Reviewerly/exHarmony huggingface dataset:
Facial Emotion Recognition Using HOG and Convolution Neural Network

Due to numerous difficulties, including the variation in face shapes between individuals, the challenge of recognizing dynamic facial attributes, the poor quality of digital images, etc., detecting human emotion depending on facial expression is difficult for the computer vision community.Thus, in this study, we propose an approach for emotion recognition depending on facial expression using histogram of oriented gradients and convolution neural network (HOG-CNN).The HOG-CNN composed of three stages, median filter, HOG, and CNN.The first stage is preprocessing using median filter.The second stage is feature extraction using HOG.The third stage is classification using CNN.The proposed method was tested and evaluated on the UMD face database.The system attained a high performance with a mean average accuracy of 98.07%, average precision of 94.78%, and average recall of 97.15%.
Fargraph+: Excavating the parallelism of graph processing workload on RDMA-based far memory system

Disaggregated architecture brings new opportunities to memory-consuming applications like graph processing. It allows one to outspread memory access pressure from local to far memory, providing an attractive alternative to disk-based processing. Although existing works on general-purpose far memory platforms show great potentials for application expansion, it is unclear how graph processing applications could benefit from disaggregated architecture, and how different optimization methods influence the overall performance. In this paper, we take the first step to analyze the impact of graph processing workload on disaggregated architecture by extending the GridGraph framework on top of the RDMA-based far memory system. We propose Fargraph+, a system with parallel graph data offloading and far memory coordination strategy for enhancing efficiency of graph processing workload on RDMA-based far memory architecture. Specifically, Fargraph+ reduces the overall data movement through a well-crafted, graph-aware data segment offloading mechanism. In addition, we use optimal data segment splitting and asynchronous data buffering to achieve graph iteration-friendly far memory access. We further configure efficient parallelism-oriented control to accelerate performance of multi-threading processing on graph iterations while improving memory efficiency of far memory access by utilizing RDMA queue features. We show that Fargraph+ achieves near-oracle performance for typical in-local-memory graph processing systems. Fargraph+ shows up to 11.2× speedup compared to Fastswap, the state-of-the-art, general-purpose far memory platform.
Exploring the efficacy and reliability of automatic text summarisation systems: Arabic texts in focus

This study compared the salient features of the three basic types of automatic text summarisation methods (ATSMs)—extractive, abstractive, and real-time—along with the available approaches used for each type. The data set comprised 12 reports on the current issues on automatic text summarisation methods and techniques across languages, with a special focus on Arabic whose structure has been largely claimed to be problematic in most ATSMs. Three main summarizers were compared: TAAM, OTExtSum, and OntoRealSumm. Further to this, a humanoid version of the summary of the data set was prepared, and then compared to the automatically generated summary. A 10-item questionnaire was built to help with the assessment of the target ATSMs. Also, Rouge analysis was performed to assess the efficacy of all techniques in minimising the redundancy of the data set. Findings showed that the precision of the target summarizers differed considerably, as 80% of the data set has been proven to be aware of the problems underlying ATSMS. The remaining parameters were in the normal range (65–75%). In light of the equations-based assessment of ATSMS, the highest range was noted with the removal of stop word, the least range was noted with POS tagging, stem weight, and stem collection. Regarding Arabic, the statistical analysis has been proven to be the most effective summarisation method (accuracy = 57.59%; reminiscence = 58.79%; F-Value = 57.99%). Further research is required to explore how the lexicogrammatical nature of languages and generic text structure would affect the text summarisation process.
3-Dimensional Topology in Materials Science

In this article, we discuss applications of topology to materials science. We explain how topology is used to express the structure and shape of materials. The structure of a multicyclic polymer is described by applying graph theory and the 3-dimensional shape is described by applying knot theory. Polycontinuous patterns of microphase separation of a block copolymer melt can be analyzed by applying the 3-dimensional topology. We also discuss the topological structures of interlocking molecules composed of metal-peptide rings using the concept of polyhedral links.
Automatic configuration of programmable photonic digital circuits based on genetic algorithms

Programmable photonic circuits, a good way to provide enough function flexibility for photonic circuits, have great potential to be realizing multi-function. However, it has been a challenge to configure a huge mesh network in order to achieve the target function. In this paper, a path selection method based on genetic algorithms has been introduced to complete the automated setting of the huge mesh network. pSim, the most advanced simulation tool of integrated circuits, is used for verification of the performance of automatically selecting the optical path on the square mesh network. Moreover, an optoelectronic hybrid circuit, which can implement state transition, based on this algorithm is proposed.
OpenMSIStream: A Python package for facilitating integration of streaming data in diverse laboratory environments

Eminizer et al., (2023). OpenMSIStream: A Python package for facilitating integration of streaming data in diverse laboratory environments. Journal of Open Source Software, 8(83), 4896, https://doi.org/10.21105/joss.04896
Influence of damp heat testing on color revent characteristics of optical imaging system

Optical imaging systems are widely used sensing devices that may be exposed to long-term or harsh environments. On the other hand, there are few studies on the evaluation of the imaging performance of optical imaging systems by environmental aging tests. In this paper, a typical and common environmental testing, damp heat (steady state) testing, is selected to investigate the influence of its imaging quality. It was found that the imaging quality of both undistorted and distorted optical images was significantly degraded after about 20 cycles of damp heat testing. The color error parameters and noise parameters that characterize its image quality are significantly increased. And this decline in image quality is irreversible. This article will provide a reference for rationally evaluating the service life of an optical imaging system.
A Gaussian process guided super resolution sampling strategy for the efficient recovery of complex surfaces

Abstract Accurate recovery of complex surfaces of manufactured artefacts frequently requires intensive sampling, resulting in inefficient measurements for some point-by-point probe instruments. To tackle this problem, we fully exploit Gaussian process (GP) to guide the super resolution (SR) model to perform efficient and accurate sampling. The model makes use of a kernel-based GP method to model these low-frequency geometric features, while a pretrained SR method with multiple residual attention blocks is used to focus on the high-frequency features and further improve the details of the surface. In addition to geometric errors and distance information, global uncertainty from the statistical properties of the GP and an additional feature error from the SR are combined as critical criteria to select the most informative points of the surface. The effectiveness of the proposed method was demonstrated through several experiments on synthetic and real-world data, showing that the proposed method achieves state-of-the-art performance for pointwise measurements.
Super-Resolution Reconstruction of Remote Sensing Images Based on Symmetric Local Fusion Blocks

In view of the rich information and strong autocorrelation of remote sensing images, a super-resolution reconstruction algorithm based on symmetric local fusion blocks is proposed using a convolutional neural network based on local fusion blocks, which improves the effect of high-frequency information reconstruction. By setting local fusion in the residual block, the problem of insufficient high-frequency feature extraction is alleviated, and the reconstruction accuracy of remote sensing images of deep networks is improved. To improve the utilization of global features and reduce the computational complexity of the network, a residual method is used to set the symmetric jump connection between the local fusion blocks to form the symmetry between them. Experimental results show that the reconstruction results of 2-, 3-, and 4-fold sampling factors on the UC Merced and nwpu-resisc45 remote sensing datasets are better than those of comparison algorithms in image clarity and edge sharpness, and the reconstruction results are better in objective evaluation and subjective vision.
A Hybrid Binary Bird Swarm Optimization (BSO) and Dragonfly Algorithm (DA) for VM Allocation and Load Balancing in Cloud

The cloud platform is becoming one of the fastest-rising environments in human activities, connecting the whole world in the upcoming decades. The three crucial aspects of cloud computing that enhance the quality of service are load balancing, task scheduling, and resource allocation. To address these issues, the research proposed dynamic degree balance with CPU_based VM allocation policy integrated with hybrid bird swarm optimization (BSO) and dragonfly algorithm (DA). The proposed algorithm focuses on improving the overall performance of the system by limiting DoI, execution time, and response time, while also maintaining system balance. In the CloudSim tool, D2B_CPU based BSO-DA is implemented and evaluated. The simulation results, on the other hand, show that the proposed BSO and DA-based load balancing scheme is significantly more effective in balancing load optimally among virtual machines more quickly than existing algorithms. The proposed method's efficiency is evaluated by comparing it to other existing techniques.
Long and short spatial-temporal relations model for automatic generation of Labanotation

As a major research field in computer vision, automatic generation of Labanotation has been attracting the attention of many researchers. These researchers use various methods to engage in dance education and protection, but the existing methods do not consider the spatial-temporal dependence modeling of dance movements. Therefore, they do not represent complex dance movements optimally. We propose an automatic generation network of Labanotation based on the long and short spatial-temporal relations. It includes local spatial-temporal feature extraction network, global spatial-temporal feature extraction network, and local and global feature fusion network. It can align the input and output sequences while modeling the skeletal spatial-temporal relation. The local spatial-temporal features among short interval skeleton sequence relationship are obtained through the multi-scale convolution of time and space. The global spatial-temporal features are learned through the transformer network to obtain the relationship among skeleton sequences with long interval. For the output of the two networks, we use the pyramid squeeze attention network to exchange information in long and short spatial-temporal information, achieving complementarity to improve the accuracy of action recognition. Experimental results show that the proposed method outperforms the state-of-the-art methods on laban16 and laban48, which are common datasets for the research of Labanotation.
Video Surveillance Camera Identity Recognition Method Fused With Multi-Dimensional Static and Dynamic Identification Features

With the development of smart cities, video surveillance networks have become an important infrastructure for urban governance. However, by replacing or tampering with surveillance cameras, an important front-end device, attackers are able to access the internal network. In order to identify illegal or suspicious camera identities in advance, a camera identity identification method that incorporates multidimensional identification features is proposed. By extracting the static information of cameras and dynamic traffic information, a camera identity system that incorporates explicit, implicit, and dynamic identifiers is constructed. The experimental results show that the explicit identifiers have the highest contribution, but they are easy to forge; the dynamic identifiers rank second, but the traffic preprocessing is complex; the static identifiers rank last but are indispensable. Experiments on 40 cameras verified the effectiveness and feasibility of the proposed identifier system for camera identification, and the accuracy of identification reached 92.5%.
An Efficient ECK-Secured FCM-Based Firefly Optimization Algorithm for Dynamic Resource Sharing in Multi-Tenant SaaS Service Clouds

Multi-tenancy in cloud computing is one of the foremost approaches to share one application instance among different customers and it is generally used by Software as a service (SaaS) providers. The main objective of the proposed work is to minimize the down time of virtual machines essential for resource provisioning using cluster based secure dynamic resource sharing. The proposed secure dynamic resource sharing approach allocates the service tenants to matched Virtual Machines(VMs) and allocates the VMs into physical host machines using the elliptic curve key based firefly optimization approach. First the functional characteristics of service users are grouped into clusters using FCM (Fuzzy C_means clustering) algorithm as tenants. After clustering, the tenant users are checked for authorization with the help of elliptic curve key value. When the users in the tenants are authorized then the grouped services are scheduled dynamically using the firefly optimization algorithm. The result of the work is appraised in terms of resource utilization, execution time, speed, and speedup.
Exploitation of second-and fourth-order PDEs to improve Lossy compression of noisy images

Abstract This paper presents a lossy compression method for noisy images. The main contributions in this paper are: improve the performance of noisy image compression algorithms by using partial differential equations (PDEs) as an image preprocessing filter and show that it is more advantageous to apply a restoration filter before compressing noise images. Method : The preprocessing filter is applied to the noisy image first. A comparing of the obtained outcomes with those in the scientific literature devoted to the restoration of noisy images is performed. The filtered image is then submitted to a compression algorithm. The compression algorithm employed is a hybrid of the following: DWT + SPIHT + HUFFMAN. Results : The simulation results demonstrate that the suggested technique is as efficient as approaches described in the scientific literature for picture compression and/or image restoration.
Nonlinear image translation using adaptive rectifier with structure adaption

When an image translation task contains intradomain translations, the untranslated source image will be discriminated as the real by the discriminator. Thus if the network’s nonlinearity is insufficient, the generator can fool the discriminator by producing output that resembles the source image. We propose an activation function termed “adaptive rectified linear unit (ReLU) with structure adaption (SA-AdaReLU)” to enhance the control and nonlinearity of the network in image translation tasks. SA-AdaReLU is composed of two technologies: adaptive ReLU (AdaReLU) and structural adaptive function. The proposed AdaReLU can dynamically change the channel-wise data distribution to better utilize the features in negative regions, which helps to improve the control of the network when inner-domain translation is involved. Meanwhile, the structural adaptive function further enhances the feature selection ability of adaptive instance normalization (AdaIN) and enhances the network’s spatial nonlinearity to manipulate the spatial structure on the feature maps. Extensive experiments have demonstrated the effectiveness of the proposed SA-AdaReLU. In addition, with SA-AdaReLU, fewer layers are required to achieve the same visual effect for building the generator, thus reducing the computational complexity.
Optimal event log sanitization for privacy-preserving process mining

Event logs that originate from information systems enable comprehensive analysis of business processes. These logs serve as the starting point for the discovery of process models or the analysis of conformance of a log with a given specification. However, logs potentially contain personal information about individuals involved in process execution. In this paper, we therefore address the risk of privacy attacks on event logs. Specifically, we rely on group-based privacy guarantees instead of noise insertion in order to enable anonymization without adding new behaviour to the log. To this end, we propose two new algorithms for event log sanitization that provide privacy guarantees in terms of k-anonymity for the behavioural perspective of a process and t-closeness for sensitive information associated with events. The algorithms thereby avoid the disclosure of employee identities, prevent the identification of employee membership in the log, and preclude the characterization of employees based on sensitive attributes. Our algorithms overcome the limitations of an existing, greedy algorithm, providing users with a trade-off between computational complexity and the utility of the sanitized event log for downstream analysis. Our Experiments demonstrate that sanitization with our algorithms generates event logs of higher utility compared to the state of the art.
Crowd sensing and spatiotemporal analysis in urban open space using multi‐viewpoint geotagged videos

Abstract Increasing concern for urban public safety has motivated the deployment of a large number of surveillance cameras in open spaces such as city squares, stations, and shopping malls. The efficient detection of crowd dynamics in urban open spaces using multi‐viewpoint surveillance videos continues to be a fundamental problem in the field of urban security. The use of existing methods for extracting features from video images has resulted in significant progress in single‐camera image space. However, surveillance videos are geotagged videos with location information, and few studies have fully exploited the spatial semantics of these videos. In this study, multi‐viewpoint videos in geographic space are used to fuse object trajectories for crowd sensing and spatiotemporal analysis. The YOLOv3‐DeepSORT model is used to detect a pedestrian and extract the corresponding image coordinates, combine spatial semantics (such as the positions of the pedestrian in the field of view of the camera) to build a projection transformation matrix and map the object recorded by a single camera to geographic space. Trajectories from multi‐viewpoint videos are fused based on the features of location, time, and directions to generate a complete pedestrian trajectory. Then, crowd spatial pattern analysis, density estimation, and motion trend analysis are performed. Experimental results demonstrate that the proposed method can be used to identify crowd dynamics and analyze the corresponding spatiotemporal pattern in an urban open space from a global perspective, providing a means of intelligent spatiotemporal analysis of geotagged videos.
Studying the challenges of developing hardware description language programs

Developing domain specific architectures (e.g., Google’s TPU) typically requires writing programs in Hardware Description Languages (HDLs). Compared to traditional general-purpose programming languages (GPPLs) (e.g., C++, Java, Python), developing programs in HDLs (e.g., VHDL or Verilog) lacks support from our community. Such an imbalance in the support for GPPLs and HDLs will impede future advances in computer systems. We believe that our software engineering community should pay more attention to supporting HDL development. Thus, we make an initial attempt in this direction to study the challenges of developing programs in HDLs by mining HDL-related questions in technical forums. We identified 16,700 HDL-related questions in two Stack Exchange forums: Stack Overflow (SO) and Electrical Engineering (EE) Stack Exchange. Through qualitative analysis, topic modeling, and quantitative analysis, we examined the types of questions, the questions’ topics, and identified the most challenging topics for developers. We identified ten types of HDL-related questions, including seven types identified in prior work and three new types more relevant to HDLs (e.g., questions related to code explanation and tool search). We also observed that most of the challenges facing HDL developers are similar to those facing GPPL developers, while some challenges (e.g., lower-level operations such as bit and register operations) are more specific to HDLs. Finally, we observed that HDL-related questions are less likely and take a longer time to get accepted answers than GPPL-related questions, and identified the most challenging topics of questions (e.g., file/memory I/O). Our work identified opportunities for different stakeholders in the software and hardware communities to improve the practices of developing HDL programs: software engineering researchers may leverage their expertise to help in advancing HDL languages and methodology, such as to improving the language abstractions for low-level operations such as bit/register operations or memory/file I/Os; Stack Exchange and its moderators may leverage the community size and expertise in both the SO and EE forums to collectively recommend experts to answer questions related to HDLs; HDL language and library developers may provide more actionable error messages, better documentation and logging support to help HDL developers address their encountered issues; tool developers are encouraged to provide advanced IDEs and testing frameworks to help HDL developers improve their development and testing productivity.