Reviewerly/exHarmony huggingface dataset:
Ensemble Clustering Algorithm Development for Tabular Data by a Given Partition Quality Measure

Over the years, ensemble algorithms became a common solution for supervised learning tasks as they show more robustness and usually are more accurate. But for unsupervised learning and clustering in particular ensemble methods remain poorly researched. In this work we propose a group of ensemble clustering algorithms by exploiting existing ideas and improving them with modern hyperparameter tuning algorithms and quality measure choosing heuristics. Our proposed approach uses MASSCAH to choose base clustering algorithm and optimize its hyperparameters using SMAC. The proposed solution was experimentally compared to existing solutions and k-means algorithm, tuned by an expert. Experiments were conducted on 64 synthetic datasets from Gaussian distribution and on 20 real-world datasets. We used 4 cluster validity indices to evaluate the quality of resulting partitions. From the results of experiments we can conclude that the proposed solution performs comparable to k-means tuned by an expert and outperforms existing ensemble clustering solutions. Apart from that, the proposed solution can be used as an automatic solution for clustering.
US-Rule: Discovering Utility-driven Sequential Rules

Utility-driven mining is an important task in data science and has many applications in real life. High-utility sequential pattern mining (HUSPM) is one kind of utility-driven mining. It aims at discovering all sequential patterns with high utility. However, the existing algorithms of HUSPM can not provide a relatively accurate probability to deal with some scenarios for prediction or recommendation. High-utility sequential rule mining (HUSRM) is proposed to discover all sequential rules with high utility and high confidence. There is only one algorithm proposed for HUSRM, which is not efficient enough. In this article, we propose a faster algorithm called US-Rule, to efficiently mine high-utility sequential rules. It utilizes the rule estimated utility co-occurrence pruning strategy (REUCP) to avoid meaningless computations. Moreover, to improve its efficiency on dense and long sequence datasets, four tighter upper bounds (LEEU, REEU, LERSU, and RERSU) and corresponding pruning strategies (LEEUP, REEUP, LERSUP, and RERSUP) are designed. US-Rule also proposes the rule estimated utility recomputing pruning strategy (REURP) to deal with sparse datasets. Finally, a large number of experiments on different datasets compared to the state-of-the-art algorithm demonstrate that US-Rule can achieve better performance in terms of execution time, memory consumption, and scalability.
Benchmarking-as-a-service for cloud-hosted DBMS

Database Management Systems (DBMS) operated on cloud resources are the storage backend for a multitude of data-intensive applications. In the process of finding the optimal cloud and DBMS, benchmarking is the common approach to evaluate the non-functional features. However, benchmarking cloud-hosted DBMS is a complex, error-prone and time-consuming task. The Benchmarking-as-a-Service platform benchANT demonstrates how these challenges are resolved by an end-to-end automated benchmarking process.
A fast constructive path planning algorithm for mobile robot navigation

Path planning is a vitally important ability for autonomous mobile robots. Because of the high computational complexity, the optimal solution is generally infeasible since the required computation time increases exponentially with the increase in the problem size. Instead, it is common to rely on heuristic and meta-heuristic algorithms to find near-optimal solutions. Although heuristic algorithms have proven effective and computationally inexpensive in small workspaces, they do not always scale to large environments and tend to get trapped in local minima. Also, while meta-heuristic algorithms are attracting considerable attention because of their effectiveness in optimization, they still require significant computational resources and are non-deterministic. In this paper, we introduce a novel Fast Constructive Algorithm (FCA) for deterministic path optimization that requires comparatively few computational resources to generate an optimized path. Our proposed FCA efficiently generates the waypoints for a path based only on the obstacles that intersect with the straight-line segment linking the robot's current position to the target location. The key idea is to construct the path by iteratively calculating the best waypoint to avoid the next obstacle in the robot's path. The effectiveness of the FCA is assessed on several maps with distinct complexities and its performance is compared with different state-of-the-art path planning algorithms. Our results show that the proposed FCA is competitive and can outperform existing algorithms in terms of path length and computation time.
Edge devices object detection by filter pruning

Edge computing provides new possibilities for Deep Neural Network (DNN) applications, but the constrained resources of edge devices pose limitations to real-time inference execution. Much effort has been devoted to DNN pruning. However, conventional pruning methods often cause degradation of the detection accuracy. In this work, we try to answer how pruning can be performed without paying the price of performance loss. Conventional filter-level pruning methods remove filters demanded as less important. The drawback is that the pruned information is lost, and the detection accuracy deteriorates. To address this issue, we propose a new filter-level pruning method to enable real-time inference execution of DNNs on edge devices, while minimizing the detection accuracy drop. Our proposed pruning method analyzes layers' properties to assign to each layer a different pruning ratio, proportional to how much pruning the layer will impact the overall detection accuracy. Inside each layer, a percentage of filters that produce similar information is removed. Experimental results show that our pruning method yields accurate and compact DNNs, with minimal loss in performance. For a custom object detection network, based on the popular YOLO v3, we reduce the network filters by 60% and floating-point operations (FLOPs) by 80%. Experimental evaluations on Jetson Nano demonstrate that our proposed pruning method increases the inference speed by 16%. Compared to the original heavy network, the pruned network achieves a comparable accuracy, but with significantly lower memory usage and computational cost, making the solution suitable for real-time inference.
Model Checking and Strategy Synthesis for Multi-agent Systems for Resource Allocation

We present a technique for verifying strategic abilities of multi-agent systems via SAT-based bounded model checking. In our approach we focus on systems of agents that pursue goals with regard to the allocation of shared resources. The problem to be solved is to determine whether a coalition of agents has a joint strategy that guarantees the achievement of all resource goals, irrespective of how the opposing agents in the system act. Our approach does not only decide whether such a strategy exists, but also synthesises the strategy. The technique is based on a propositional logic encoding of the model checking problem. The encoding is satisfiable if and only if some specified coalition of agents has a strategy to reach a resource allocation goal. Each satisfying truth assignment of the encoding characterises a successful strategy.
Gradient Estimation in Model-Based Reinforcement Learning: A Study on Linear Quadratic Environments

Stochastic Value Gradient (SVG) methods underlie many recent achievements of model-based Reinforcement Learning agents in continuous state-action spaces. Despite their practical significance, many algorithm design choices still lack rigorous theoretical or empirical justification. In this work, we analyze one such design choice: the gradient estimator formula. We conduct our analysis on randomized Linear Quadratic Gaussian environments, allowing us to empirically assess gradient estimation quality relative to the actual SVG. Our results justify a widely used gradient estimator by showing it induces a favorable bias-variance tradeoff, which could explain the lower sample complexity of recent SVG methods.
Combinational Fusion and Global Attention of the Single-Shot Method for Synthetic Aperture Radar Ship Detection

Synthetic Aperture Radar (SAR), an active remote sensing imaging radar technology, has certain surface penetration ability and can work all day and in all weather conditions. It is widely applied in ship detection to quickly collect ship information on the ocean surface from SAR images. However, the ship SAR images are often blurred, have large noise interference, and contain more small targets, which pose challenges to popular one-stage detectors, such as the single-shot multi-box detector (SSD). We designed a novel network structure, a combinational fusion SSD (CF-SSD), based on the framework of the original SSD, to solve these problems. It mainly includes three blocks, namely a combinational fusion (CF) block, a global attention module (GAM), and a mixed loss function block, to significantly improve the detection accuracy of SAR images and remote sensing images and maintain a fast inference speed. The CF block equips every feature map with the ability to detect objects of all sizes at different levels and forms a consistent and powerful detection structure to learn more useful information for SAR features. The GAM block produces attention weights and considers the channel attention information of various scale feature information or cross-layer maps so that it can obtain better feature representations from the global perspective. The mixed loss function block can better learn the positions of the truth anchor boxes by considering corner and center coordinates simultaneously. CF-SSD can effectively extract and fuse the features, avoid the loss of small or blurred object information, and precisely locate the object position from SAR images. We conducted experiments on the SAR ship dataset SSDD, and achieved a 90.3% mAP and fast inference speed close to that of the original SSD. We also tested our model on the remote sensing dataset NWPU VHR-10 and the common dataset VOC2007. The experimental results indicate that our proposed model simultaneously achieves excellent detection performance and high efficiency.
An Efficient Robust Color Watermarking Algorithm Based on DWT, DCT, BFO and Implementation

Digital watermarking is getting more research and industry attention. Digital multimedia data allows for robust and simple data editing and modification. However, the spread of digital media presents concerns for digital content owners. It is important to note that digital data can be copied without quality or content loss. This has a considerable impact on copyright holders' ability to safeguard their intellectual property rights. The method of transmitting information by imperceptibly embedding it into digital media is digital watermarking. There are various methods in literature, such as DWT and DCT, which take full energy, are seen and integrated. New strategies and procedures for optimization are required. The present study proposes a novel design and computation technique based on the discrete wavelet and discrete cosine transforms. Watermarking techniques have been progressing to shield media content such as text, audio, video, etc. From copyright. The proposed hybrid DWT-DCT Bacterial Foraging Optimization (BFO) technique improves the efficiency of watermarking digital images by 97%. Bacterial foraging optimization (BFO) is an innovative technique for intelligent optimization. It is a widely used optimization algorithm in a wide variety of applications. However, when compared to other optimizers, the BFO performs poorly in terms of convergence. This technique uses a high-frequency image region. A variety of techniques are compared with the (NCC) Normalized Cross Correlations, (PSNR) Peak Noise Signal Ratio and IF (Image Fidelity). The highest performance is seen in DWT-DCT-BFO watermarking.
<sup>2</sup>β-softmax: A Hardware-Friendly Activation Function with Low Complexity and High Performance

Softmax activation function is widely used in deep learning for multi-classification tasks. However, because of the exponentiation computation, its complexity is high in hardware implementation. Without adversely affecting mathematical characteristics and functions, we propose a new hardware-friendly softmax function: <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> β-softmax. The experiment based on MNIST dataset demonstrates the effectiveness of the proposed method. And compared with the traditional softmax function, its superiority lies in that the power consumption and area can be reduced by more than 50% in the hardware implementation.
Smart home applications for cognitive health of older adults

The lifespan of older people is growing together with their proportion in the population growth in the older population. This leads to an increasing need for support services for older adults who will inevitably experience a significant decrease in their cognitive capabilities and mental health conditions. Cognitive functions, including memory, attention, sleeping, problem-solving activities, or speed processing, are playing a crucial role in everyone’s independent daily life. Technologies can help maintain their independence and improve quality of life, reducing the care costs. In this regard, Smart home applications (SMAs) offer a solution to the complex needs of older adults and their families, monitoring physiological and functional issues, as well as aiding in emergency detection and response. This chapter provides an overview of current applications reported in the scientific literature, identifies the frameworks proposed for designing these types of applications, and defines evidence-based recommendations for designing SMAs for cognitive health of older adults.
Sensor Simulation for Monocular Depth Estimation using Deep Neural Networks

Depth estimation is one of the basic building blocks for scene understanding. In the case of monocular depth estimation using neural networks, many such approaches are highly hardware dependent because they result in a task- and environment-specific optimizing problem. Most DNN methods use commonly available datasets which leads to overfitting on particular sensor properties. Finding a generalized model with the consideration of different hardware properties of sensors and platforms is challenging if not impossible. For this reason, it is desirable to adapt existing and well-trained models into a new domain in order to let them simulate different depth sensors without the need for large datasets and time-consuming learning. Therefore, a small dataset has been created with the Structure Sensor for evaluating the transferable structural characteristic between neural networks. Finally, two input feature representations for the neural networks are considered to mimic the depth sensor including its artifacts including holes. The results show that a simple domain adaptation technique and a small dataset are adequate to simulate and adapt to a specific domain from a target domain. Therefore, the network is able to accurately predict depth maps as if they were created by a specific depth sensor. This also includes unique artifacts of the sensor, thereby allowing for a plausible simulation of specific depth sensing hardware which is beneficial for areas like prototyping in the context of Augmented Reality.
CoMeT: Configurable Tagged Memory Extension

Commodity processor architectures are releasing various instruction set extensions to support security solutions for the efficient mitigation of memory vulnerabilities. Among them, tagged memory extension (TME), such as ARM MTE and SPARC ADI, can prevent unauthorized memory access by utilizing tagged memory. However, our analysis found that TME has performance and security issues in practical use. To alleviate these, in this paper, we propose CoMeT, a new instruction set extension for tagged memory. The key idea behind CoMeT is not only to check whether the tag values in the address tag and memory tag are matched, but also to check the access permissions for each tag value. We implemented the prototype of CoMeT on the RISC-V platform. Our evaluation results confirm that CoMeT can be utilized to efficiently implement well-known security solutions, i.e., shadow stack and in-process isolation, without compromising security.
Gain Variation of Phased-Arrays with Normally Distributed Pointing Errors

When a phased array is placed on positioners, the limited accuracies of positioners would cause some random degradation of array gains. In this work, the array gains from an array with random rotations were evaluated and approximated by 2nd-order polynomials. Further, the probability density functions (PDF) were derived and agreed with simulations by the Monte Carlo method.
Modification of Objective Full Reference Metrics for Image Quality Measurement and Evaluation

This paper presents a modification of objective PSNR metric for image quality measurement and evaluation. In the modified metric the MSE is substituted by 3 different distance measures: MAE, Euclidean distance and Canberra distance. All metrics were tested with 12 different 3-channels, 8-bits images. Every test image was distorted by 10 different values of dispersion of white Gaussian noise with zero mean. The experiments show that the dispersion of the PSNR with Euclidean distance has the lowest standard deviation and thus improved the original PSNR. It is also suggested that due to normality of the data and their deviation, the results has to be accepted with some standard uncertainty.
Research on Segmentation and Recognition of Liver CT Image Based on Multi-scale Feature Fusion

Liver segmentation is a common difficulty and key problem in the field of medical imaging. Aiming at the problem that the existing liver CT images have various shapes and the boundary regions are difficult to segment. So an improved U-Net liver segmentation method is proposed. First, introduce the CSPP module with a dilated convolution and multi-scale feature fusion structure to expand the receptive field while extracting richer spatial information; secondly, connect the CSPP modules in series and combine the residual structure to form the DREG module and join the U-Net network jump connection structure. The high-level and low-level feature information is fused to retain the subtle edge information of the liver; the above method solves the problem of diverse sample data shapes and difficult segmentation of boundary regions. Experimental results on the MICCAI 2017 Liver Tumor Segmentation(LiTS) challenge dataset show that this method obtains a good accuracy rate and has high application value for the clinical auxiliary diagnosis of liver CT images.
An Image Processing Techniques For Pattern Detection In Covid-19

The use of face mask is essential at public places for the prevention of COVID-19. In order to detect whether someone has worn mask or not there is need of a mechanism that captures the image and detect the mask pattern and takes decision accordingly. Applications of image processing for decision making after face mask pattern detection to assist the prevention of COVID-19 has been considered. The existing researches in field of image processing and pattern detection have been considered. The systems that are making use of neural network to train the network model in order to perform prediction are also examined. The major issues of existing research are the time consumption and lack of accuracy. More over the size of image also influence the performance of system. Various mechanisms used in case of existing research to perform pattern detection have been reviewed. Present work considers the role of image sensor, image processing unit, image compression mechanism and pattern detection mechanism.
A novel dehazing algorithm based on Retinex principle

Due to the atmospheric scattering or light intensity attenuation, phenomena such as haze, fog, or smoke can cause the obvious degradation of image qulity. By analysis of the atmospheric attenuation and scattering, a novel dehazing algorithm based Retinex principle and homomorphic filtering is proposed in this paper. Experimental results on different hazy images demonstrate the effectiveness of the proposed algorithm and high quality haze-free images can be obtained after the histogram equalization process of the proposed algorithm.
Array computing based system for visual servoing of neuroprosthesis of upper limbs

A hardware-software system implemented for visual servoing of prosthetic arms. The prosthetic system comprises glass worn eye-tracker and video camera and a prosthesis - mounted stereo camera system. The proposed light-weight architecture has to be implemented as a wearable device. We propose a hybrid solution which contains object selection and recognition, in glass camera view, object matching to the prosthesis-mounted camera to compute depth map for object reaching by the prosthetic arm. The object selection and object matching both use the Scale Invariant Feature Transform (SIFT) algorithm. The SIFT algorithm is time-consuming, so the FPGA acceleration is proposed for the light-weight device. The proposed implementation is compatible with real-time servoing of the prosthetic arm.
Click Fraud Detection Approaches to analyze the Ad Clicks Performed by Malicious Code

Abstract Mobile PR is an important component of the mobile app ecosystem. A major threat to this ecosystem’s long-term health is click fraud, which involves clicking on ads while infected with malware or using an automated bot to do it for you. The methods used to identify click fraud now focus on looking at server requests. Although these methods have the potential to produce huge numbers of false negatives, they may easily be avoided if clicks are hidden behind proxies or distributed globally. AdSherlock is a customer-side (inside the app) efficient and deployable click fraud detection system for mobile applications that we provide in this work. AdSherlock separates the computationally expensive click request identification procedures into an offline and online approach. AdSherlock uses URL (Uniform Resource Locator) tokenization in the Offline phase to create accurate and probabilistic patterns. These models are used to identify click requests online, and an ad request tree model is used to detect click fraud after that. In order to develop and evaluate the AdSherlock prototype, we utilise actual applications. It injects the online detector directly into an executable software package using binary instrumentation technology (BIT). The findings show that AdSherlock outperforms current state-of-the-art methods for detecting click fraud with little false positives. Advertisement requests identification, mobile advertising fraud detection are some of the keywords used in this article.
Punctual Cloud: Unbinding Real-time Applications from Cloud-induced Delays

Cloud computing has become a prominent technology for the computing paradigm in various industrial sectors nowadays. For most industrial applications to perform in real-time, the support of periodic computing is required. However, it remains a challenge when the computing is executed in a cloud, since both the network connection and the cloud environment are uncertain. In this paper, we propose a new architecture to deploy real-time applications in the cloud. We call it punctual cloud. We detail the implementation and demonstrate how punctual cloud is deployed in a cloud-native manner on Kubernetes. We evaluate the system&#x2019;s performance with a real-time resource allocation problem and show that, compared to a system without punctual cloud, which has maximum 40&#x0025; punctual deliveries, our proposed architecture can attain over 90&#x0025; responses to be delivered punctually for the application, while also being capable of remedying the performance degradation caused by long and uncertain response delays in the system.