Reviewerly/exHarmony huggingface dataset:
A Systematic Review of Visualization in Building Information Modeling

Building Information Modeling (BIM) employs data-rich 3D CAD models for large-scale facility design, construction, and operation. These complex datasets contain a large amount and variety of information, ranging from design specifications to real-time sensor data. They are used by architects and engineers for various analysis and simulations throughout a facility's life cycle. Many techniques from different visualization fields could be used to analyze these data. However, the BIM domain still remains largely unexplored by the visualization community. The goal of this article is to encourage visualization researchers to increase their involvement with BIM. To this end, we present the results of a systematic review of visualization in current BIM practice. We use a novel taxonomy to identify main application areas and analyze commonly employed techniques. From this domain characterization, we highlight future research opportunities brought forth by the unique features of BIM. For instance, exploring the synergies between scientific and information visualization to integrate spatial and non-spatial data. We hope this article raises awareness to interesting new challenges the BIM domain brings to the visualization community.
A combination of color-black-and-white visual cryptography and polynomial based secret image sharing

A new (k,n) threshold secret image sharing scheme with two decoding options is introduced in this paper. The proposed scheme combines color-black-and-white visual cryptography scheme (CBW-VCS) and polynomial based secret image sharing (PSIS) together, to offer stacking-to-see decryption and lossless image reconstruction. To construct the color shares, a general (k,n) threshold CBW-VCS is firstly given. A grayscale secret image is converted to a p-radix image and a binary image. The p-radix image is encrypted by the (k,n) PSIS under mod p operation, and n p-radix shadows are obtained. Then, a color share generation algorithm with data embedding is proposed to construct n color shares. Theoretical analysis and sufficient experiments are provided to show the effectiveness and advantages of the proposed scheme.
A buffer-based online clustering for evolving data stream

Data stream clustering plays an important role in data stream mining for knowledge extraction. Numerous researchers have recently studied density-based clustering algorithms due to their capability to generate arbitrarily shaped clusters. However, most of the algorithms are either fully offline, hybrid online/offline, or cannot handle the property of evolving data stream. Recently, a fully online clustering algorithm for evolving data stream called CEDAS was proposed. However, similar to other density-based clustering algorithms, CEDAS requires predefining the global optimal radius of micro-clusters, which is a difficult task; in addition, an erroneous choice deteriorates cluster performance. Moreover, the algorithm ignores the presence of temporarily irrelevant micro-clusters, which may be relevant in the future. In this study, we present a fully online density-based clustering algorithm called buffer-based online clustering for evolving data stream (BOCEDS). This algorithm recursively updates the micro-cluster radius to its local optimal. It also introduces a buffer for storing irrelevant micro-clusters and a fully online pruning method for extracting the temporarily irrelevant micro-cluster from the buffer. In addition, BOCEDS proposes an online micro-cluster energy-updating function based on the spatial information of the data stream. Experimental results are compared with those of CEDAS and other alternative hybrid online/offline density-based clustering algorithms, and BOCEDS proves its superiority over the other clustering algorithms. The sensitivity of clustering parameters is also measured. The proposed algorithm is then applied to real-world weather data streams to demonstrate its capability to detect changes in data stream and discover arbitrarily shaped clusters. The proposed BOCEDS can be available in https://sites.google.com/view/md-manjur-ahmed and https://sites.google.com/view/kamrul-just.
Detecting Small Objects Using a Channel-Aware Deconvolutional Network

Detecting small objects is a challenging task due to their low resolution and noisy representation even using deep learning methods. In this paper, we propose a novel object detection method based on the channel-aware deconvolutional network (CADNet) for accurate small object detection. Specifically, we develop the channel-aware deconvolution (ChaDeConv) layer to exploit the correlations of feature maps in different channels across deeper layers, improving the recall rate of small objects at low additional computational costs. Following the ChaDeConv layer, the multiple region proposal sub-network (Multi-RPN) is employed to supervise and optimize multiple detection layers simultaneously to achieve better accuracy. The Multi-RPN module is only used in the training phase and does not increase the computation cost of the inference. In addition, we design a new anchor matching strategy based on the center point translation (CPTMatching) of anchors to select more extending anchors as positive samples in the training phase. The extensive experiments on the PASCAL VOC 2007/2012, MS COCO, and UAVDT datasets show that the proposed CADNet achieves state-of-the-art performance compared to the existing methods.
State Transition Simulated Annealing Algorithm for Discrete-Continuous Optimization Problems

A simulated annealing (SA) algorithm is an effective method for solving optimization problems, especially for combinatorial optimization problems. However, SA algorithms rely heavily on the iterative mechanism of the neighborhood structure. When the scale of the problem becomes larger, the convergence rate is slow and it is difficult to reach the global optimal solution. In this paper, seven special intelligent operators are introduced to a SA algorithm, and therefore, a new state transition SA (STASA) algorithm is proposed for combinatorial optimization and continuous optimization problems. The performance of the STASA algorithm is tested on the traveling salesman problem and some continuous function optimization problems. The experimental results show that the proposed algorithm is efficient and reliable.
Energy-efficient Virtual Machine Allocation Technique Using Flower Pollination Algorithm in Cloud Datacenter: A Panacea to Green Computing

Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters. Meanwhile, datacenter infrastructure comprises hardware resources that consume high amount of energy and give out carbon emissions at hazardous levels. In cloud datacenter, Virtual Machines (VMs) need to be allocated on various Physical Machines (PMs) in order to minimize resource wastage and increase energy efficiency. Resource allocation problem is NP-hard. Hence finding an exact solution is complicated especially for large-scale datacenters. In this context, this paper proposes an Energy-oriented Flower Pollination Algorithm (E-FPA) for VM allocation in cloud datacenter environments. A system framework for the scheme was developed to enable energy-oriented allocation of various VMs on a PM. The allocation uses a strategy called Dynamic Switching Probability (DSP). The framework finds a near optimal solution quickly and balances the exploration of the global search and exploitation of the local search. It considers a processor, storage, and memory constraints of a PM while prioritizing energy-oriented allocation for a set of VMs. Simulations performed on MultiRecCloudSim utilizing planet workload show that the E-FPA outperforms the Genetic Algorithm for Power-Aware (GAPA) by 21.8%, Order of Exchange Migration (OEM) ant colony system by 21.5%, and First Fit Decreasing (FFD) by 24.9%. Therefore, E-FPA significantly improves datacenter performance and thus, enhances environmental sustainability.
Blockchain-Based Distributed Cloud Storage Digital Forensics: Where's the Beef?

The current state of the art in digital forensics has primarily focused on the acquisition of data from cloud storage. Here, we present a new challenge in digital forensics: blockchain-based distributed cloud storage, using STORJ as a technology example.
Multi-Scale Image Block-Level F-CNN for Remote Sensing Images Object Detection

Nowadays, with the development of remote sensing technology, very-high-resolution (VHR) remote sensing image object detection technology attracts more and more attention. However, various challenges still exist in remote sensing image object detection field, such as the complex and varied appearances, the expensive manual annotation, and difficult in fast detecting the large scene image. In this paper, we propose a multi-scale image block-level fully convolutional neural network (MIF-CNN) for remote sensing image object detection, which can solve the above problems to a certain degree. First, the training data sets which only require class labels and do not need the bounding box label can reduce the spend of manual annotation during training. Second, the MIF-CNN is designed to extract the multi-scale-based high-level feature which can better represent the various types of objects. The image block-level fully convolutional network contributes to improving computing efficiency and can directly detect any size of the input image, including a large scene remote sensing image. In the testing phase, the large scene image is directly input to MIF-CNN model, and the detection results are generated from the MIF-CNN output maps which are improved by the proposed bounding boxes modification strategy and local re-recognized strategy. The experiments on NWPU VHR-10 [1] and two Airports data sets demonstrate the effectiveness of the proposed method.
BOGO

A memory safety violation occurs when a program has an out-of-bound (spatial safety) or use-after-free (temporal safety) memory access. Given its importance as a security vulnerability, recent Intel processors support hardware-accelerated bound checks, called Memory Protection Extensions (MPX). Unfortunately, MPX provides no temporal safety. This paper presents BOGO, a lightweight full memory safety enforcement scheme that transparently guarantees temporal safety on top of MPX's spatial safety. Instead of tracking separate metadata for temporal safety, BOGO reuses the bounds metadata maintained by MPX for both spatial and temporal safety. On free, BOGO scans the MPX bound tables to invalidate the bound of dangling pointers; any following use-after-free error can be detected by MPX as an out-of-bound error. Since scanning the entire MPX bound tables could be expensive, BOGO tracks a small set of hot MPX bound table pages to check on free, and relies on the page fault mechanism to detect any potentially missing dangling pointer, ensuring sound temporal safety protection. Our evaluation shows that BOGO provides full memory safety at 60% runtime overhead and at 36% memory overhead for SPEC CPU 2006 benchmarks. We also show that BOGO incurs reasonable 2.7x slowdown for the worst-case malloc-free intensive benchmarks; and moderate 1.34x overhead for real-world applications.
GRNN

Recurrent neural networks (RNNs) have gained significant attention due to their effectiveness in modeling sequential data, such as text and voice signal. However, due to the complex data dependencies and limited parallelism, current inference libraries for RNNs on GPUs produce either high latency or poor scalability, leading to inefficient resource utilization. Consequently, companies like Microsoft and Facebook use CPUs to serve RNN models.
Financial innovation and economic growth: Empirical evidence from China, India and Pakistan

Abstract This study investigates the causal relationship between financial innovation and economic growth in China, India, and Pakistan over the period of 1970–2016. Using an Autoregressive Distributed Lag (ARDL) bound testing and Granger causality‐based Error Correction Model (ECM), this study finds that financial innovation generally has a positive and statistically significant impact on economic growth in the short‐run and long‐run. These results show that in the long‐run, monetary management and credit flow to the private sector play an essential role in economic growth. The trade openness and gross capital formation contribute considerably to the economic growth in China, India, and Pakistan. For robustness, this study also applies the Dynamic Ordinary Least Square (DOLS) and Fully Modified Ordinary Least Square (FMOLS) method. The findings of this study suggest that the financial sector plays an essential role in supporting innovation activity in Asian countries.
A longitudinal scanline based vehicle trajectory reconstruction method for high-angle traffic video

In this paper, a robust and efficient High-angle Spatial-Temporal Diagram Analysis (HASDA) model is built to reconstruct high-resolution vehicle trajectories from infrastructure traffic surveillance videos. A combined methodology was developed, comprising of scanline-based trajectory extraction and feature-matching coordinate transformation. A scanline-based trajectory extraction technique is introduced to separate vehicle strands from pavement background on the spatial-temporal diagram by considering color features, gradient features, and motion features. Particular cleaning algorithms for removing static object noises, shadows, and occlusions are also established. Feature-matching coordinate transformation converts the pixel coordinates to the real-world coordinates to generate the physical vehicle trajectory. To evaluate the algorithm, generated trajectory results were compared to the reconstructed version of the Next Generation Simulation (NGSIM) dataset. 15-min NGSIM video was divided into a 5-min dataset for the calibration and the remaining 10-min data for evaluation. Model parameters calibrated based on the 5-min video data are then applied to the 10-min testing data. Two levels of performance measurements are considered to evaluate both trajectory-level and point-level results. A reference algorithm based on mainstream motion-based detection and tracking methods are used as a baseline algorithm. Based on the evaluation results, the proposed method shows promising trajectory detection results, that on average more than 90% of vehicle trajectories are constructed by the proposed methods from the NGSIM videos. The HASDA model outperforms the reference algorithm and shows superior transferability in the training-testing experiment. Further work needs to be done to improve the algorithm performance against shadows and occlusions by incorporating more intelligent and advanced techniques.
Next-Generation Digital Forensics: Challenges and Future Paradigms

In recent years, Information and Communications Technology (ICT) has rapidly advanced, bringing numerous benefits to the lives of many individuals and organisations. Technologies such as Internet of Things (loT) solutions, Cloud-Based Services (CBSs), Cyber-Physical Systems (CPSs) and mobile devices have brought many benefits to technologically-advanced societies. As a result, commercial transactions and governmental services have rapidly grown, revolutionising the life styles of many individuals living in these societies. While technological advancements undoubtedly present many advantages, at the same time they pose new security threats. As a result, the number of cases that necessitate Digital Forensic Investigations (DFIs) are on the rise, culminating in the creation of a backlog of cases for law enforcement agencies (LEAs) worldwide. Therefore, it is of paramount importance that new research approaches be adopted to deal with these security threats. To this end, this paper evaluates the existing set of circumstances surrounding the field of Digital Forensics (DF). Our research study makes two important contributions to the field of DF. First, it analyses the most difficult technical challenges that need to be considered by both LEAs and Digital Forensic Experts (DFEs). Second, it proposes important specific future research directions, the undertaking of which can assist both LEAs and DFEs in adopting a new approach to combating cyber-attacks.
Robust medical image compression based on wavelet transform and vector quantization

Medical imaging, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound, serves as a precursor for determining a patient's expedition towards therapeutics or surgery. The rising pervasiveness of chronic illnesses worldwide has resulted in a great increase in the number of diagnostic imaging techniques being executed annually. This, in turn, has given rise to more advanced imaging technologies and software to expedite accurate diagnosis. For the purposes of patient medical history, these images are stored for very long periods. Also, future research and medical developments renders such records very sensitive, emphasizing their need to be stored. Storing, however, poses a great challenge since there is limited storage capacity to preserve these ever growing medical images. Any technology that improves medical image compression is welcome, since indirectly, it also promotes applications such as telemedicine that require fewer bits to transmit imagery over a computer network. In this study is proposed a DWT-VQ (Discrete Wavelet Transform – Vector Quantization) technique to compress images, and to preserve their perceptual quality at a medically tolerant level. In this hybrid technique, speckle and salt and pepper noises in ultrasound imagery are significantly reduced. If the image is not ultrasound, the process has a negligible effect, but the edge is preserved. The images are then filtered using DWT. A threshold approach is applied to generate coefficients by efficient means. The result is then vector quantized. Finally, the quantized coefficients are Huffman encoded. The resulting bits that represent the compressed image are then stored, and retrieved when needed. The result of the proposed technique is promising, as it outperforms other recent techniques.
A Hierarchical Approach for Joint Parameter and State Estimation of a Bilinear System with Autoregressive Noise

This paper is concerned with the joint state and parameter estimation methods for a bilinear system in the state space form, which is disturbed by additive noise. In order to overcome the difficulty that the model contains the product term of the system input and states, we make use of the hierarchical identification principle to present new methods for estimating the system parameters and states interactively. The unknown states are first estimated via a bilinear state estimator on the basis of the Kalman filtering algorithm. Then, a state estimator-based recursive generalized least squares (RGLS) algorithm is formulated according to the least squares principle. To improve the parameter estimation accuracy, we introduce the data filtering technique to derive a data filtering-based two-stage RGLS algorithm. The simulation example indicates the efficiency of the proposed algorithms.
Design and development methodologies of Kkongalmon, a location-based augmented reality game using mobile geographic information

Augmented reality is a rapidly growing area of interactive design where it allows virtual contents to be seamlessly integrated with displays of real-world scenes. Along with the meteoric rise of smart mobile devices capable of producing interesting augmented reality environments, the field of interactive game contents has been explored. Pokémon GO, which is released by Niantic, is one of the representative game contents that are implemented using a location-based augmented reality (AR) technology. Even though it is implemented on the basis of the famous and powerful intellectual property (IP) which is Pokémon, the game can be said to be a good example that shows how location-based AR technology using mobile geographic information is being utilized. In the Korean game industry, however, AR-based games are not being paid much attention to. This paper aims at describing the current situation of and future prospects for AR technology and identifying location-based AR game development methods, problems, and improvements by investigating Kkongalmon. In addition, this paper presents efficient development methodologies including design guidelines, tools, and interfaces in AR applications using mobile geographic information. This will enable the improvement of understanding of user experience in mobile AR applications.
Phoebe: Pronunciation-aware Contextualization for End-to-end Speech Recognition

End-to-End (E2E) automatic speech recognition (ASR) systems learn word spellings directly from text-audio pairs, in contrast to traditional ASR systems which incorporate a separate pronunciation lexicon. The lexicon allows a traditional system to correctly spell rare words observed only in LM training, if their phonetic pronunciation is known during inference. E2E systems, however, are more likely to misspell rare words.We propose an E2E model which benefits from the best of both worlds: it outputs graphemes, and thus learns to spell words directly, while leveraging pronunciations for words which might be likely in a given context. Our model is based on the recently proposed Contextual Listen, Attend, and Spell (CLAS) model. As in CLAS, our model accepts a set of bias phrases, which are first converted into fixed length embeddings which are provided as additional inputs to the model. Unlike CLAS, which accepts only the textual form of the bias phrases, the proposed model also has access to the corresponding phonetic pronunciations, which improves performance on challenging sets which include words unseen in training. The proposed model provides a 16% relative word-error-rate reduction over CLAS when both the phonetic and written representation of the context bias phrases are used.
Faculty awareness of the economic and environmental benefits of augmented reality for sustainability in Saudi Arabian universities

In recent decades, smart technology has been one of the major thought-provoking research subjects in the quest to meet the needs of society in ways that do not damage or deplete the environment and that conserve natural resources. Augmented reality is an emerging technology that blends three-dimensional virtual objects with reality. Technologies such as Augmented Reality can be useful tools to promote sustainable development in higher education in terms of various economic, social and environmental considerations. This paper aims to investigate faculty members’ awareness of the potential benefits of incorporating Augmented Reality technology in Saudi Arabian universities in terms of its economic and environmental sustainability. For this study, quantitative data was collected by means of a survey questionnaire where participants responded to statements on a five-point Likert scale that ranged from “totally agree” to “totally disagree”. The study was conducted with a sample comprising 228 academic and e-learning department staff from Saudi universities. The exploratory factor analysis technique was utilised to identify factors related to awareness of advantages of using Augmented Reality in education, particularly in relation to sustainability. Two factors were identified, namely environmental and economic factors. Study findings indicated that academic and e-learning department staff believe that the use of Augmented Reality in higher education has positional environmental and economic sustainability benefits. The findings from this study provide insights that will assist further studies regarding AR in the Gulf Cooperation Council countries.