Reviewerly/exHarmony huggingface dataset:
Robust Visual Tracking with Reliable Object Information and Kalman Filter

Object information significantly affects the performance of visual tracking. However, it is difficult to obtain accurate target foreground information because of the existence of challenging scenarios, such as occlusion, background clutter, drastic change of appearance, and so forth. Traditional correlation filter methods roughly use linear interpolation to update the model, which may lead to the introduction of noise and the loss of reliable target information, resulting in the degradation of tracking performance. In this paper, we propose a novel robust visual tracking framework with reliable object information and Kalman filter (KF). Firstly, we analyze the reliability of the tracking process, calculate the confidence of the target information at the current estimated location, and determine whether it is necessary to carry out the online training and update step. Secondly, we also model the target motion between frames with a KF module, and use it to supplement the correlation filter estimation. Finally, in order to keep the most reliable target information of the first frame in the whole tracking process, we propose a new online training method, which can improve the robustness of the tracker. Extensive experiments on several benchmarks demonstrate the effectiveness and robustness of our proposed method, and our method achieves a comparable or better performance compared with several other state-of-the-art trackers.
Data Integrity of Radiology Images Over an Insecure Network Using AES Technique

Background: While transmitting the medical images in radiology information systems the adversary effect can break the CIA (Confidentiality, Integrity, and Availability) triads of information security. The objective of the study was to transmit the complete set of image objects in a dataset without data integrity violation. Methods: In this paper a hybrid cryptographic technique which combines the prime details from the patient dataset (stack of axial 2D images) and the Advanced Encryption Standard (AES) method has been proposed. The steps include a) Creating an artificial X-ray image (DRR) from the 3D volume, b) dividing the DRR image in x and y directions equally into four regions, c) applying the zig-zag pattern to each quadrant, and d) encryption of each quadrant with block cipher mode using the AES algorithm. After dataset transmission the DRR image was regenerated at the receiver and compared each of the deciphered blocks (transmitted ones) using the histogram technique. Results: The technique was tested on CT and MRI scans of sixty datasets. The image injection techniques, such as adding and deleting an image from the dataset and modifying the image pixels, were tested. The results were validated statistically using mean square error and histogram matching techniques. Conclusion: The combination of the DRR and the AES technique has ensured the secured transmission of the entire dataset and not an individual image.
The Potential of IFLA LRM and RDA Key Entities for Identification of Entities in Textual Documents of Cultural Heritage: The <i>RunA</i> Collection

This paper addresses the potential to employ IFLA Library Reference Model (LRM) and Resource Description and Access (RDA) key entity classes in annotating textual documents of cultural heritage (RunA collection). It describes methods (comparison and analysis) and procedures (identification of entity classes and annotation classes in unstructured data with a special annotation tool) for the evaluation of international data models based on the need to develop a cross-sectoral linked data collection. The article identifies key entities – works, agents (persons, institutions), concepts, places, timespans, and events – for reflecting simple or hierarchical relationships between different objects, which can be used as nodal points for semantic connection and networking in digital collections, including unstructured data sets, e.g., correspondence. The article concludes with a discussion of the challenges of correctly applying LRM’s WEMI structured entities to free-form textual data.
Motion Planning for Autonomous Driving With Extended Constrained Iterative LQR

Abstract Autonomous driving planning is a challenging problem when the environment is complicated. It is difficult for the planner to find a good trajectory that navigates autonomous cars safely with crowded surrounding vehicles. To solve this complicated problem, a fast algorithm that generates a high-quality, safe trajectory is necessary. Constrained Iterative Linear Quadratic Regulator (CILQR) is appropriate for this problem, and it successfully generates the required trajectory in realtime. However, CILQR has some deficiencies. Firstly, CILQR uses logarithmic barrier functions for hard constraints, which will cause numerical problems when the initial trajectory is infeasible. Secondly, the convergence speed is slowed with a bad initial trajectory, which might violate the real-time requirements. To address these problems, we propose the extended CILQR by adding two new features. The first one is using relaxed logarithmic barrier functions instead of the standard logarithmic barrier function to prevent numerical issues. The other one is adding an efficient initial trajectory creator to generate a good initial trajectory. Moreover, this initial trajectory helps CILQR to converge to a desired local optimum. These new features extend CILQR’s usage to more practical autonomous driving applications. Simulation results show that our algorithm is effective in challenging driving environments.
Analisis kesenjangan sistem manajemen keamanan informasi (SMKI) sebagai persiapan sertifikasi ISO/IEC 27001:2013 pada institusi pemerintah

The Madiun City Communication and Informatics Service (Diskominfo) is a government institution that has the responsibility for managing information and communication technology in the Madiun city government. As a government institution to serving and providing information to the public, Diskominfo Madiun City is vulnerable to information security threats that can hinder its performance. Information Security Management System ISO / IEC 2701: 2013 is a system that expected to be able to provide effectiveness and efficiency of information security management at Diskominfo Madiun city. This research aims to determine the current conditions and how the readiness of Diskominfo Madiun City to achieve ISO/IEC 27001:2013 certification. From the results of the gap analysis, it can be seen that the percentage of readiness of Diskominfo Madiun City is 71%, with a readiness range between 19% - 100%. The highest level of readiness is 100% on the requirements of clause 4 concerning the organizational context and clause 10 concerning improvements, where all information security requirements have been met. While the lowest readiness percentage is 19% which is shown in the requirements of clause 6 regarding planning. The gap analysis method is used to determine how far the ISO/IEC 27001:2013 requirements are fulfilled. The results of the gap analysis show the extent of the readiness of Diskominfo Madiun City to carry out ISO/IEC 27001:2013 certification. The results of the research indicate that Diskominfo Madiun City must improve its readiness for ISO/IEC 27001:2013 certification by fulfill the requirements of the required information security documents based on ISO/IEC 27001:2013 standards.
Distance education in universities: Lessons from the pandemic

The development of distance education has been the focus of researchers over the past years, but due to the COVID-2019 pandemic, the use of distance learning has become a vital task for the education system The massive forced transition of universities to distance learning has provided invaluable experience and made it possible to learn useful lessons The purpose of the article is to analyze the organization of distance learning for students in a pandemic The article focuses on the following questions: To what extent were universities ready for the transition to distance learning?;Can distance learning be able to replace full-time education without reducing the quality of education?, Does the current e-education system meet the requirements for education in the 21st century? The article is written on the basis of the results of questioning and interviewing students and teachers of four universities in Belgorod In the course of the conducted socio-pedagogical research, it was found that Belgorod educational organizations of higher education (universities), relying on the previously created base of distance learning, were able to promptly organize the implementation of curricula remotely and successfully complete the academic year At the same time, Belgorod universities do not consider distance education as a real alternative to the traditional one The study also showed that modern e-education in the form in which it exists does not fully meet the requirements of the technological revolutions of the 21st century, since it is aimed only at the transfer of information and does not contribute to the formation of a creative personality with a high degree of mobility © 2020 Ubiquity Press All rights reserved
AR-voice interaction research based on factor analysis

Voice gradually becomes a quick channel for people to input information. At the same time, the commercialization of augmented reality technology is advancing. While these two emerging technologies are maturing and combining, the interactive interface and methods in the corresponding environment have also greatly changed. This paper collects data that may affect the user's interactive experience in AR voice scenarios through experiments and questionnaires. With the help of the factor analysis, three key factors, and nine influencing factors of AR voice interaction are obtained. Evaluation indexes of interactive experience influencing factors in the AR voice environment are generated, and three modification schemes are drawn up according to analysis results. Through the analytic hierarchy process, the most worthwhile improving plan is selected. Finally, compared with the traditional interactive interface, four targeted design recommendations for the interactive design of the AR voice interaction are shown.
Enhancing Cybersecurity Capability in Local Governments through Competency-Based Education

Local government agencies face significant challenges related to cybersecurity. Advances in cybersecurity threats have been part of the difficulty, but government agencies, particularly communities, also face unique challenges given their broad mandates and challenges. This is particularly true in large metropolitan areas where activities cross many local and county jurisdictions yet require a coordinated and collaborative response. Local jurisdictions face differing cybersecurity challenges related to local issues such as criminal activity, population, and the mix of land use as well as external considerations such as ports, airports and international borders requiring enhanced coordination with state and federal authorities. Rapid ongoing changes in technology also provide a relentless pattern of change which must be managed. These widely differing challenges, along with resource constraints, lead neighboring communities to possess widely varying cybersecurity capabilities. This research project is an effort to speed the process of training and developing talent to meet these challenges.
Characterizing Cybersecurity Threats to Organizations in Support of Risk Mitigation Decisions

This study characterizes the cybersecurity threats experienced by organizations. Today's organizations face difficult decisions regarding the mitigation of cybersecurity threats. Taking different types of cybersecurity threats—physical, human, communication and data, and operational—this study applied topic modeling techniques on textual data gathered from 10-K reports of 87 organizations. A cluster analysis on the resultant topic weights showed three different clusters of organizations which were used to characterize the cybersecurity threats. Specifically, organizations in the manufacturing sector prioritized physical threats, whereas those in the information technology and finance sectors accorded greater importance to human, communication and data, and operational threats. This study offers preliminary characterization of cybersecurity threats that can be beneficial for organizational decision-makers. Implications for research and practice are discussed.
A Data-Driven and Biologically Inspired Preprocessing Scheme to Improve Visual Object Recognition

Autonomous object recognition in images is one of the most critical topics in security and commercial applications. Due to recent advances in visual neuroscience, the researchers tend to extend biologically plausible schemes to improve the accuracy of object recognition. Preprocessing is one part of the visual recognition system that has received much less attention. In this paper, we propose a new, simple, and biologically inspired pre processing technique by using the data-driven mechanism of visual attention. In this part, the responses of Retinal Ganglion Cells (RGCs) are simulated. After obtaining these responses, an efficient threshold is selected. Then, the points of the raw image with the most information are extracted according to it. Then, the new images with these points are created, and finally, by combining these images with entropy coefficients, the most salient object is located. After extracting appropriate features, the classifier categorizes the initial image into one of the predefined object categories. Our system was evaluated on the Caltech-101 dataset. Experimental results demonstrate the efficacy and effectiveness of this novel method of preprocessing.
Privacy Preserving Machine Learning Challenges and Solution Approach for Training Data in ERP Systems

The digital transformation is ubiquitous and pushing the case further for ERP companies to incorporate more machine learning algorithms in order to drive intelligent real-time decision-making capabilities. ERP systems have started incorporating machine learning use cases powered by the huge enterprise data, cloud and compute capabilities. However, privacy of data remains a challenge. Data privacy is at the core of a machine learning model that is trained on sensitive information. Not just for profit businesses, but even academic endeavors in the field of medicine cannot progress if they can’t access sensitive medical information in a privacy preserved format. Ramifications of applying a ML model without even fully understanding what is happening inside its hidden layers can be disastrous and the resulting perils can lead to legal consequences. Therefore, Privacy preserving AI techniques started evolving in last few years. The privacy preserving AI field is still growing and there is an understanding gap in organizations and individuals, which makes privacy breach or compromise a pervasive business challenge. This paper focuses on what are key challenges for ERP companies as far as Training machine learning models on their enterprise data is concerned. And how can these challenges be overcome by applying data anonymization and differential privacy techniques.
Application of Plane Homography for Vehicle Speed Calculations in Traffic Accident Investigations

The analysis of vehicle speeds at the moment of traffic accidents is becoming increasingly important. To solve difficult calculations arising from video analysis when the shooting direction of the camera is the same as the driving direction of the vehicles, an improved plane homography method is proposed. This method is based on detecting the frame rate of and extracting all of the key image frames from a surveillance video. Next, a normalizing approach for the control points is proposed to reduce the ill-conditions of matrix calculations when the perspective image is rectified to an orthographic image. The information in the rectified image is then measured and recorded by extracting the mark point information. Finally, the corresponding curve of time and speed for the examined vehicle is created, which can be used to analyze the average braking deceleration of the accident-causing vehicle. This method can directly determine the speed of a vehicle using videos of a traffic accident without any reference to the dimensions of the vehicle itself. Considering an actual traffic accident as an example, the driving trajectories and velocity curves for two vehicles are obtained using this method. The proposed method can effectively overcome the shortcomings of the commonly used speed recognition methods and it is easy to utilize.
Text-based Causality Modeling with a Conceptual Label in a Hierarchical Topic Structure Using Bayesian Rose Trees

This paper describes a method for constructing a causality model from review text data.Review text data include the evaluation factors of rating, and causality model extraction from text data is important for understanding the evaluation factors and their relationships.Several methods are available for extracting causality models by using a topic model.In particular, the method based on hierarchical latent Dirichlet allocation is useful for hierarchically comprehending causality structure.However, the depth of each topic in a hierarchical structure is forcefully pruned even if granularities differ for each topic.Thus, interpreting a hierarchical topic structure is difficult.To solve these problems, we construct a hierarchical topic structure with different depths by using Bayesian rose trees.Furthermore, we use conceptual labeling to add explicit semantics for each topic for interpretation.An experiment confirms that this model is accurate and interpretable using actual data.
Designing “Library Smartbot” to Support Information Services in Academic Library

This research aims to develop a smartbot prototype that can be used by libraries to help librarians improve their information service quality. This study will contribute to academic libraries, especially supporting information and reference services by providing quick responses and accurate answers. Librarians will benefit from this prototype and users will also receive fast and accurate information from the library. The method used in this study consists of two phases: Plan and Design. This paper focuses on the planning and design process of the library smartbot prototype. These activities are as follows: Plan, where strategic planning is carried out, including looking at the organizational structure in which the smartbot will be implemented, examining the technology in the library, and collecting data and information that will support the smartbot. The Design phase defines the tasks in the modeling process and building the prototype. Artificial intelligence studies are rarely conducted in the library field, especially in Indonesia.
A construction method of obstacle avoidance system for welfare vehicle by spatial mapping of driving environment using mixed reality

Recently, welfare vehicles are widely used among the aged. However, it is hard for inexperienced beginners or the aged to manipulate the welfare vehicle in narrow and crowded places. For the problem, automatic driving of welfare vehicles has been studied. The welfare vehicle drives on the mixed traffic area with humans. As the traffic area is so complicated for the automatic drive, the control method should be changed between manual driving and automatic driving according to the driving situation. In this paper, an automatic driving system with obstacle avoidance using Mixed Reality (MR) is proposed. The characteristic of the system is to use a virtual platoon control, i.e. the virtual vehicle drives avoiding obstacles on the head mounted display (HMD), and the welfare vehicle follows the virtual vehicle. The HMD detects the obstacles using the spatial mapping, and the avoiding path is planned using A* function of Unity. The effectiveness of proposed system is evaluated in some driving scenes.
Theoretically Expressive and Edge-aware Graph Learning

We propose a new Graph Neural Network that combines recent advancements in the field. We give theoretical contributions by proving that the model is strictly more general than the Graph Isomorphism Network and the Gated Graph Neural Network, as it can approximate the same functions and deal with arbitrary edge values. Then, we show how a single node information can flow through the graph unchanged.
Investigating the relationship between AI and trust in human-AI collaboration

With the increasing development of information technology, the implementation of artificial intelligence (AI) has been widespread and has empowered virtual team collaboration by increasing collaboration efficiency and achieving superior collaboration results in recent years.Trust in the process of human-AI interaction has been identified as a challenge for team collaboration in this context.However, little research has investigated the relationship between human-AI interaction and trust.This study proposes a theoretical model of the relationship between human-AI interaction and team members' trust during collaboration processes.We conclude that team members' cognitive and emotional perceptions during the interaction process are associated with their trust towards AI.Moreover, the relationship could also be moderated by the specific AI implementation traits.Our model provides a holistic view of human-AI interaction and its association with team members' trust in the context of team collaboration.
CC-Case-Safety and Security Engineering Methodology

As the complexity of computer systems increases, assuring safety and security is significant. The authors aim to construct a new development methodology CC-Case that can assure the demands of complex systems, including IoT and AI, using safety and security technologies in an integrated manner. As a central framework of CC-Case, this manuscript shows requirements extraction by STAMP/STPA extension to safety and security (STAMP S &amp; S) and assurance using GSN divided into a logical model and a concrete model. STAMP S &amp; S makes it possible to model requirements based on system theory and extract more comprehensive safety and security requirements in a single model diagram. Besides, the GSN defines the overall picture of the assurance and verifies and validates the hazards and threats extracted by STAMP S &amp; S. This paper presents the procedures of CC-Case with STAMP, GSN, and show examples of level 3 autonomous driving.
IaaS, PaaS, or SaaS? The Why of Cloud Computing Delivery Model Selection – Vignettes on the Post-Adoption of Cloud Computing

Most large-scale organizations adopted Cloud Computing (CC) on a company level in recent years.Managers now face the challenge to appropriately implement CC "operationally", i.e., for information systems (ISs).We refer to this as post-adoption, addressing the extent of technology usage after adoption.Specifically, managers need to choose among the CC delivery models Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-asa-Service (SaaS).We differentiate the determinants of this post-adoption decision for IaaS, PaaS, and SaaS.Based on this analysis, we derive criteria that guide managers' delivery model selection: Adopt 1) IaaS for ISs requiring flexibility and reduced time to market, 2) PaaS to access specialized resources, and 3) SaaS to focus on core competencies.Moreover, we analyze the impact on the CC strategy and postulate them as recommendations: I) acknowledge the interplay between governance and time-to-market, II) realize cost savings on company level, and III) consider strategically important ISs for CC.