Reviewerly/exHarmony huggingface dataset:
Interactive Design and Preview of Colored Snapshots of Indoor Scenes

Abstract This paper presents an interactive system for quickly designing and previewing colored snapshots of indoor scenes. Different from high‐quality 3D indoor scene rendering, which often takes several minutes to render a moderately complicated scene under a specific color theme with high‐performance computing devices, our system aims at improving the effectiveness of color theme design of indoor scenes and employs an image colorization approach to efficiently obtain high‐resolution snapshots with editable colors. Given several pre‐rendered, multi‐layer, gray images of the same indoor scene snapshot, our system is designed to colorize and merge them into a single colored snapshot. Our system also assists users in assigning colors to certain objects/components and infers more harmonious colors for the unassigned objects based on pre‐collected priors to guide the colorization. The quickly generated snapshots of indoor scenes provide previews of interior design schemes with different color themes, making it easy to determine the personalized design of indoor scenes. To demonstrate the usability and effectiveness of this system, we present a series of experimental results on indoor scenes of different types, and compare our method with a state‐of‐the‐art method for indoor scene material and color suggestion and offline/online rendering software packages.
Beyond Uniform Equivalence between Answer-set Programs

This article deals with advanced notions of equivalence between nonmonotonic logic programs under the answer-set semantics, a topic of considerable interest, because such notions form the basis for program verification and are useful for program optimisation, debugging, and modular programming. In fact, there is extensive research in answer-set programming (ASP) dealing with different notions of equivalence between programs. Prominent among these notions is uniform equivalence , which checks whether two programs have the same semantics when joined with an arbitrary set of facts. In this article, we study a family of more fine-grained versions of uniform equivalence, viz. relativised uniform equivalence with projection , which extends standard uniform equivalence in terms of two additional parameters: one for specifying the input alphabet and one for specifying the output alphabet for programs. In particular, the second parameter is used for projecting answer sets to a set of designated output atoms. Answer-set projection, in particular, allows to compare programs that make use of different auxiliary atoms, which is important for practical programming aspects. We introduce novel semantic characterisations for the program correspondence problems under consideration and analyse their computational complexity. In the general case, deciding these problems lies on the third level of the polynomial hierarchy. Therefore, this task cannot be efficiently reduced to propositional answer-set programs itself (under the usual complexity-theoretic assumptions). However, reductions to quantified Boolean formulas (QBFs) are feasible. Indeed, we provide efficient (in fact, linear-time constructible) reductions to QBFs and discuss simplifications for certain special cases. These QBF reductions yield the basis for a prototype implementation, the system cc ⊤, for deciding correspondence problems by using off-the-shelf QBF solvers. We discuss an application of cc ⊤ for verifying the correctness of solutions by students drawn from a laboratory course on logic programming and knowledge representation at the Technische Universität Wien, employing relativised uniform equivalence with projection as the underlying program correspondence notion.
Privacy Enhancing Technologies (<scp>PETs</scp>) for connected vehicles in smart cities

Abstract Many Experts believe that the Internet of Things (IoT) is a new revolution in technology that has brought many benefits for our organizations, businesses, and industries. However, information security and privacy protection are important challenges particularly for smart vehicles in smart cities that have attracted the attention of experts in this domain. Privacy Enhancing Technologies (PETs) endeavor to mitigate the risk of privacy invasions, but the literature lacks a thorough review of the approaches and techniques that support individuals' privacy in the connection between smart vehicles and smart cities. This gap has stimulated us to conduct this research with the main goal of reviewing recent privacy‐enhancing technologies, approaches, taxonomy, challenges, and solutions on the application of PETs for smart vehicles in smart cities. The significant aspect of this study originates from the inclusion of data‐oriented and process‐oriented privacy protection. This research also identifies limitations of existing PETs, complementary technologies, and potential research directions.
Estimating the power generating of a stand-alone solar photovoltaic panel using artificial neural networks and statistical methods.

(2021). Estimating the power generating of a stand-alone solar photovoltaic panel using artificial neural networks and statistical methods. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects: Vol. 43, Progress in Photovoltaic Technologies and Applications, pp. 2496-2508.
Object Searching on Video Using ORB Descriptor and Support Vector Machine

One of the main stages in object searching on video is extracting object regions from video. Template matching is popular technique for performing a such task. However, the use of template matching has a limitation that requires a large object as a template. If the template size is too small, it would obtain few features. On the other hand, ORB descriptors are often used for representing the object with a good accuracy and fast processing time. Therefore, this research proposed to use machine learning method combining with ORB descriptor for object searching on video data. Processing video in all frames is inefficient. Thus, frames are selected into keyframes using mutual information entropy. The ORB descriptors are then extracted from selected frame in order to find candidate region of objects. To verify and classify the object regions, multiclass support vector machine was used to train ORB descriptor of regions. For evaluation, the use of ORB would be compared with other descriptor, such as SIFT and SURF for showing its superiority in both accuracy and processing time. In experiment, it is found that object searching with ORB descriptor performs faster processing time, which is 0.219 s, while SIFT 1.011 s and SURF 0.503 s. Meanwhile, it also achieves the best F1 value, which is 0.9 compared to SIFT 0.63 and SURF 0.65.
K‐ear: Extracting data access periodic characteristics for energy‐aware data clustering and storing in cloud storage systems

Abstract Rapid increase in energy consumption is a serious problem in cloud storage systems. Data accessed in large‐scale storage systems usually exhibit temporal and spatial characteristics, which make it possible to reduce energy consumption by clustering data with similar access characteristics for storage in the same zone of cloud storage systems. Existing works usually only focus on the frequency of data access. However, widely existing phenomena show data access with seasonal and tidal characteristics in cloud storage systems. The seasonal and tidal characteristics of data access are extracted thoroughly in this paper. According to the extracted data access characteristics, energy‐aware data clustering through a machine learning algorithm (K‐ear) is proposed. K‐ear classifies data into five seasonal categories according to their seasonal access characteristics and then classifies every seasonal category into three tidal categories according to its tidal access characteristics. The 15 classified categories are stored in different storage zones with different energy and performance modes. Simulation experiments using CloudSimDisk with the constructed mathematic models demonstrate that the proposed K‐ear algorithm is more energy‐efficient than the default data clustering algorithms in Hadoop and the classical data clustering storage strategy according to the data access frequency (Striping‐Based Energy‐Aware Strategy).
Human Action Recognition with Sparse Autoencoder and Histogram of Oriented Gradients

This paper presents a video-based human action recognition method leveraging deep learning model. Prior to the filtering phase, the input images are pre-processed by converting them into grayscale images. Thereafter, the region of interest that contains human performing action are cropped out by a pre-trained pedestrian detector. Next, the region of interest will be resized and passed as the input image to the filtering phase. In this phase, the filter kernels are trained using Sparse Autoencoder on the natural images. After obtaining the filter kernels, convolution operation is performed in the input image and the filter kernels. The filtered images are then passed to the feature extraction phase. The Histogram of Oriented Gradients descriptor is used to encode the local and global texture information of the filtered images. Lastly, in the classification phase, a Modified Hausdorff Distance is applied to classify the test sample to its nearest match based on the histograms. The performance of the deep learning algorithm is evaluated on three benchmark datasets, namely Weizmann Action Dataset, CAD-60 Dataset and Multimedia University (MMU) Human Action Dataset. The experimental results show that the proposed deep learning algorithm outperforms other methods on the Weizmann Dataset, CAD-60 Dataset and MMU Human Action Dataset with recognition rates of 100%, 88.24% and 99.5% respectively.
TUKNN: A Parallel KNN Algorithm to Handle Large Data

In this work, we study the performance of the K-Nearest Neighbour (KNN) based predictive model in sequential as well as parallel mode to observe its performance both in terms of accuracy and execution time. We propose a parallel KNN algorithm, called TUKNN to handle voluminous data. Based on our experimental study, it has been observed that our method is capable of handling datasets with large dimensionality and instances with high accuracy. We also recommend best possible proximity measure and optimal range of K values for better accuracy.
A Model-based dehazing scheme for unmanned aerial vehicle system using radiance boundary constraint and graph model

Unmanned aerial vehicle system (UAVs) imaging has become a challenging area of research due to the dynamic atmospheric environment. The images captured by UAVs are often deteriorated by factors such as clouds occlusion, poor atmospheric illumination, and limited capability of the imaging system. To tackle problems, this paper presents a novel visibility restoration scheme for UAVs images by considering the following two assumptions: (1) The actual scene radiance of a UAVs image is bounded. (2) Pixels sharing the same appearance must have the same transmission value in a local neighborhood. Inspired by above assumptions, an image boundary constraint utilizing the median filter has been imposed on the RGB channel for the rough estimation of transmission-map in aerial images. Furthermore, a graph-model based optimization technique has been used for the transmission-map refinement. The experimental results demonstrate the efficiency of the proposed method in terms of metrics correspond to the human-visual-system (HVS).
Expert System Of Syzygium Aqueum Disease Diagnose Using Bayes Method

Abstract This research examines how to design an application of an expert system to diagnose Syzygium aqueum (burm.f.alston) plant diseases so that it is easier to detect diseases in the Syzygium aqueum (burm.f.alston) plants. This study uses the Bayes method to be more precise because it is based on the probability value of disease symptoms that arise in Syzygium aqueum (burm.f.alston) plants. In this research, the authors used a web-based application with the Bayes method. The results or outputs from this application provide the probability value of disease certainty and then the hypothesis is chosen with the largest value.
An Improvement of ECDSA Weak Randomness in Blockchain

Abstract Blockchain technology has already changed industry and commercial enterprises remarkably. It is the underlying mechanism of a very well-known cryptocurrencies such as Bitcoin and Ethereum, and many other business applications. Therefore, its security draws the researchers’ attention more and more recently. One of Blockchain vulnerabilities is caused by weak randomness in ECDSA. A random number is not secure, cryptographically, which leads to a leakage in private key and even the user’s fund theft. As well the spam transaction attack may exploit the ECDSA weak randomness. This problem in security has been well known in cryptocurrencies community such as Bitcoin and fixed by applying RFC 6979 update in 2013. However, the problem is not entirely solved. The elliptic curve digital signature algorithm (ECDSA) was the first successful algorithm based on elliptic curve. This algorithm security depends on complexity of elliptic curve discrete logarithm problem (ECDLP). This algorithm applied in blockchain mechanism as a result of its low computational cost and short key. In this paper, we analyze the ECDSA weakness in blockchain and enhance its scheme by generating the signature with two secret keys. Using two secret keys will reduce the risk probability of revealing the secret key by knowing two messages. Therefore, the improved scheme can improve the security of the ECDSA.
Impossibility of creating a superposition of unknown quantum states

The superposition principle is fundamental to quantum theory. Yet a recent no-go theorem has proved that quantum theory forbids superposition of unknown quantum states, even with nonzero probability. The implications of this result, however, remain poorly understood so far. In this Rapid Communication we show that the existence of a protocol that superposes two unknown pure states with nonzero probability (allowed to vary over input states) leads to the violation of other no-go theorems. In particular, such a protocol can be used to perform certain state discrimination and cloning tasks that are forbidden not only in quantum theory but in no-signaling theories as well.
Knowledge-graph based Proactive Dialogue Generation with Improved Meta-learning

Knowledge graph-based dialogue systems can narrow down knowledge candidates for generating informative and diverse responses with the use of prior information, e.g., triple attributes or graph paths. However, most current knowledge graphs (KG) cover incomplete domain-specific knowledge. To overcome this drawback, we propose a knowledge graph based proactive dialogue generation model (KgDg) with three components, improved model-agnostic meta-learning algorithm (MAML), knowledge selection in knowledge triplets embedding, and knowledge aware proactive response generator. For knowledge triplets embedding and selection, we formulate it as a problem of sentence embedding to better capture semantic information. Our improved MAML algorithm is capable of learning general features from a limited number of knowledge graphs, which can also quickly adapt to dialogue generation with unseen knowledge triplets. Extensive experiments are conducted on a knowledge aware dialogue dataset (DuConv). The results show that KgDg adapts both fast and well to knowledge graph based dialogue generation and outperforms state-of-the-art baselines.
Fast Eye Detector Using CPU Based Lightweight Convolutional Neural Network

Eye detection is a crucial task for the success of driver drowsiness detection. The eye location is determined by employing feature extractions to discriminate distinctive features. Convolutional Neural Networks (CNN) has achieved the best results in the object detection task. However, this requires expensive computation, whereas practical application demands for this work to run real-time on low-cost devices such as CPU. The eye feature is very different from other organ facial features, so the shallow architecture of CNN deserves to be employed. This paper proposes the Fast Eye-CPU (FE-CPU) as a real-time eye detector that can work on the CPU. The architecture consists of two main modules, including the backbone to rapidly extract features and the detection module for predicting eye regions. As a result, the detector achieves high accuracy on several benchmark datasets, and it can work in real-time by 467 frames per second on Intel I5-6600 as a CPU device.
Extending X3D Realism with Audio Graphs, Acoustic Properties and 3D Spatial Sound

A fundamental requirement for both realistic modeling and immersive presence is spatial audio, correctly rendering the presentation of each object with aural characteristics. Auditory attributes involve perceived directions, distances, and the propagation paths from complex sound sources to each listener in a potentially complex 3D scene. Many long-running efforts in both hardware and software have improved the ability to aurally render high-fidelity spatialized sound in real time. Motivated by current progress, this work proposes integrating acoustic properties associated with geometric shapes together with 3D spatial sound in the X3D Graphics standard. This combination is possible by exploiting the structure and functionality of Web Audio API, an effective framework for processing and synthesizing audio in Web applications. The paper describes design patterns and initial implement work for this spatial composition of audio graphs and scene graphs. Both specifications are device neutral, without dependencies on specific platforms or audio hardware. Examples for evaluation lead to useful conclusions and areas for future model development.
UI-UX Design and Analysis of Local Medicine and Medication Mobile-based Apps using Task-Centered Design Process

Indigenous knowledge has been utilized by indigenous people for so long. Unfortunately, the process of conveying it to people outside of its circle is barred by its tacit form. To transform it into an explicit manifestation, a knowledge management system is needed. Elicitation of the knowledge is needed to build a proper knowledge representation. A crowdsourcing and crawling-based knowledge elicitation app is proposed to do so. In app building, user interface and user experience are important parts. As it is crucial to satisfy users' needs and what they want to accomplish with the apps, task-centered design process was chosen. The steps in the process consists of conducting tasks and users analysis, choosing representative tasks for task centered design, plagiarizing, roughing out the design, thinking about the design, building a mock-up or prototype, testing the prototype with users, iterating the design, and evaluating the prototype from all iteration. The prototype's evaluation is done by comparing SUS questionnaire's performance score of first and second iteration. The evaluation of the prototype's performance concludes that the second iteration has successfully deliver well-presented UI and giving good UX to users. The research successfully observed the UI and UX design of a knowledge elicitation apps that is viable.
Software Effort Estimation from Use Case Diagrams Using Nonlinear Regression Analysis

Software effort estimation in the early stages of the software life cycle is one of the most essential and daunting tasks for project managers. In this research, a new model based on nonlinear regression analysis is proposed to predict software effort from use case diagrams. It is concluded that, where software size is classified from small to very large, one linear or non-linear equation for effort estimation cannot be applied. Our model with three different non-linear regression equations can incorporate the different ranges in software size.
MemEnc: A Lightweight, Low-Power, and Transparent Memory Encryption Engine for IoT

Recent advancement in technologies has led to the widespread adoption and deployment of Internet-of-Things devices. Because of the ubiquitous nature of these devices, they process large amounts of personal and sensitive data. These data are typically stored on DRAM chips, and hence becomes an easy target for attackers. Memory encryption is a commonly adopted solution to provide confidentiality. However, realizing a lightweight, low-latency, low-power solution for resource-constrained devices is a challenge. To address this, we designed MemEnc, a purely hardware-based solution that performs encryption on-the-fly and handles memory requests transparently without any OS intervention. MemEnc runs at a maximum frequency of 401 MHz and requires about 23.2 kGE (gate equivalents) on 65-nm ASIC and consumes only 1.9 mW of power at 250 MHz. Using comprehensive benchmarking, we also analyze the applicability of the proposed solution on real-world workloads. Our experiments show that certain real-time applications can run with full memory encryption and still meet system requirements. Moreover, we integrated our memory encryption engine with ARM TrustZone and present comparative results for the different case studies with Intel SGX. We show that static and dynamic efficiency-security tradeoff is necessary for all scenarios.
Photorealism and Kinematics for Web-based CAD data

As cloud technology gains traction as a platform in the architecture, engineering, and construction (AEC) sector, so does the adoption of Web3D technologies for the visualisation of massive 3D models. However, the interaction with highly complex CAD models typical of these sectors is still critical. Various efforts are found in the literature to create suitable transmission formats that do not require users to wait long periods for massive scenes to load and to define standards for enhancing such data with interaction. However, most of the existing frameworks are either domain-specific or too general, which results in increased data preparation times and additional needs for processing at the application level. This paper describes a novel system for CAD (Computer-Aided Design) data interaction built on Web3D technologies. First, we discuss the approach to prepare CAD models for visualisation: importation of data, the definition of mechanical behaviours, and physically-based rendering (PBR) properties. Next, we describe how to export CAD models as an X3D scene with federated glTF nodes to increase performance and overall client interactivity. We followup with a description of how the Denavit-Hartenberg (DH) parameters can enable the visualisation of mechanical motion characteristics directly from the design and enhance user interaction. Finally, we summarise lessons learned from this industry-based software engineering experience. We also identify several future research directions in this area.
Multi-UAV 3D Path Planning in Simultaneous Attack

As the key technology of UAV mission planning, path planning has a very important impact on the combat effectiveness of UAV. In order to destroy enemy units, multiple UAVs are often needed to attack the target at the same time. In the complex combat environment, the path planning of multiple UAVs is a very complex problem. On the basis of genetic algorithm, this paper designs a path planning algorithm to ensure that multiple UAVs arrive at the same time from different angles on the premise of satisfying environment constraints and the motion constraints of UAV. The algorithm uses the method of regionalization to initialize the path, designs a reasonable fitness function, and adds an adaptive disturbance operator to plan the path of each UAV. Finally, the length difference between the paths planned for each UAV is taken as the path evaluation factor. Some of the paths are replanned to achieve the goal of the same path length and simultaneous attack by UAV. The effectiveness of the algorithm is verified by experiments in typical combat scenarios, and the superiority of the adaptive disturbance operator in the optimization of the algorithm is verified by the contrast experiment.