has few requirements for domain knowledge and data types, and also has strong universality. In order to address the lack of guidance in hyperparameters optimizing for sample augmentation based on GAN, the joint optimization of GAN and classifier is introduced to realize the network anomaly sample augmentation and anomaly detection. Firstly, the network anomaly sample augmentation model and network anomaly detection classifier based on GAN are designed. Subsequently, hyperparameters trained with the sample augmentation model are optimized and selected according to the performance of the classifier on the augmented sample set. Finally, experiments are conducted on two types of datasets, namely network performance parameter characteristics and network traffic data characteristics. In accordance with the experimental results, the proposed sample augmentation method based on the joint optimization could effectively improve the accuracy of network anomaly detection in the few-shot scenarios.
A Scalable Data Provenance Mechanism for Cloud Environment using Ethereum Blockchain

Cloud computing has become an important technology for the processing of large computationally expensive programs and data from real-world. IT organizations are en-trusting the cloud vendors for the security of their computational infrastructures including the data. Thus, there is a need for better provenance assurance for the data present in the cloud for better security and to establish trust between the cloud vendors and customers. Blockchain technology operates in a decentralized way for building trust between the entities in the system using immutable ledger. In this work, we propose the decentralized and trusted cloud data provenance mechanism using Ethereum blockchain platform, IPFS and scalable consensus mechanism. PoW is the consensus algorithm currently used in Ethereum Blockchain. PoW consensus mechanism needs lot of computational power for the processing of data in the Blockchain. Thus, we implement Proof of Stake (POS) consensus algorithm in Ethereum to improve the efficiency of the proposed data provenance mechanism. We demonstrate the performance of PoS-enabled provenance framework in a multi-node testbed. The results reveal that POS performs better than PoW for cloud data provenance.
Quantum Algorithm for Linear Regression for Large Feature-Set

Linear Regression is a widely used optimization problem with many practical applications. Adiabatic Quantum Computers are excellent at solving such optimization problems. Current quantum-based solutions allow for datasets having only a small number of features (maximum 32). We have come up with a novel approach that allows for a greater number of features for Linear Regression. We have found that our approach works for a large number of features, ranging from 64 to 128. We obtained a reasonably similar MSE (Mean Squared Error) to that of sklearn’s standard Linear Regression class, with a difference that lies between 0.01 and 0.03.
An Adaptive Pan-Tilt Camera Control for Visual Target Tracking

The problem of target tracking with an active monocular camera is considered in this paper. A model of the Pan-Tilt camera unit is developed using homogeneous transformation matrices. Using target offset (error) coordinates in the image plane, an adaptive control is proposed for the active camera system to track a moving target and keeping its image in the camera view. To illustrate the effectiveness of the proposed controller, simulation and experimental results are presented.
Advanced methods for robust object detection

Object detection is one of the most important and challenging problems in computer vision. The task is to localize every object of interest in a natural image. This has many applications, including surveillance, autonomous driving, robotics, medical imaging, etc. However, object detection is extremely challenging, due to the need to overcome problems such as scale-variance, occlusion, variable lighting, complex backgrounds and all the factors of variability associated with the natural world. The introduction of deep learning has enabled remarkable breakthroughs and led to the development of many advanced object detection algorithms in recent years. In this chapter, we review these advances.
Towards modeling structures of the game worlds by systems of graphs

The paper is the next step in the search for a formal role-playing game (RPG) model. The main contribution of this paper is a system of graphs, which clearly shows the structure of the formal RPG model proposed. With this system, the game world is optimized for easy application of game mechanics using graph rules. In addition, the system of four graphs, such as locations, characters, items and plots graphs, can be useful in the conceptual design phase of computer games because it allows for modifications at the level of component graphs. On the one hand, the holistic model of the game structure fully complies with the proposed formal graph model, on the other hand, its implementation in the Godot engine is performed to prove the concept. The formal definitions introduced in this paper will be illustrated with examples related to the presented case study.
Concurrent Spatial Color Information Processing for Video-Based Vehicle Detection Applications

The increase in the number of vehicles requires an efficient and well-organized solution for traffic management and regulation. Among several traffic management systems of Intelligent Transportation Systems (ITS), video-based system has gained wide popularity that uses computer vision techniques. One of the significant processes of computer vision technology is to detect vehicle from traffic videos. Effective image processing-based computer vision techniques are available to detect vehicles. The accuracy of the detection primarily depends on the section the video being processed for detection, which is completely neglected by the existing image processing technique. Hence, in this chapter, a Concurrent Spatial Color Information Processing System (CSCIPS) is proposed that uses a novel detection framework known as Virtual Multi-Loop Crates (VMLC). The proposed framework VMLC uses all the spatial color information for image processing technique concurrently without loss of any information. The performance of the proposed CSCIPS system with VMLC is analyzed with background subtraction-based image processing techniques in benchmark traffic videos. The results show better performance in processing time, storage space, and accuracy for vehicle detection when compared to other existing frameworks.
A Study on the Effects of AEO Certification Maintenance Level on the Business Outcomes: Focusing on the Mediating Effect of the Government Support Policy

Purpose - Considering the rapidly changing trade environment and the high trade dependence of the Korean economy, this study aims to continuously improve policies and systems to enhance corporate competitiveness by empirically studying the impact of AEO company certification maintenance levels on business outcomes. Design/Methodology/Approach - This study conducted literature research and empirical analysis at the same time. A research model was designed based on previous studies related to the background of the introduction of the AEO program, the current status of its introduction in each country, certification standards, and corporate performance. Based on this, a survey was conducted on AEO managers, and the hypothesis was verified by statistical analysis. Findings - The analysis result on the amount to which the government support policies affected business outcomes showed that if the companies accepted the benefits for AEO certificated companies provided by government and MRA policy directions positively, non-financial outcomes were improved, such as the enhancement of supply chain management, transaction competitiveness, and risk management. Research Implications - As a result of verification on the effect of bootstrap mediation, it was confirmed that through AEO support policies, the maintenance of AEO certification level improved both financial and non-financial outcomes, such as the enhancement of transaction competitiveness and risk management.Considering the rapidly changing trade environment and the high trade dependence of the Korean economy, this study aims to continuously improve policies and systems to enhance corporate competitiveness by empirically studying the impact of AEO companies’ certification maintenance levels on business outcomes.
Analysis of Resource Management Methods Based on Reinforcement Learning

As the scale of service-based applications rapidly grows over recent years, tremendous amount of user data is being generated on a daily basis, which needs to be processed by computing jobs. Distributed computing frameworks are extensively applied to efficiently process large-scale data using finite resources, which has consequently placed resource management at the center of attention for many researchers. Traditional heuristic-based resource management algorithms are widely used in the industry, while often require experts with rich experience to design and tune rules, which is usually a time- consuming process and difficult to be generalized to computing jobs with distinct natures and scales. With the immense successes of reinforcement learning (RL) in the fields of games, auto- driving, and robotics, researchers begin to model and learn the task of resource management through the perspectives of RL, which has been proven to outperform conventional methods by experimental results. In this paper, we aim to summarize the relevant background, introduce both the heuristic-based and RL- based algorithms and propose a few areas of improvement for future work to come.
Data collection in wireless sensor networks by ground robots with full freedom

This chapter discusses the path planning problem using mobile ground robots to support the operation of the WSNs. We focus on designing paths for mobile robots. Considering the mobility constraints of mobile robots, we introduce a concept of a viable path, which combines the concerns of both robots and sensor networks. We formulate the problem of planning the shortest viable path for a single robot as a variant of the DTSPN. Accordingly, we develop an SVPP algorithm. We further consider the problem of planning viable paths for multiple robots and present a k-SVPP algorithm. As the constraints of mobile robots and the sensor networks are both taken into account in the path planning phase, the created paths enable the robots to effectively and efficiently collect data from sensor nodes.
Dupopt: A Redundancy Video Analysis Mechanism

The video analysis based on Deep Neural Network (DNN) model has been widely deployed to provide various services in mobile applications. DNN-based video analysis with high accuracy requires massive computation power beyond the hardware capability of mobile devices. Offloading to an edge server or remote cloud server is a promising solution to solve the above challenge. However, offloading computation leads to higher network latency which will reduce analysis accuracy. This paper uses redundant transmission to reduce network latency in video analysis and proposes a mechanism named Dupopt, which can select the optimal combination of copy number and frame resolution to maximize analysis accuracy. Numerical results show that the mechanism Dupopt can significantly improve the accuracy of video analysis compared with the transmission mechanism without redundancy.
A Novel Requirements Prioritization Approach based on 360 Degree Feedback and Group Recommendation

Requirements’ prioritization (RP) is an important activity in software development and a crucial step towards making proper decisions for the software release planning. RP is performed by various categories of stakeholders, such as end-users, customers, developers, designers, managers etc. Numerous different techniques exist to prioritize requirements but a set of problems is present in almost every case: (i) hesitation or even lack of knowledge and inability of some stakeholders to evaluate the priorities of some candidate requirements, and (ii) difficulties for stakeholders in reaching consensus on the final RP. The aim of this paper is to present a novel RP approach that addresses these problems and supports the justification of stakeholders’ decisions. We categorize stakeholders in distinct sets according to their role in the software project and we ask them to follow a 360-Degree Feedback (360DF) assessment for calculating stakeholders’ weights. The RP approach considers as prioritization criteria the satisfaction/dissatisfaction of stakeholders from offering/not offering software requirements as part of next software release and utilizes techniques from Intuitionistic Fuzzy Sets (IFSs) to represent and handle the stakeholders’ hesitation/uncertainty. The RP approach also takes advantage from Recommender Systems (RS) to support stakeholders during the evaluation procedure and to assist them to reach consensus for the final prioritization list.