Reviewerly/exHarmony huggingface dataset:
The More, the Better? Improving VR Firefighting Training System with Realistic Firefighter Tools as Controllers

A virtual reality (VR) controller plays a key role in supporting interactions between users and the virtual environment. This paper investigates the relationship between the user experience and VR control device modality. We developed a VR firefighting training system integrated with four control devices adapted from real firefighting tools. We iteratively improved the controllers and VR system through a pilot study with six participants and conducted a user study with 30 participants to assess two salient human factor constructs-perceived presence and cognitive load-with three device modality conditions (two standard VR controllers, four real tools, and a hybrid of one real tool and one standard VR controller). We found that having more realistic devices that simulate real tools does not necessarily guarantee a higher level of user experience, highlighting a strategic approach to the development and utilization of VR control devices. Our study gives empirical insights on establishing appropriate combinations of VR control device modality in the context of field-based VR simulation and training.
Disentangling Deep Network for Reconstructing 3D Object Shapes from Single 2D Images

Recovering 3D shapes of deformable objects from single 2D images is an extremely challenging and ill-posed problem. Most existing approaches are based on structure-from-motion or graph inference, where a 3D shape is solved by fitting 2D keypoints/mask instead of directly using the vital cue in the original 2D image. These methods usually require multiple views of an object instance and rely on accurate labeling, detection, and matching of 2D keypoints/mask across multiple images. To overcome these limitations, we make effort to reconstruct 3D deformable object shapes directly from the given unconstrained 2D images. In training, instead of using multiple images per object instance, our approach relaxes the constraint to use images from the same object category (with one 2D image per object instance). The key is to disentangle the category-specific representation of the 3D shape identity and the instance-specific representation of the 3D shape displacement from the 2D training images. In testing, the 3D shape of an object can be reconstructed from the given image by deforming the 3D shape identity according to the 3D shape displacement. To achieve this goal, we propose a novel convolutional encoder-decoder network—the Disentangling Deep Network (DisDN). To demonstrate the effectiveness of the proposed approach, we implement comprehensive experiments on the challenging PASCAL VOC benchmark and use different 3D shape ground-truth in training and testing to avoiding overfitting. The obtained experimental results show that DisDN outperforms other state-of-the-art and baseline methods.
Multi-directional PVD steganography avoiding PDH and boundary issue

Pixel value differencing steganography (PVDS) technique efficiently identifies the edge and smooth regions from an image, therefore, the PVDS technique is more suitable for concealing the secret information in an image. Notwithstanding the advantages, the PVDS techniques give rise to some major issues. Most of the PVDS techniques suffer from boundary issue (BI). Further, the majority of the PVDS techniques are exposed to pixel difference histogram (PDH) analysis. In this paper, two improved PVDS based techniques, such as (1) indicator-based PVDS (IPVDS) and (2) multi-directional overlapped PVDS (MDOPVDS) has been proposed to address the BI as well as PDH issue. The IPVDS technique utilizes the concept of indicator pixel in a 2 × 3 pixel block. On the other hand, the MDOPVDS technique utilizes the pixel overlap strategy in a 3 × 3 pixel block using all three directions, such as (1) horizontal, (2) vertical, and (3) diagonal. Results of the proposed technique are evaluated in terms of the conflicting metrics like peak signal-to-noise ratio (PSNR), capacity, and security. Further, it is observed that the proposed technique maintains a good symmetry between the aforementioned conflicting metrics. Additionally, both the proposed technique successfully avoids the BI.
An Effective Location Privacy-Preserving K-anonymity Scheme in Location Based Services

With the development of global positioning systems, communication technologies, the Internet and mobile terminals, location-based services (LBS) have become increasingly widely used. LBS brings great convenience to people's daily life and social activities. At the same time, people pay more attention to the problem of sensitive information leakage when using LBS. Users need to provide their accurate location information when sending queries to the LBS server. User location privacy is extremely vulnerable to serious threats during the exchange of services, and these location-related queries may cause serious privacy issues. In this paper, considering that the opponent may use auxiliary information such as data analysis and crawlers to determine the approximate location, we propose an effective location privacy-preserving k-anonymity scheme (PPKS) to generate virtual locations based on the probability density function in mathematical statistics to realize the k-anonymity of users with privacy zone awareness in LBS. We first determine the origin of the region and then calculate the probability density function of the X-axis and the Y-axis. Subsequently, the k-1 positions are distributed to the X-axis and Y-axis in proportion, respectively. Finally, according to the proportions, the k-1 virtual positions are generated. The results of security analysis and experimental evaluation show that the solution can significantly improve the level of privacy and anonymity.
How Useful Is a Distributed Ledger for Tracking and Tracing in Supply Chains? A Systems Thinking Approach

Background: The use of blockchain technology for tracking and tracing (T&amp;T) in supply chains is the subject of lively debate in scientific literature. However, distributed ledger technology (DLT) does not have to have the characteristic blockchain structure and often performs better without such a structure. Generalized DLT for T&amp;T in supply chains has rarely been discussed in the existing literature. Methods: This article presents an exploratory case study research of eight companies to identify the main goals, and problems that the companies have when they engage in T&amp;T. This practical perspective is complemented by a theoretical systems thinking perspective. Based on these two foundations, we discuss the usefulness of blockchain technology and, more generally, DLT for T&amp;T in supply chains. Results: Based on our analysis, DLT is only necessary in special cases, e.g., when the owners of the data have an interest in deleting the data, but the data stakeholders do not. In the other cases examined, DLT competes with other technologies, such as conventional, centralized databases in combination with digital signatures. Furthermore, it became evident that DLT can only be useful for supply chain tracing. The technological features of DLT do not provide any benefit for supply chain tracking, i.e., the timely communication of the status of a physical good. Conclusions: Distributed ledgers often have a disadvantage in that they are very complex and, therefore, expensive. DLT should preferably only be used when it is technologically necessary or the simplest/cheapest choice, which is probably not all that often. Finally, the usefulness of distributed ledger technology and its integrated smart contract technology is highly dependent on how easy it is to link the real physical world to a digital record/contract in an error-free and tamper-proof way. Currently, such a definite link exists only in very few cases and is often impossible.
Detection and Geovisualization of Abnormal Vessel Behavior from Video

Intelligent maritime situational awareness pursues an effective understanding of the majority of the activities related to the maritime domain (impacting the safety, security, economy, or environment), with the aid of artificial intelligence systems. Such an understanding requires the development of automated processes capable of not only detecting abnormal behavior but also of visually-representing and interpreting it. Although much progress has been made in anomaly detection and visualization using vessel self-reporting positioning data, there have been no corresponding advances using video data, despite the increasing use of cameras for maritime surveillance. In this work, we introduce a framework which goes beyond vessel tracking for anomaly detection in video, and is therefore applicable to scenes with a high density of vessels. The proposed framework detects abnormal behavior using a Generative Adversarial Network (GAN) and interprets this knowledge using metrics derived from clustering the positions and courses provided by an independent vessel/motion detector. These detections are geovisualized using an advanced displaying tool where detected abnormal behavior may be localized on the globe, providing an infrastructure for intelligent maritime situational awareness.
Geometric Control for Load Transportation With Quadrotor UAVs by Elastic Cables

Groups of unmanned aerial vehicles (UAVs) are increasingly utilized in transportation task as the combined strength allows to increase the maximum payload. However, the resulting mechanical coupling of the UAVs imposes new challenges in terms of tracking control. Thus, we design a geometric trajectory tracking controller for the cooperative task of four quadrotor UAVs carrying and transporting a rigid body, which is attached to the quadrotors via inflexible elastic cables. The elasticity of the cables together with techniques of singular perturbation allows a reduction in the model to that of a similar model with inelastic cables. In this reduced model, we design a controller such that the position and attitude of the load exponentially converge to a given desired trajectory. We then show that this result leads to a uniformly converging tracking error for the original elastic model under some assumptions. Furthermore, under the presence of unstructured disturbances on the system, we show that the error is ultimately bounded with an arbitrarily small bound. Finally, a simulation illustrates the theoretical results.
Requirement Engineering in the Age of System and Product Complexity – A Literature Review

Product, service, and system development can be approached with numerous processes and methodologies. Many of these models begin with the definition of requirements to fulfill. Thus, these requirements outline the purpose of the development and design. Due to the inherent attribute of the requirement definition and the product development process, the aspects to design are usually defined in the beginning, and subsequently implemented or realized. This time difference between the definition and the actual realization of the requirements creates a high potential for uncertainty and risk, which is especially critical when it comes to emergent behaviors and complexity of the product or system. Based on the connection between requirements and complexity, the presented research set out to define and assess the current state of the research in the form of a comprehensive literature review, to serve as a basis for further research. This review includes the topics of general complexity, requirement engineering, and system/product complexity. The literature showed ongoing and active research for both fields with a longer history for complexity. Since it was first mentioned in 1948, complexity has expanded its application and research to various engineering domains which were identified based on cross connections. Requirement engineering showed its origins in computer science/engineering, and successive expansion into other domains, such as mechanical engineering. Both fields, the one for complexity and requirement engineering, also show recent trends, such as the application and inclusion of AI and Machine Learning, Agile, and certain security/resilience foci. All in all, a comprehensive overview for the topics is provided with insights into expansion and evolution.
Multi-instance Publicly Verifiable Time-Lock Puzzle and Its Applications

Time-lock puzzles are elegant protocols that enable a party to lock a message such that no one else can unlock it until a certain time elapses. Nevertheless, existing schemes are not suitable for the case where a server is given multiple instances of a puzzle scheme at once and it must unlock them at different points in time. If the schemes are naively used in this setting, then the server has to start solving all puzzles as soon as it receives them, that ultimately imposes significant computation cost and demands a high level of parallelisation. We put forth and formally define a primitive called “multi-instance time-lock puzzle” which allows composing a puzzle’s instances. We propose a candidate construction: “chained time-lock puzzle” (C-TLP). It allows the server, given instances’ composition, to solve puzzles sequentially, without having to run parallel computations on them. C-TLP makes black-box use of a standard time-lock puzzle scheme and is accompanied by a lightweight publicly verifiable algorithm. It is the first time-lock puzzle that offers a combination of the above features. We use C-TLP to build the first “outsourced proofs of retrievability” that can support real-time detection and fair payment while having lower overhead than the state of the art. As another application of C-TLP, we illustrate in certain cases, one can substitute a “verifiable delay function” with C-TLP, to gain much better efficiency.
Scrambling Based Riffle Shift on Stego-Image to Channelize the Ensured Data

In recent years information hiding has got much attention as it acts as an alternate option for secured communication. The Secret content would get imbedded with the image using various possible image embodiment techniques, in which the Least Significant Bit (LSB) substitution is one of the preferred content embodiment strategy; however, asserting the quality and the originality of the content embedded image (stego) is yet a grievous concern in the field of Information Security. In this article, a proficient Scrambling Based Haar Wavelet Transform (SBHWT) approach has been sought to ensure the novelty of the stego-image that supports the safe transfer of data over the channel. In this process, the image is scrambled using a Sudoku pattern, and then streams of the secret content are embedded into the image blocks based on the proposals of the Bishop Traversal logic of chess. The content embodiment is attempted using normal LSB substitution and DNA pattern based substitution. Next, the image is descrambled to recover the original image, which is then subjected to Haar wavelet transformation for image resolution reduction. The resultant reduced image is channelized to the recipient and the inverse action of the SBHWT is made in order to get back the secret code stream. The experimental results demonstrate that the projected SBHWT approach is less predictable and exclusive in its pattern.
K-AID

Knowledge enhanced pre-trained language models (K-PLMs) are shown to be effective for many public tasks in the literature, but few of them have been successfully applied in practice. To address this problem, we propose K-AID, a systematic approach that includes a low-cost knowledge acquisition process for acquiring domain knowledge, an effective knowledge infusion module for improving model performance, and a knowledge distillation component for reducing the model size and deploying K-PLMs on resource-restricted devices (e.g., CPU) for real-world application. Importantly, instead of capturing entity knowledge like the majority of existing K-PLMs, our approach captures relational knowledge, which contributes to better improving sentence-level text classification and text matching tasks that play a key role in question answering (QA). We conducted a set of experiments on five text classification tasks and three text matching tasks from three domains, namely E-commerce, Government, and Film&TV, and performed online A/B tests in E-commerce. Experimental results show that our approach is able to achieve substantial improvement on sentence-level question answering tasks and bring beneficial business value in industrial settings.
The use of machine learning to identify the correctness of HS Code for the customs import declarations

As an increasing volume of international trade activities around the world, the amount of cross-boarder import declarations grows rapidly, resulting in an unprecedented scale of potentially fraudulent transactions, in particular false commodity code (e.g., HS Code). The incorrect HS Code will cause duty risk and adversely impact the revenue collection. Physical investigation by the customs administrations is impractical due to the substantial quantity of declarations. This paper provides an automatic approach by harnessing the power of machine learning techniques to relief the burden of customs targeting officers. We introduced a novel model based on the off-the-shelf embedding encoder to identify the correctness of HS Code without any human effort. Determining whether the HS Code is correctly matched with commodity description is a classification task, so the labelled data is typically required. However, the lack of gold standard labelled data sets in customs domain limits the development of supervised-based approach. Our model is developed by the unsupervised mechanism and trained on the unlabelled historical declaration records, which is robust and able to be smoothly adapted by the different customs administrations. Rather than typically classifying whether the HS Code is correct or not, our model predicts the score to indicate the degree of the HS Code being correct. We have evaluated our proposed model on the ground-truth data set provided by Dutch customs officers. Results show promising performance of 71% overall accuracy.
An Extended Meta-Model of Problem Frames for Enriching Environmental Descriptions

Problem Frames (PF) have attracted extensive attention and research in the requirements engineering community, particularly in the field of modeling environment-based CyberPhysical Systems (CPS). This paper proposes an approach based on the combination of PF and model-driven engineering (MDE). The approach builds an extended meta-model of the Problem Frames approach to support representing causal behaviours of the environment of CPS, which are essential domain knowledge of the environment for modelling CPS requirements and complexity analysis. EMF (Eclipse Model Framework) and the Sirius technology are used to create GUI items for the meta-model and provide a platform for modeling tools based on the Eclipse environment for PF modelers. An example case study is used to demonstrate the feasibility of our approach.
Causal Pathway Extraction from Web-Board Documents

This research aim is to extract causal pathways, particularly disease causal pathways, through cause-effect relation (CErel) extraction from web-board documents. The causal pathways benefit people with a comprehensible representation approach to disease complication. A causative/effect-concept expression is based on a verb phrase of an elementary discourse unit (EDU) or a simple sentence. The research has three main problems; how to determine CErel on an EDU-concept pair containing both causative and effect concepts in one EDU, how to extract causal pathways from EDU-concept pairs having CErel and how to indicate and represent implicit effect/causative-concept EDUs as implicit mediators with comprehension on extracted causal pathways. Therefore, we apply EDU’s word co-occurrence concept (wrdCoc) as an EDU-concept and the self-Cartesian product of a wrdCoc set from the documents for extracting wrdCoc pairs having CErel into a wrdCoc-pair set from the documents after learning CErel on wrdCoc pairs by supervised-machine learning. The wrdCoc-pair set is used for extracting the causal pathways by wrdCoc-pair matching through the documents. We then propose transitive closure and a dynamic template to indicate and represent the implicit mediators with the explicit ones. In contrast to previous works, the proposed approach enables causal-pathway extraction with high accuracy from the documents.
Pruning Meta-Trained Networks for On-Device Adaptation

Adapting neural networks to unseen tasks with few training samples on resource-constrained devices benefits various Internet-of-Things applications. Such neural networks should learn the new tasks in few shots and be compact in size. Meta-learning enables few-shot learning, yet the meta-trained networks can be over-parameterised. However, naive combination of standard compression techniques like network pruning with meta-learning jeopardises the ability for fast adaptation. In this work, we propose adaptation-aware network pruning (ANP), a novel pruning scheme that works with existing meta-learning methods for a compact network capable of fast adaptation. ANP uses weight importance metric that is based on the sensitivity of the meta-objective rather than the conventional loss function, and adopts approximation of derivatives and layer-wise pruning techniques to reduce the overhead of computing the new importance metric. Evaluations on few-shot classification benchmarks show that ANP can prune meta-trained convolutional and residual networks by 85% without affecting their fast adaptation.
Implementation of Washing Machine System Via Utilization of Fuzzy Logic Algorithms

In this paper, we designed and implemented a washing machine based on fuzzy logic rules. The washing machine fuzzy logic system is based on detecting four input parameters The amount of dust (dirt), the kind of dust, the sensitivity of the material, and the number of garments are all factors to consider. The washing speed, washing time, hot water temperature, and amount of detergent are applied to the output based on the parameters entered. Applying a fuzzy logic rule makes it easier to get a cleaner cloth compared to the traditional method of manually adjusting the output parameters. In addition, applying fuzzy logic saves the amount of water, detergent and electricity, and finally saves time, which is a big factor, reducing process costs. This project aims to design and implement a washing marching system. As well as to study the effect of applying fuzzy logic rules in enhancing washing machine control system. And to apply the proposed project using the Matlab software as well. This investigation was simulated using the MATLAB/Fuzzy logic toolbox. Finally, practically mechanical and electronic engineering concepts should be used to implement this system.