Reviewerly/exHarmony huggingface dataset:
Object detection methods for Image-based Visual Servoing of 6-DOF Industrial robot

The employment of robots in manufacturing industries is on the rise due to the need for automated industrial operations. Visual servoing is a well-advanced method of controlling the robot movement based on visual sensor feedback. In image-based visual servoing (IBVS), the image features at the final pose implicitly define the desired camera pose relative to the target. The major issue associated with image-based visual servoing of industrial manipulators is the identification and tracking of object to be manipulated from the image sequence. In this paper, two methods are employed to detect the object for providing image feature vector in the visual servoing feedback loop. One method employs a robust and fast algorithm called SpeededUp Robust Features (SURF) for image comparison. Another method utilizes the method of comparing measuring the properties of the image region and comparing with the given image to detect the object. These algorithms are applied in the image-based visual servoing system for obtaining the image feature error to drive the system. Real-time experiments are conducted in IBVS system where IRB 1200 industrial robot manipulator is active with eye-in-hand camera. The results illustrate the improved performance of the object identification algorithms.
Overview of Image Inpainting Techniques: A Survey

Images that are corrupted during transmission or improper storage, can be restored via image inpainting by filling the missing/damaged region of images such that the observer can-not perceive the inpainted region. This paper provides an overview of the traditional methods and deep learning methods which have been used for inpainting task. The traditional methods could accurately fill the missing regions when the hole size is small but fails to inpaint large sized holes and also they cannot hallucinate novel contents. With the availability of huge computation power systems, the recent advancements in deep learning methods have shown exceptional results in image inpainting tasks. However, there is still room for improvement in this task in terms of the mask size of arbitrary shape and at arbitrary locations, reducing computational resources, reducing training time, generating high quality results etc.
Eye detection using Faster-RCNN

Eye detection is essential in many computer vision applications such as driver drowsiness detection, human behavior analysis, liveness detection, gaze estimation, etc. However, eye detection in a facial image is challenging due to face rotation, pose variation, scale variation, occlusion, etc. Therefore, Faster RCNN deep learning-based eye detection model is proposed under the occlusion and pose variation. Pre-processing was done using contrast stretching which makes the database better for detection. Fine-tuning of hyperparameters and augmentation makes the detector more accurate and robust. The effectiveness of the eye detection model was analyzed on the publicly available AR and GI4E databases. This model gives 98.32% and 98.11% accuracy for the AR and GI4E databases with a 0.52 ms/image computational time. Extensive experiments suggested that our proposed model resulted better than state-of-the-art methods for eye detection.
Continuous American Sign Language Recognition Using Computer Vision And Deep Learning Technologies

Sign language is a non-verbal communication method used to communicate between hard of hearing or deaf and ordinary people. Automatic Sign language detection is a complex computer vision problem due to the diversity of modern sign languages and variations in gesture positions, hand and finger form, and body part placements. This research paper aims to conduct a systematic experimental evaluation of computer vision-based approaches for sign language recognition. The present research focuses on mapping non-segmented video streams to glosses to gain insights into sign language recognition. The proposed machine learning model consists of Recurrent Neural Network (RNN) layers such as Long Short-Term Memory (LSTM). The model is implemented using current deep learning frameworks such as Google TensorFlow and Keras API.
Construction of Accounting Internal Control Management Platform Based on IoT Cloud Computing

The Internet of Things is a network that interconnects all items on the Internet through radiofrequency identification, infrared sensors, global positioning systems, laser scanners, and other sensing equipment according to the agreed communication protocol to achieve intelligent identification, positioning, analysis, monitoring, and management. Internal accounting control is an accounting measure established for the enterprise to identify and analyze the operating conditions and to be responsible for the enterprise assets. Accounting internal control is the core of internal control, and the rational use of internal control theory is conducive to promoting the healthy development of enterprises. According to the control object, it can be divided into financial risk control, operational risk control, human resource risk control, strategic risk control, and information distortion control. This paper is aimed at studying the construction of an accounting internal control management platform based on the Internet of Things cloud computing. How the internal accounting control system adapts to the development of the socialist market economy is the current research focus. In view of the current development status of the control system, this paper combines the internal control with the accounting information system and explores the enterprise management from the strategic management level and the operational management level. This paper mainly builds an accounting big data analysis platform based on cloud computing and believes that the platform should have the function of financial comprehensive analysis. The experiment in this paper found that the actual management cost of the company in 2017 was 2630 million yuan, and the predicted management cost was 2600 million yuan. The gap between the predicted data and the actual data is getting closer and closer, indicating that the prediction platform is effective.
Virtualization and Vice Versa: A New Procedural Model of the Reverse Virtualization for the User Behavior Tracking in the Virtual Museums

In this paper we present a method of the user behavior (UB) tracking by capturing and measuring user activities through the defined procedural model of the reverse virtualization process, implementing a proof of concept on a real case scenario: the Civic Gallery of Ascoli. In order to define the universal model of such “vice versa” virtual reality (VR) experience, we assigned particular descriptive functions (descriptors) to each interactive feature of the virtual user space. In this virtualization phase we store user interaction information locally using the web-socket streams protocol, ensuring complete control and manipulation of monitored functions. Our algorithm firstly collects the user interaction data and extracts the descriptors’ arguments into the indexed vector of corresponding variables. The next step determines UB pattern by solving the inverse descriptive functions in combination with an appropriate statistical analysis of gathered data. The final result of the proposed method is the repository of salient data that is used in the further user experience improvement, as well as to enable the museums to distinguish the most important points of the visitor interest in the virtual web tours. Our approach also offers a potential benefit of obtained results in an automatic calculation and prediction of UB patterns using artificial intelligence (AI).
Augmented Reality and 3D Printing for Archaeological Heritage: Evaluation of Visitor Experience

Augmented Reality (AR) and 3D printing have increasingly been used in archaeological and cultural heritage to make artifacts and environments accessible to the general public. This paper presents the case study of the Ljungaviken dog, an archaeological find of dog skeleton remains dated around 8000 years ago. The dog remains have been digitized using 3D scanning and displayed in an AR application. A physical replica has also been created with 3D printing. Both the AR application and the 3D printed copy have been shown in a temporary museum exhibition. In this paper, we present the visitors' experience evaluation based on a study with 42 participants. Aspects being evaluated are related to the realism, enjoyment, and easiness of use of the AR application. Moreover, the two media are compared in terms of understanding, visual quality, and experience satisfaction. The results show an overall positive experience for both the display solutions, with slightly higher scores for the AR application in the comparison. When asked about overall preference, the participants reported similar results between both media. Due to issues of displaying fragile objects in a museum setting, as well as recent restrictions following pandemic closures and availability, the results presented in this paper show a positive alternative towards using digital artifacts to showcase our cultural heritage.
Processing Physiological Sensor Data in Near Real-Time as Social Signals for Their Use on Social Virtual Reality Platforms

Social interactions increasingly shift to computer-mediated communication channels. Compared to face-to-face communication, their use suffers from a loss or distortion in the transmission of social signals, which are prerequisites of social interactions. Social virtual reality platforms offer users a variety of possibilities to express themselves verbally as well as non-verbally. Although these platforms take steps towards compensating the addressed communication gap, there is still high demand to ensure and further improve the correct transmission of social signals. To address this issue, we investigate the processing of physiological sensor data as social signals. This paper provides two major contributions: Firstly, a concept for processing physiological sensor data in near real-time as social signals. The concept enables the processing of physiological sensor data on an individual level as well as across all users. For both the individual user and the collective, single sensors or the data from the whole sensor cluster can be analysed, resulting in four ways of analysis. Secondly, we provide concrete suggestions for a software setup, based on an extensive analysis of available open source software, to support a potential future implementation of the proposed concept. The results of this work are highly relevant for social virtual reality platforms, especially since modern head-mounted displays are often already equipped with appropriate measurement sensors. Moreover, the results can also be transferred to numerous other media, applications and research fields concerned with processing physiological sensor data, which reinforces the provided added value.
A secure optimal placement strategy based on Monte Carlo simulation and hypothesis testing

The co-location attack evil pursues cloud computing until present. Our approach to address this challenge falls under the secure VM placement strategies which provide an a-priori knowledge about the security level. Besides, it gives the hand to control the maximum rate of the approved insecure placements. To do this, it uses the Monte Carlo simulation and hypothesis testing. We evaluate our approach for correctness in terms of security approximation and performance in terms of energy consumption. It outperforms the existing works in terms of energy consumption with a negligible time delay.
Concerns identified in code review: A fine-grained, faceted classification

Code review is a valuable software process that helps software practitioners to identify a variety of defects in code. Even though many code review tools and static analysis tools used to improve the efficiency of the process exist, code review is still costly. Understanding the types of defects that code reviews help to identify could reveal other means of cost improvement. Thus, our goal was to identify defect types detected in real-world code reviews, and the extent to which code review can be benefited from defect detection tools. To this end, we classified 417 comments from code reviews of 7 OSS Java projects using thematic analysis. We identified 116 defect types that we grouped into 15 groups to create a defect classification. Additionally, 38% of these defects could be automatically detected accurately. We learnt that even though many capable defect detection tools are available today, a substantial amount of defects that can be detected automatically, reach code review. Also, we identified several code review cost reduction opportunities.
Encrypted Traffic Detection: Beyond the Port Number Era

Internet service providers (ISP) rely on network traffic classifiers to provide secure and reliable connectivity for their users. Encrypted traffic introduces a challenge as attacks are no longer viable using classic Deep Packet Inspection (DPI) techniques. Distinguishing encrypted from non-encrypted traffic is the first step in addressing this challenge. Several attempts have been conducted to identify encrypted traffic. In this work, we compare the detection performance of DPI, traffic pattern, and randomness tests to identify encrypted traffic in different levels of granularity. In an experimental study, we evaluate these candidates and show that a traffic pattern-based classifier outperforms others for encryption detection.
Few-Shot Open-Set Traffic Classification Based on Self-Supervised Learning

Encrypted traffic classification is a key technology for network monitoring and management, and its recent research results are mostly based on deep learning. Due to the difficulty in obtaining sufficient labeled data, few-shot traffic classification has received considerable attention. However, most of the existing results have two defects. First, they are mostly based on the assumption of a labeled base dataset for pre-training. Second, they neglect the problem of unknown traffic discovery under open-set conditions. In this paper, aiming at the problem of few-shot open-set encrypted traffic classification, a corresponding framework FSOSTC is constructed under the condition of unsupervised pre-training. Two data augmentation methods for packet feature map are proposed to assist the pre-training through self-supervised learning, which is combined with parameter fine-tuning, unknown discovery and class extension strategies. Experiments on public datasets verify the effectiveness of FSOSTC. For the few-shot open-set malicious traffic classification task, the CSA reaches 95.41% and the AUROC reaches 0.8664.
Security Constraints on an Multi-agent System to Manage Users and Spaces in an Adaptive Environment System

An actual problem on an IoT adaptive systems is to manage user preferences and local actuators specifications. This paper uses a multi agent system to achieve a Smart Environment System, that supports interaction between persons and physical spaces, that users smartly adapt to their preferences in a transparent way. This work proposes also a set of security customization’s to secure the actuators and users on space, that has been developed using a multi agent system architecture with different features to achieve a solution that achieve all the proposed objectives.
Ambient Photomontage: Ambiently Superimposing Multiple Perspectives on a Human Visual Perspective

The paper presents a novel method named Ambient Photomontage to ambiently superimpose multiple human and nonhuman perspectives on a user’s current visual perspective. Ambient Photomontage is built on a digital platform for sharing human seeing, capturing the visual perspective of people in the world, and enabling us to share perspectives. Inspired by art, the presentation method of Ambient Photomontage enables people to defamiliarize daily unconscious seeing. Our preliminary user study presents some in-sights into the opportunities concerning Ambient Photomontage’s current design.
Towards a Model-Driven Ontology-Based Architecture for Generating Adaptive User Interfaces

With the advent in pervasive computing, the development of pervasive applications is more and more complex. Such applications require adaptive user interfaces that are able to cope with the heterogeneity of the context of use, regarding the user, the platform, and the environment. Many works have sparked their interest on model-driven approaches to create adaptive user interfaces. Even, while these approaches provide support for user interface modeling and transformation to final applications, they omit the use of the semantic in their user interface models and do not consider reusing knowledge within their design and generation process. In this paper, we integrate model-driven and ontology-based approaches to promote knowledge reuse and enhance the semantics of the developed user interfaces. More specifically, we introduce a model-driven ontology-based architecture for generating adaptive user interfaces. The present architecture is based on semantic models, including the user interface, the user context, and the adaptation rule ontology models. In particular, the user interface ontology model, using knowledge from the context and adaptation rule models, is first adapted to the current context of use, then transformed to a platform independent model, and finally generated to a final source code. In order to illustrate the feasibility and the usefulness of the proposed architecture, we present a case study, where an adaptive user interface is generated.
An Overview of Quantum Computing Approach in the Present-Day Energy Systems

With the increase in global population and global heating, energy demand is also constantly increasing. The uprising demand to be fulfilled by taking care of environmental conditions’ protection to keep global warming in check. Significant efforts have been put into designing, controlling, handling, planning, and managing energy systems. In this regard, bio-inspired or nature-inspired evolutionary optimization schemes in the existing energy systems and innovative energy sources are inducted into the available resources. On the other hand, quantum computing has changed the classical computational approach with speed and efficiency. The assurance of quantum computing in the optimization of energy systems also gained a research attraction. The optimization techniques employed with the quantum advantage by quantum computers supersede classical approaches. This study explores the viability of quantum computing in energy system optimization and various challenges to tackle. This work will help the readers to plan for applying this approach in sustainability energy harvesting, intelligent power and energy systems, distribution network, and renewable energy. Security of the smart grid, intelligent energy systems, evaluation of the energy production process, and other similar or related applications may also be explored.
The Application of Role-Based Framework in Preventing Internal Identity Theft Related Crimes: A Qualitative Case Study of UK Retail Companies

Abstract This paper aims to examine the challenges of preventing internal identity theft related crimes (IIDTRC) in the UK retail sector. Using an in-depth multiple case studies of a selected number of cross-functional management teams in the UK retail companies, management roles were analysed. We used semi-structured interview as a qualitative data collection technique and used Nvivo aided thematic analysis and interpretivism underpinned by Role-Based Framework (RBF) for analysis. Our findings revealed that vagueness of roles and lack of clarity in sharing data security responsibilities are the major challenges of preventing IIDTRC in UK retail companies. We suggest an application of RBF which provides a conceptual analysis for cross-functional management team to address the challenges of preventing IIDTRC. RBF enables clarity of shared roles where both information security and crimes prevention teams work in unison is required to prevent IIDTRC to maximise internal data security. Contributions for policymakers are offered in this paper.
The Rise of Multimodal Tutors in Education

Abstract This chapter describes the insights derived by the design and development of the Multimodal Tutor, a system that uses artificial intelligence for providing digital feedback and to support psychomotor skills acquisition. In this chapter, we discuss the insights which we gained from eight studies: (1) an exploratory study combining physiological data and learning performance (Learning Pulse); (2) a literature survey on multimodal data for learning and a conceptual model (the Multimodal Learning Analytics Model); (3) an analysis of the technical challenges of Multimodal Learning Analytics (the Big Five Challenges); (4) a technological framework for using multimodal data for learning (the Multimodal Pipeline); (5) a data collection and storing system for multimodal data (the Learning Hub); (6) a data annotation tool for multimodal data (the Visual Inspection Tool); (7) a case study in Cardiopulmonary Resuscitation training (CPR Tutor) consisting of a feasibility study for detecting CPR mistakes; and (8) a real-time feedback study.
Data Security in Clinical Trials Using Blockchain Technology

Blockchain technology in a clinical trial setting is a valuable asset due to decentralization, immutability, transparency, and traceability features. For this chapter, a literature review was conducted to map the current utilization of blockchain systems in clinical trials, particularly data security managing systems and their characteristics, such as applicability, interests of use, limitations, and issues. The advantages of data security are producing a more transparent and tamper-proof clinical trial by providing accurate, validated data, therefore producing a more reliable and credible clinical trial. On the other hand, data integrity is a critical issue since data obtained from trials are not instantly made public to all participants. Work needs to be done to establish the significant implications in security data when applying blockchain technology in a real-world clinical trial setting and generalized conditions of use to establish its security.
BLOFF

In this era of explosive growth in technology, the internet of things (IoT) has become the game changer when we consider technologies like smart homes and cities, smart energy, security and surveillance, and healthcare. The numerous benefits provided by IoT have become attractive technologies for users and cybercriminals. Cybercriminals of today have the tools and the technology to deploy millions of sophisticated attacks. These attacks need to be investigated; this is where digital forensics comes into play. However, it is not easy to conduct a forensic investigation in IoT systems because of the heterogeneous nature of the IoT environment. Additionally, forensic investigators mostly rely on evidence from service providers, a situation that can lead to evidence contamination. To solve this problem, the authors proposed a blockchain-based IoT forensic model that prevents the admissibility of tampered logs into evidence.