Reviewerly/exHarmony huggingface dataset:
LZRR: LZ77 parsing with right reference

Lossless data compression has been widely studied in computer science. One of the most widely used lossless data compressions is Lempel-Ziv (LZ) 77 parsing, which achieves a high compression ratio. Bidirectional (a.k.a. macro) parsing is a lossless data compression technique that computes a sequence of phrases copied from another substring (target phrase) on either the left or the right position in an input string. Navarro et al. recently showed that a large gap may exist between the size of the smallest bidirectional parse of a given string and that of LZ77 parsing. In addition, finding the smallest bidirectional parse of a given text is NP-complete. Several variants of bidirectional parsing algorithm have been proposed thus far, but no prior work for bidirectional parsing algorithm has achieved compression that is at least as good as LZ77 parsing for any string. In this paper, we present the first practical bidirectional parsing algorithm named LZ77 parsing with right reference (LZRR), in which the number of LZRR phrases is theoretically guaranteed to be not larger than the number of LZ77 phrases. Experimental results using benchmark strings show that the number of LZRR phrases is approximately six percent smaller than that of LZ77 phrases.
Multi-surface Interferometric Algorithm and Error Analysis with Adaptive Phase Shift Matching

To simultaneously measure topographies of both surfaces of a transparent plate through wavelength-tuning interferometry at arbitrary cavity lengths, a flexible phase shift matching algorithm (FPSMA) is proposed. The FPSMA includes two parts: 1) taking the 4-term 3-order Nuttall window as an example, a phase demodulation approach is designed and analyzed for extracting the harmonic phases of the front surface, rear surface, and thickness variation of the measured plate; 2) based on the residual error analysis of the developed 5N-Nuttall algorithm for different cavity length coefficient M and the phase-shifting parameter N, a parameter-change technique that allows the developed phase demodulation approach to be applied to arbitrary cavity lengths by adaptively changing the phase shifts and frames of the captured interferograms. By this technique, the algorithm failures due to the spectral aliasing of harmonics can be avoided effectively and conveniently, then the correct phase demodulation can be ensured. Besides, using the developed method, when the values of N or M are chosen in advance, the corresponding available measurement ranges can be given. Besides, the relationship between the window length and the measurement limitation of the measurable thinnest optical thickness is investigated. Considering the common linear and nonlinear errors in the wavelength-shifting process, the error analysis of FPSMA is performed and discussed for situations close to reality. The comparative studies and the experimental results of two transparent parallel plates also support the performance of the developed FPSMA.
FPGA Implementations of Espresso Stream Cipher

Security of resource-constrained hardware devices, such as the devices in Internet of Things regime, must consider performance and area consumption metrics. One of the viable options for designing lightweight cryptography could be stream cipher. The stream ciphers are symmetric ciphers designed for resource-limited devices. In this paper, we implemented the Espresso stream cipher on the Xilinx Spartan-7 FPGA device. During our implementation, we considered four cases: basic implementation with a full-width input, serial implementation with an unpipelined (basic) algorithm, serial implementation with a pipelined algorithm, and parallel cases. According to the results received, the parallel version reached a throughput of 1778 Mbps and consumed 113 slices. The serial version with an unpipelined output had the least area consumption of 68 slices to achieve a throughput of 511 Mbps. Results of implemented design are then compared with other stream cipher implementations, namely, Grain, Trivium, and MICKEY. It is identified that, despite limited parallelization, the Espresso stream cipher has one of the lowest area consumption compared to other stream cipher implementations.
Knowledge Management Framework for Cloud Federation

Abstract A cloud federation (CF) is an alliance of cloud service providers (CSPs) working together to overcome scalability and portability barriers. However, there are some business challenges (e.g., lack of trust, lack of schemes for revenue sharing, and lack of schemes for resource sharing) and technological challenges (e.g., missing schemes for resource provisioning, lack of coordinated resource management, and little load balancing), causing instability in CFs. As CF alliances pursue strategic goals, they require intensive knowledge sharing. In fact, practitioners have confirmed a positive impact of knowledge management on stability and success of strategic alliances (SA). According to the literature, SAs may also face learning issues such as non–controlled information revelation or unbalanced dissemination of core competencies. These findings pose challenges about the nature of the knowledge and how to share it within a CF. Nonetheless, there is only scarce literature on KM in CF. Thus, the purpose of the paper is to propose a KM framework for CFs with the aim of strengthening stability and potential CF commercialization.
Blockchain and Smart Contracts to Improve Dental Healthcare for Children in Primary School

Privacy preserving is a matter of great importance in many sensitive fields including financial and healthcare areas. As the blockchain technology has achieved an obvious success in privacy protection at the financial level when the Bitcoin has been introduced in 2008, there are considerable attempts and later on successful projects to introduce the blockchain technology to the healthcare fields, where the patient privacy and access control to the personal information is at high priority. The aim of our paper mainly considers the introduction of the smart contracts that are based on the blockchain technology as a method to preserve the patient privacy and improve the reality of dental healthcare providing services.The main focus will be on the treatment plan of the patient by building a smart contract system between multiple partners. By using “the five types models” our proposed system has been built combining the advantages of blockchain technology and the benefits of smart contracts. The programming language that we have used is Solidity along with proof of authority algorithm on Ethereum blockchain. The proposed smart contract system which is based on blockchain technology can provide the privacy preservation of patient information in addition to the digitalization of healthcare data.
Teaching New Dogs Old Tricks

This chapter discusses how the basics of espionage have remained the same, even in the digital age. The pendulum of espionage--and protection from it--has swung wide over the past century. Different public and private sectors have renewed focus on not only cyber protections, but on increased physical protection of critical assets and ensuring trusted personnel in the workforce. Within this chapter, the authors review the basics of protecting critical assets to ensure that changes in espionage can be mitigated at an early stage. While the techniques of espionage have many variables, especially in a digital age, the authors have established that the use of a risk assessment that focuses on identifying the threats, the specific variables or methods of espionage, and developing and implementing mitigation measures is of the utmost importance.
Research on Equipment Maintenance Guidance Technology Based on MR and Digital Twin

Aiming at the problem that large-scale and complex equipment cannot be repaired without professional guidance, which caused by the complex structure, difficult maintenance operations, limited training conditions for maintenance personnel, a research technology for equipment maintenance guidance based on MR and digital twin technology is proposed. With the support of Mixed Reality (MR) technology and Digital Twin (DT) technology, the basic system framework of auxiliary maintenance based on the two is studied. This paper proposes a research technology for equipment maintenance guidance based on MR and DT technology. In the maintenance process, MR technology is used to solve the visualization problem, and the digital twin system could be used to solve the problem of resource allocation and work step arrangement.
Sharing PHR Data in Cloud Using Sigmoid Key and Median Support Signature-Based Cryptosystem

In recent years it is a major and developing challenge to provide the security of the data that have been stored in personal health records (PHR). Some encryption and key generation techniques in classical works have been created for this purpose. However, it has disadvantages such as ineffective and decreased security rules in access control regulations. To improve the security performance of PHR data for Sigmoid-based signature cryptosystem is proposed to transfer the data stored in a third-party cloud server securely. In the proposed work, the PHR data is taken as an input and the key encrypting key algorithm is implemented for the attribute key generation. After the attribute, the key generation sigmoid is calculated for the PHR data to keep the key secured. To keep the personal data of the patient in a secured manner the median support-based signature is generated and without displaying the generated signature the cipher text is displayed. To gain access to information stored in the cloud, the users have to verify the both generated private key and signature. On the successful verification of both the private key and signature, later, if the data requested by the user is a valid user then the user will be getting permission to access the cloud. The experimental findings show that our proposed technique outperforms other current methods in key computation time, communication complexity, encryption time, decryption time, and cloud operating mechanism.
Optimizing Federated Learning on Non-IID Data Using Local Shapley Value

Federated learning (FL) was originally proposed as a new distributed machine learning paradigm that addresses the data security and privacy protection issues with a global model trained by ubiquitous local data. Currently, FL techniques have been applied in some data-sensitive areas such as finance, insurance, and healthcare. Although FL has broad application scenarios, there are still some significant and fundamental challenges, one of which is the training on Not independently and identically distributed (Non-IID) data. More concretely, the global model aggregation and collaboration of a massive number of participants on the Non-IID data remain an unsolved problem. We find that most of the model aggregation optimization algorithms in the literature suffer from significant accuracy loss in the Non-IID setting for FL. To this end, in this paper, we propose a novel model aggregation algorithm terms FedSV, which dynamically updates global model aggregation weights according to each local participant’s contribution in each training round. Furthermore, to evaluate the participants’ contribution, we propose a quantization algorithm based on Local Federated Shapley Value, which dynamically computes the contribution by the properties of the participant. Extensive experiments on Non-IID data partition, such as CIFAR-10 and MNIST, demonstrate that our approach can improve accuracy during training compared with existing methods.
Multi-Sensor Data Fusion for Smart Home Reliable Pedestrian Localization

Today's elderly face physical problems that reduce their independence to perform daily activities. The integration of environmental and body sensors in smart homes is a pertinent solution to provide a better quality of life in safer conditions. People localization and tracking in indoor environments are among the main services to be developed to follow them at home and allow them to assess their physical states. In this paper, we propose to localize and track the center of pressure of people (one or more) in a smart home using a load sensing floor and wearable sensors. Data fusion is performed using an informational filter where an inverted pendulum biomechanical model is introduced. The results obtained are very promising and have been validated using a motion tracking system and force plates.
A Lightweight Multi-scale Feature Fusion Network for Real-Time Semantic Segmentation

Recently, semantic segmentation has become an emerging research area in computer vision due to a strong demand for autonomous vehicles, robotics, video surveillance, and medical image processing. To address this demand, several real-time semantic segmentation models have been introduced. Relying on existing Deep Convolution Neural networks (DCNNs), these models extract contextual features from the input image and construct the output at the decoder end by simply fusing deep features with shallow features which causes a large semantic gap. However, this large gap causes boundary degeneration and noisy feature effects in the output. To address this issue, we propose a novel architecture, called Feature Scaling Feature Fusion Network (FSFFNet) which alleviates the gap by successively fusing features at consecutive levels in multiple directions. For better dense pixel-level representation, we also employ a feature scaling technique which helps the model assimilate more contextual information from the global features and improves model performance. Our proposed model achieves 71.8% validation accuracy (mIoU) on the Cityscapes dataset whilst having only 1.3M parameters.
Sensitivity-Informed Provable Pruning of Neural Networks

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 2 December 2020Accepted: 21 September 2021Published online: 13 January 2022Keywordscompression, pruning, generalizationAMS Subject Headings68Publication DataISSN (online): 2577-0187Publisher: Society for Industrial and Applied MathematicsCODEN: sjmdaq
Application of Sub-Sample Dithering to Reduce Probability of Signal Detection

Communications Security is Achieved in the Physical Layer with the utilization of Low Probability of Detection (LPD) and Low Probability of Intercept (LPI) Modulation Techniques. Standard modulation techniques have cyclical time varying properties that can be exploited by signals intelligence (SIGINT) for signal detection, traffic analysis, and signal interception. There is a large body of analysis on wide-sense cyclostationary features of these modulation techniques with the conclusion that the primary cause of periodic cyclostationary features is the modulation symbol rate. Deterministic variability in the symbol rate can be used to reduce or eliminate cyclostationary features. Several techniques have been proposed, including DAC clock dithering. In this paper we provide a description of a polyphase resampler application to provide symbol rate dithering and an analysis of the dithering on the second, and higher order cyclostationary features. Multiple modulation types are included in the analysis. ADC de-dithering is typically not an option at the receiver - the delays inherent in standard receiver architectures prohibit real-time control of the ADC during signal demodulation while the polyphase resampler can be applied digitally within the receiver at matched filter sample rates.
Cyber Vulnerability Implantation Revisited

In this paper we revisit a study presented at MILCOM 2014. Our goal then was to determine the utility of implanting a vulnerability into a cybersecurity software protocol to an actor planning to execute an offensive cyber operation. Based on a case study describing the then recently discovered Heartbleed bug as an offensive cyber operation, a model was devised to estimate the adoption rate of an implanted flaw in OpenSSL. Using the adoption rate of the cryptographic protocol Transport Layer Security version 1.2 as a proxy, we predicted that the global adoption of the vulnerability of at least 50&#x0025; would take approximately three years, while surpassing 75&#x0025; adoption would take four years. Compared to subsequently collected real-world data, these forecasts turned out to be surprisingly accurate. An evaluation of our proposed model shows that it yields results with a root-mean-square error of only 1.2&#x0025; over the forecasting period. Thus, it has a significant degree of predictive power. Although the model may not be generalizable to describe the adoption of any software protocol, the finding helps validate our previously drawn conclusion that exploiting implanted cyber vulnerabilities, in a scenario like the one presented, requires a planning horizon of multiple years. However, as society becomes further dependent on the cyber domain, the utility of intentional vulnerability implantation is likely an exercise in diminishing returns. For a defender, however, our model development process could be useful to forecast the time required for flawed protocols to be phased out.
A Multi-Objective Optimization-based Clustering Approach for CORD-19 Scholarly Articles

The pandemic disease COVID-19, originated from the SARS-CoV-2 virus has spread globally. Researchers are working tirelessly on areas including studying the transmission of COVID-19, promoting its identification, designing new vaccines and therapies, and recognizing its socio-economic consequences. This extensive research leads to the exploration of thousands of scientific papers related to biology, chemistry, genetics, health, and economy. Therefore, it is essential to develop an intelligent text mining technique for segregating this rich source of data to perform easy access, information retrieval, and interpretation within minimum time and resources. We propose a multi-objective optimization-based document clustering approach for the CORD-19 (COVID-19 Open Research Dataset) dataset in this paper. Here, a new technique utilizing BioBERT has been proposed, which benefits from the abstract and the document text, rather than only the brief abstract, to perceive a concise understanding of the text to generate clusters with better definitions. The main contributions of the proposed work are two-fold: in the first step, we have used BioBERT to generate the sentence embedding which is further used for the document representation. In the next step, we have developed a multi-objective optimization (MOO) based clustering algorithm for grouping the generated document vector representations. In this MOO-based clustering, we have used Non-dominated Sorting Genetic Algorithm-II and Fuzzy c-means algorithm as the underlying MOO and clustering technique, respectively. This model is evaluated using the Silhouette Score (Silhouette score) and Calinski-Harabasz index (CH index), and the clustering solutions are visualized using word clouds. The clustering results exhibit significant improvements over various other existing clustering models.
Fuzzy Information Granulation Methodology for Identification of Class-Conscious Speech

In recent years, great advances have been made in NLP for the accuracy and coverage of given word, appearing in a certain context. However, there are some problems, with processing of the meaning representation in automatic word sense disambiguation systems, and it becomes incomprehensible for different social classes to understand expressive language. We try to explore and determine the correct words and expressions usage in “Bag of words analysis” and give some recommendations for future successes in interpretation systems. Our aim is to apply the fuzzy information granulation method that can improve these systems without any discussion.
A Scalable, Real-Time Packet Capturing Solution

The evolution of technology and the increasing connectivity between devices lead to an increased risk of cyberattacks. Good protection systems, such as Intrusion Detection System (IDS) and Intrusion Prevention System (IPS), are essential in trying to prevent, detect and counter most of the attacks. However, the increasing creativity and type of attacks raise the need for more resources and processing power for the protection systems which, in turn, requires horizontal scalability to keep up with the massive companies' network infrastructure and with the complexity of attacks. Technologies like machine learning, show promising results and can be of added value in the detection and prevention of attacks in real-time. But good algorithms and tools are not enough. They require reliable and solid datasets to be able to effectively train the protection systems. The development of a good dataset requires horizontal-scalable, robust, modular and fault-tolerance systems, so that the analyses may be done also in real-time. This paper describes an architecture for horizontal-scaling capture architecture, able to collect packets from multiple sources and prepared for real-time analysis. It depends on multiple modular nodes with specific roles to support different algorithms and tools.
A Proactive Data-Parallel Framework for Machine Learning

Data parallel frameworks become essential for training machine learning models. The classic Bulk Synchronous Parallel (BSP) model updates the model parameters through pre-defined synchronization barriers. However, when a worker computes significantly slower than other workers, waiting for the slow worker will lead to excessive waste of computing resources. In this paper, we propose a novel proactive data-parallel (PDP) framework. PDP enables the parameter server to initiate the update of the model parameter. That is, we can perform the update at any time without pre-defined update points. PDP not only initiates the update but also determines when to update. The global decision on the frequency of updates will accelerate the training. We further propose asynchronous PDP to reduce the idle time caused by synchronizing parameter updates. We theoretically prove the convergence property of asynchronous PDP. We implement a distributed PDP framework and evaluate PDP with several popular machine learning algorithms including Multilayer Perceptron, Convolutional Neural Network, K-means, and Gaussian Mixture Model. Our evaluation shows that PDP can achieve up to 20X speedup over the BSP model and scale to large clusters.
Event-Driven Malicious URL Extractor

Cyber-attacks are attacks that are commonly carried out in order to obtain sensitive information or disrupt internet-based services. Recent occurrences, both internationally and locally, have shown an influx of these attacks expanding rapidly through the use of malicious URLs (Uniform Resource Locators). Traditional measures, including such blacklisting malicious URLs, make it extremely difficult to respond to such attacks in a timely and efficient manner. Most existing solutions remain restricted in terms of scalability and proactive user safeguarding in situations when freshly formed URLs are correlated with a recent event, such as Covid-19 related frauds. The proposed solution is presented with the primary aim of addressing traditional system limitations and offering an interface for users to protect themselves by detecting phishing/malicious URLs in real time. In this research, we will examine extracting user-input event-related keywords and leveraging NLP (Natural Language Processing) algorithms to match them with the accompanying URL (Uniform Resource Locator) token data to determine whether the URLs are malicious or benign.