Reviewerly/exHarmony huggingface dataset:
Assessment of despeckle filtering algorithms for segmentation of breast tumours from ultrasound images

In the present work, the performance assessment of despeckle filtering algorithms has been carried out for (a) noise reduction in breast ultrasound images and (b) segmentation of benign and malignant tumours from breast ultrasound images. The despeckle filtering algorithms are broadly classified into eight categories namely local statistics based filters, fuzzy filters, Fourier filters, multiscale filters, non-linear iterative filters, total variation filters, non-local mean filters and hybrid filters. Total 100 breast ultrasound images (40 benign and 60 malignant) are processed using 42 despeckle filtering algorithms. A despeckling filter is considered to be appropriate if it preserves edges and features/structures of the image. Edge preservation capability of a despeckling filter is measured by beta metric (β) and feature/structure preservation capability is quantified using image quality index (IQI). It is observed that out of 42 filters, six filters namely Lee Sigma, FI, FB, HFB, BayesShrink and DPAD yield more clinically acceptable images in terms of edge and feature/structure preservation. The qualitative assessment of these images has been done on the basis of grades provided by the experienced participating radiologist. The pre-processed images are then fed to a segmentation module for segmenting the benign or malignant tumours from ultrasound images. The performance assessment of segmentation algorithm has been done quantitatively using the Jaccard index. The results of both quantitative and qualitative assessment by the radiologist indicate that the DPAD despeckle filtering algorithm yields more clinically acceptable images and results in better segmentation of benign and malignant tumours from breast ultrasound images.
Lane Detection With a High-Resolution Automotive Radar by Introducing a New Type of Road Marking

Autonomous driving will significantly shape the near future of transportation that requires the distinct knowledge of the driving environment, especially the lane boundaries ahead of the vehicle. For this sensing task, optical and automotive radar sensors are mostly applied, while the radar sensor is less sensitive to non-ideal illumination conditions. Road boundaries can be detected by radar sensors through objects like guardrails, delineators, road curbs, and so on. However, in a multi-lane roadway or with missing roadside infrastructure, the adjacent lanes and the road boundary also have to be recognized by the radar sensors. This makes it necessary to integrate appropriate radar reflectors into the lane or road boundaries. Such reflectors, when integrated in the road, should not harm the vehicle tires, so their height shall be low, but they still have to be able to reflect the radar signals properly. Conventional road markings can be accordingly adjusted with such reflectors to support vehicle guidance with radar sensors. In this paper, the scattering property of various types of reflectors is evaluated by simulations over a wide angular and range geometry. The simulation results are analyzed with regard to the view angle influence on the radar cross section. Then, the specimen measurement results will be presented and finally the clustering process will be introduced.
Influence of a Marker-Based Motion Capture System on the Performance of Microsoft Kinect v2 Skeleton Algorithm

Microsoft Kinect sensors are being widely used as low-cost marker-less motion capture systems in various kinematic studies. Previous studies investigated the reliability and validity of Microsoft Kinect sensors by employing marker-based motion capture systems. Both systems employ infrared emitters and detectors to track human posture and physical activities. This paper hypothesizes that the motion capture systems may interfere with Microsoft Kinect one sensor and may influence the sensor's performance in tracking the skeleton. Hence, this paper investigated the impact of a motion capture system on the Microsoft Kinect v2 skeleton algorithm using a mannequin in the presence of eight Qualisys Oqus 300/310 cameras and retroreflective markers. It was found that the motion capture system introduced a destructive impact on the Microsoft Kinect v2 skeleton tracking algorithm. In addition, it was observed that retroreflective markers placed near the joints caused the Microsoft Kinect v2 to give an incorrect reading of estimate the joint position. The motion capture cameras thus caused a time-varying distortion of the Microsoft Kinect estimate of the joint position. It is believed that the inference can be reduced by decreasing the number of markers and avoiding facing the motion capture cameras in sight of Microsoft Kinect v2.
Examination of cybercrime and its effects on corporate stock value

Purpose Cybercrime is a prevalent and serious threat to publicly traded companies. Defending company information systems from cybercrime is one of the most important aspects of technology management. Cybercrime often not only results in stolen assets and lost business but also damages a company’s reputation, which in turn may affect the company’s stock market value. This is a serious concern to company managers, financial analysts, investors and creditors. This paper aims to examine the impact of cybercrime on stock prices of a sample of publicly traded companies. Design/methodology/approach Financial data were gathered on companies that were reported in news stories as victims of cybercrime. The market price of the company’s stock was recorded for several days before the news report and several days after. The percentage change in the stock price was compared to the change in the Dow Jones Industrial average to determine whether the stock price increased or decreased along with the rest of the market. Findings Stock prices were negatively affected in all time periods examined, significantly so in one period. Practical implications This paper describes cases concerning cybercrime, thereby bringing attention to the value of cybersecurity in protecting computers, identity and transactions. Cyber security is necessary to avoid becoming a victim of cybercrime. Specific security improvements and preventive measures are provided within the paper. Preventive measures are generally less costly than repairs after a cybercrime. Originality/value This is an original manuscript that adds to the literature regarding cybercrime and preventive measures.
Emerging Perspectives of Application Programming Interface Strategy: A Framework to Respond to Business Concerns

Software specialists increasingly find themselves in situations where their application programming interface (API)-related decisions have implications on software business. We present a strategic API framework to aid in consideration of business concerns when designing, updating, or maintaining APIs.
Feature Selection with Unsupervised Consensus Guidance

Most of the unsupervised feature selection methods employ pseudo labels generated by clustering to guide the feature selection; however, noisy and irrelevant features degrade the cluster structure, which is ineffective to supervise feature selection. In light of this, we propose the Consensus Guided Unsupervised Feature Selection (CGUFS) framework, which introduces consensus clustering to generate pseudo labels for feature selection. Generally speaking, multiple diverse basic partitions are generated from the data and the consensus clustering is employed to provide the high-quality and robust partition to guide the feature selection in a one-step framework. In addition, complex constraints such as non-negative are removed due to the crisp indicators of consensus clustering. Based on the CGUFS framework, two formulations are put forward by using the utility function and co-association matrix, respectively, and we propose the (weighted) K-means-like optimization solution for efficient solutions with theoretical supports. Moreover, we extend the CGUFS framework to handle multi-view data feature selection. Extensive experiments on several singleview and multi-view data mining data sets in different domains demonstrate that our methods outperform the most recent state-ofthe-art work in terms of effectiveness and efficiency. Some important impact factors and model parameters within CGUFS are thoroughly discussed for practical use.
Discrete Spectral Hashing for Efficient Similarity Retrieval

To meet the required huge data analysis, organization, and storage demand, the hashing technique has got a lot of attention as it aims to learn an efficient binary representation from the original high-dimensional data. In this paper, we focus on the unsupervised spectral hashing due to its effective manifold embedding. Existing spectral hashing methods mainly suffer from two problems, i.e., the inefficient spectral candidate and intractable binary constraint for spectral analysis. To overcome these two problems, we propose to employ spectral rotation to seek a better spectral solution and adopt the alternating projection algorithm to settle the complex code constraints, which are therefore named as Spectral Hashing with Spectral Rotation and Alternating Discrete Spectral Hashing, respectively. To enjoy the merits of both methods, the spectral rotation technique is finally combined with the original spectral objective, which aims to simultaneously learn better spectral solution and more efficient discrete codes and is called as Discrete Spectral Hashing. Furthermore, the efficient optimization algorithms are also provided, which just take comparable time complexity to existing hashing methods. To evaluate the proposed three methods, extensive comparison experiments and studies are conducted on four large-scale data sets for the image retrieval task, and the noticeable performance beats several state-of-the-art spectral hashing methods on different evaluation metrics.
Evaluating the benefits of using proactive transformed-domain-based techniques in fraud detection tasks

The exponential growth in the number of E-commerce transactions indicates a radical change in the way people buy and sell goods and services, a new opportunity offered by a huge global market, where they may choose sellers or buyers on the basis of multiple criteria (e.g., economic, logistical, ethical, sustainability, etc.), without being forced to use the traditional brick-and-mortar criterion. If, on the one hand, such a scenario offers an enormous control to people, both at private and corporate level, allowing them to filter their needs by adopting a large range of criteria, on the other hand, it has contributed to the growth of fraud cases related to the involved electronic instruments of payment, such as credit cards. The Big Data Information Security for Sustainability is a research branch aimed to face these issues in relation to the potential implications in the field of sustainability, proposing effective solutions to design safe environments in which the people can operate and by exploiting the benefits related to new technologies. The fraud detection systems are a significant example of such solutions, although the techniques adopted by them are typically based on retroactive strategies, which are incapable of preventing fraudulent events. In this perspective, this paper aims to investigate the benefits related to the adoption of proactive fraud detection strategies, instead of the canonical retroactive ones, theorizing those solutions that can lead toward practical effective implementations. We evaluate two previously experimented novel proactive strategies, one based on the Fourier transform, and one based on the Wavelet transform, which are used in order to move the data (i.e., financial transactions) into a new domain, where they are analyzed and an evaluation model is defined. Such strategies allow a fraud detection system to operate by using a proactive approach, since they do not exploit previous fraudulent transactions, overcoming some important problems that reduce the effectiveness of the canonical retroactive state-of-the-art solutions. Potential benefits and limitations of the proposed proactive approach have been evaluated in a real-world credit card fraud detection scenario, by comparing its performance to that of one of the most used and performing retroactive state-of-the-art approaches (i.e. Random Forests).
MEMS Accelerometer Mini-Array (MAMA): A Low-Cost Implementation for Earthquake Early Warning Enhancement

Earthquake Early Warning Systems (EEWS) are often challenged when the earthquakes occur outside the seismic network or where the station density is sparse. In these situations, poor locations and large alert delays are more common because of the limited azimuthal coverage and the time required for the wavefield to reach the minimum number of seismic stations to issue an alert. Seismic arrays can be used to derive the directivity of the wavefield and obtain better location. However, they are uncommon because of the prohibitive cost of the sensors. Here, we propose the development of an array-based approach using mini-arrays of low-cost Microelectromechanical Systems (MEMS) accelerometers and show how they can be used to improve EEWS. In this paper, we demonstrate this approach using data from two MEMS Accelerometer Mini-Arrays (MAMA) deployed at University of California Berkeley and Humboldt State University. We use a new low-cost ( &lt;U.S. $150) Data Acquisition Unit and solve for the back azimuth of seven events with magnitudes ranging from Mw 2.7 to 5.1 at distances of 5 km to 106 km.
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning

Deep neural networks (DNN)-based machine learning (ML) algorithms have recently emerged as the leading ML paradigm particularly for the task of classification due to their superior capability of learning efficiently from large dataseis. The discovery of a number of well-known attacks such as dataset poisoning, adversarial examples, and network manipulation (through the addition of malicious nodes) has, however, put the spotlight squarely on the lack of security in DNN-based ML systems. In particular, malicious actors can use these well-known attacks to cause random/targeted misclassification, or cause a change in the prediction confidence, by only slightly but systematically manipulating the environmental parameters, inference data, or the data acquisition block. Most of the prior adversarial attacks have, however, not accounted for the pre-processing noise filters commonly integrated with the ML-inference module. Our contribution in this work is to show that this is a major omission since these noise filters can render ineffective the majority of the existing attacks, which rely essentially on introducing adversarial noise. Apart from this, we also extend the state of the art by proposing a novel pre-processing noise Filter-aware Adversarial ML attack called FAdeML. To demonstrate the effectiveness of the proposed methodology, we generate an adversarial attack image by exploiting the "VGGNet" DNN trained for the "German Traffic Sign Recognition Benchmarks (GTSRB)" dataset, which despite having no visual noise, can cause a classifier to misclassify even in the presence of preprocessing noise filters.
An improved privacy preservation technique in health-cloud

In today’s cloud computing environment, health-cloud preserves the person specific sensitive information for several purposes such as bio-medical research, health insurance companies, medical data analysis, etc. When any authorized person accesses these clouds, the released data should not compromise any individuals’ privacy and it remains useful as well. In the health-cloud system, the data must be released in such a way that any individuals’ identity cannot be revealed. The database management system alone cannot ensure any individual’s privacy. The Access Control (AC) models are also not able to protect the data from indirect access or multiple queries. To remove such issues inference control is one of the techniques which ensures the data confidentiality from indirect data access. In this paper, we have proposed a hybrid technique which includes two different inference control techniques, query set size restriction and k-anonymity to ensure individuals’ privacy. A query set size restriction is used to prevent the sensitive data from inference attacks, whereas k-anonymity is implemented to protect the data from linking attacks. Both these techniques reach a certain privacy level with satisfactory data utilization. We have also generated a rule set to increase the privacy of healthcare data.
Stability analysis of fractional Quaternion-Valued Leaky Integrator Echo State Neural Networks with multiple time-varying delays

This paper studies the stability problem of fractional order continuous-time Quaternion-Valued Leaky Integrator Echo State Neural Networks (NN) with multiple time-varying delays. The NN is decomposed into four real-valued systems without considering the non-commutativity of quaternion multiplication resulting in Hamilton rules. Then, the delay-independent stability analysis of the NN equilibrium point with Lipschitz continuous activation function is derived in the real space. The existence and uniqueness of the equilibrium point are also given using the contraction map in the real space. Simulations results show the feasibility of the method.
High-Speed ECC Processor Over NIST Prime Fields Applied With Toom–Cook Multiplication

In this paper, a high-speed elliptic curve cryptography (ECC) processor specialized for primes recommended by the National Institute of Standards and Technology (NIST) was constructed. Toom-Cook multiplication without division was proposed to implement modular multiplication for NIST primes. Compared with a traditional algorithm, the computation complexity was reduced from 16 base multiplications to 7 in 4-way Toom-Cook multiplication. Moreover, we introduced non-least-positive (NLP) form into our design, so that the carry chain in the large array accumulation was broken down, which greatly shortened the critical path and made parallel processing possible. In order to support NLP form and lazy reduction strategy, conventional fast reduction methods for NIST primes were also modified. In addition, pipeline technique at the level of point multiplication was used, so the latency of modular inverse can be covered. Implemented on the Xilinx Virtex-6 FPGA platform, the ECC processor can perform a point multiplication every 54 μs at the cost of 30.3k LUTs and 48 DSPs. Synthesized with 180nm CMOS technology, the speed achieves 43.7 μs with 466k gate counts. These experimental results show a significantly better performance per area than previous works.
Modified Dual-CLCG Method and its VLSI Architecture for Pseudorandom Bit Generation

Pseudorandom bit generator (PRBG) is an essential component for securing data during transmission and storage in various cryptography applications. Among popular existing PRBG methods such as linear feedback shift register (LFSR), linear congruential generator (LCG), coupled LCG (CLCG), and dual-coupled LCG (dual-CLCG), the latter proves to be more secure. This method relies on the inequality comparisons that lead to generating pseudorandom bit at a non-uniform time interval. Hence, a new architecture of the existing dualCLCG method is developed that generates pseudo-random bit at uniform clock rate. However, this architecture experiences several drawbacks such as excessive memory usage and high-initial clock latency, and fails to achieve the maximum length sequence. Therefore, a new PRBG method called as “modified dual-CLCG” and its very large-scale integration (VLSI) architecture are proposed in this paper to mitigate the aforesaid problems. The novel contribution of the proposed PRBG method is to generate pseudorandom bit at uniform clock rate with one initial clock delay and minimum hardware complexity. Moreover, the proposed PRBG method passes all the 15 benchmark tests of NIST standard and achieves the maximal period of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> . The proposed architecture is implemented using Verilog-HDL and prototyped on the commercially available FPGA device.
A Multiview and Multiexemplar Fuzzy Clustering Approach: Theoretical Analysis and Experimental Studies

Multiview and multiexemplar fuzzy clustering aims at effectively integrating the fuzzy membership matrix of each individual view to search for a final partition of objects in which each cluster may well be represented by one and even multiple exemplars. However, how to integrate the corresponding fuzzy membership matrix of each view such that enhanced clustering performance can be theoretically guaranteed still keeps an open topic. In this study, with the proposed exemplar invariant assumption that an exemplar of a cluster in one view is always an exemplar of that cluster in each other view, we demonstrate that multiview & multiexemplar fuzzy clustering has a theoretical guarantee of enhanced clustering performance. Based on the above-mentioned theoretical result, we develop a novel multiview & multiexemplar fuzzy clustering approach (M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FC). The key features of the proposed approach are: first, embed a quadratic penalization term into its objective function to minimize the discrepancy of exemplars across different views such that the exemplar invariant assumption can be met as much as possible; and second, optimize the proposed objective function of the proposed approach by applying the Lagrangian multiplier method and Karush-Kuhn-Tuchker conditions to assure nonnegative fuzzy memberships. Extensive experimental results show that M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FC outperforms the existing state-of-the-art multiview approaches in most cases.
A 3D virtual environment for training soccer referees

Emerging digital technologies are being used in many ways by and in particular virtual environments provide new opportunities to gain experience on real-world phenomena without having to live the actual real-world experiences. In this study, a quantitative research approach supported by expert validation interviews was conducted to determine the availability of virtual environments in the training of soccer referees. The aim is to design a virtual environment for training purposes, representing a real-life soccer stadium to allow the referees to manage matches in an atmosphere similar to the real stadium atmosphere. At this point, the referees have a chance to reduce the number of errors that they make in real life by experiencing difficult decisions that they encounter during the actual match via using the virtual stadium. In addition, the decisions and reactions of the referees during the virtual match were observed with the number of different fans in the virtual stadium to understand whether the virtual stadium created a real stadium atmosphere for the referees. For this evaluation, Presence Questionnaire (PQ) and Immersive Tendencies Questionnaire (ITQ) were applied to the referees to measure their involvement levels. In addition, a semi-structure interview technique was utilized in order to understand participants’ opinions about the system. These interviews show that the referees have a positive attitude towards the system since they can experience the events occurred in the match as a first person instead of watching them from camera as a third person. The findings of current study suggest that virtual environments can be used as a training tool to increase the experience levels of the soccer referees since they have an opportunity to decide about the positions without facing the real-world risks.