Reviewerly/exHarmony huggingface dataset:
Linear complexity and 2-adic complexity of binary interleaved sequences with optimal autocorrelation magnitude

&lt;abstract&gt;&lt;p&gt;A construction of binary sequences with period $ 4N $ and optimal autocorrelation magnitude has been investigated based on sampling and interleaving technique. We determine the exact value of the linear complexity of the constructed sequences according to the deep relationship among the characteristic polynomials, and show it is $ 2N+2 $. Moreover, we determine the 2-adic complexity of these sequences by the autocorrelation function, and show it can attain the maximum value. Results show that such sequences can resist both the Berlekamp-Massey attack and the Rational Approximation Algorithm, in addition are good for communication.&lt;/p&gt;&lt;/abstract&gt;
Efficient and Tight Oblivious Transfer from PKE with Tight Multi-user Security

We propose an efficient oblivious transfer in the random oracle model based on public key encryption with pseudorandom public keys. The construction is as efficient as the state of art though it has a significant advantage. It has a tight security reduction to the multi-user security of the underlying public key encryption. In previous constructions, the security reduction has a multiplicative loss that amounts in at least the amount of adversarial random oracle queries. When considering this loss for a secure parameter choice, the underlying public key encryption or elliptic curve would require a significantly higher security level which would decrease the overall efficiency. Our OT construction can be instantiated from a wide range of assumptions such as DDH, LWE, or codes based assumptions as well as many public key encryption schemes such as the NIST PQC finalists. Since tight multi-user security is a very natural requirement which many public key encryption schemes suffice, many public key encryption schemes can be straightforwardly plugged in our construction without the need of reevaluating or adapting any parameter choices.
Research on Side-Channel Attack Method Based on LSTM

Side channel attacks recover keys or other sensitive information by using the time, power consumption, electromagnetic radiation and fault output generated when cryptographic algorithms are executed on IOT devices. It has become one of the important threats to cryptographic security devices. In recent years, deep learning technology is widely used in the field of Side Channel Attack (SCA). In this paper, a side channel attack method based on deep learning Long Short-Term Memory (LSTM) is proposed. Because of the interpretability of the internal nonlinear structure of LSTM, its system identification capability and computational complexity are superior to those of deep learning network. To test the effectiveness of the modified attack method, we evaluated it using a public ASCAD dataset. We conducted a side-channel analysis experiment for the AES-128 encryption algorithm. Experimental results show that this method achieves good attack effect and effectively alleviates the influence caused by trajectory misalignment. The interest points of the side channel power data are determined by using the correlation power analysis method, and the appropriate interest intervals are selected as feature vectors to build the neural network model. The experimental results show that compared with MLP and CNN models, LSTM network model has higher attack efficiency in side channel attack.
Transformer for Multiple Object Tracking: Exploring Locality to Vision

Multi-object tracking (MOT) is a critical task in various domains, such as traffic analysis, surveillance, and autonomous vehicles. The joint-detection-and-tracking paradigm has been extensively researched, which appears to be faster, more convenient for training and deploying over the classic tracking-by-detection paradigm while achieving state-of-the-art performance. In this paper, we research the possibilities of enhancing the MOT system by leveraging the prevailing convolutional neural network (CNN) and a novel vision transformer technique -- Locality. There are several deficiencies in the transformer when it is adopted for computer vision tasks. While the transformers are good at modeling global information for a long embedding, the locality mechanism which learns the local features is missing. This could lead to negligence of small objects, which may arise security issues. We combine the TransTrack MOT system with the locality mechanism inspired by LocalViT and find that the locality-enhanced system outperforms the baseline TransTrack by 5.3% MOTA on the MOT17 dataset.
A Novel Enhancement of Vigenere Cipher Using Message Digest

With the growing use of information technology, it becomes quite essential to protect the information in communication through unsecured networks and in storage. Cryptography provides different encryption methods to protect the information from unauthorized users. In this paper, we propose an encryption scheme by using the Vigenere cipher along with a novel method of key generation. This scheme uses message digest MD5 and ensures confidentiality along with data integrity.
An Empirical Study of Vision Transformers for Cervical Precancer Detection

Cervical precancer is a direct precursor to invasive cervical cancer and a prime target for ablative therapy. This paper presents an empirical study of Vision Transformers (ViT) for cervical precancer classification, an extended study of our previous work using data derived from two studies conducted by the U.S. National Cancer Institute. In this study, we show that ViT can significantly outperform the current state-of-art methods. We also examine data augmentation techniques that help reduce noise that can interfere in precancer detection, such as specular reflection. We achieve 84% accuracy on the test set outperforming the existing works based on the same dataset. Apart from the performance gains, we observe the learned features focus on cervical regions of anatomical significance. Through these experiments, we demonstrate that ViT attains excellent results compared to the current state-of-the-art methods in classifying cervical images for cervical precancer screening.
Self-organized and Self-sustainable Autoscaling for Scientific Workflow Application in Cloud Environment

Cloud computing paradigm promotes dynamic resource delivery as a pay-as-you-go basis for providing utility computing services. The elasticity, capacity to obtain dynamically, and release computer resources in reaction to demand, is a primary characteristic of the cloud. Also, the resources at data centers consume enormous amounts of electricity, which incur very high operating costs, leaving big carbon footprints as a consequence. The high energy consumption is not because of the number of computer resources in use, but also the inadequate use of these resources. Cloud Resource Providers (RPs) are also experiencing under- and over-provisioning of resources due to the unpredictability of job arrival. The cloud solves the problem of under-provisioning resources for running services, which results in the loss of prospective consumers during peak hours. Even over-provisioning of resources burns capital. The key to effective cloud implementation is the determination of the optimum resource pool size for reservation. This problem can be addressed by the autoscaling function of the cloud for efficient resource utilization. A huge quantity of energy is used by the server in the cloud data center for fulfilling the request; high operational costs and resource scaling delays are the primary roadblocks to successful resource scalability. To adopt the dynamics, uncertainties, Quality of Service (QoS) interference, and compromise goals presented in the cloud, there is a need for a mathematical model with the benefits of self-organization and self-sustainable autoscaling with associated algorithms. These issues are tackled here by a well-known ecological model based on predator–prey interaction. This research proposes to resolves the issues of resource provisioning and distribution by employing an autoscaling procedure based on aforementioned the predator–prey model. Furthermore, energy-related issues are addressed at the data center level through resource management, which employs ecological principles such as allometric scaling law and metabolic rate. To resolve the issues of resource scaling delay, a holonic multiagent system approach is proposed.
Local Search in Selected Crossover Operators

The purpose of the paper is to analyze an incorporation of local search mechanisms into five crossover operators (KPoint, AEX, HGreX, HProX and HRndX) used in genetic algorithms, compare the results depending on various parameters and draw the conclusions. The local search is used randomly with some probability instead of the standard crossover procedure in order to generate a new individual. We analyze injecting the local search in two situations: to resolve the conflicts and also without a conflict with a certain probability. The discussed mechanisms improve the obtained results and significantly accelerate the calculations. Moreover, we show that there exists an optimal degree of the local search component, and it depends on the particular crossover operator.
Real-Time Alignment for Connectomics

In Connectomics, researchers are creating the brain's wiring diagram at nanometer resolution. As part of this processing workflow, 2D electron microscopy (EM) images must be aligned to 3D volumes. However, existing alignment methods are computationally expensive and can take a long time. We hypothesize that adding biological features improve and accelerate the alignment procedure. Since especially mitochondria can be detected accurately and fast, we propose a new alignment method, MITO, that uses these structures as landmark points. With MITO, we can decrease the alignment time by 27%, and our experiments indicate a throughput of 33 Megapixels/s, which is faster than the acquisition speed of current microscopes. We can align an image volume of $$1268\times 1524\times 160$$ voxels in less than 12 s. We compare our method to the following feature generators: ORB, BRISK, FAST, and FREAK.
Asset Sensitivity for Aligning Risk Assessment Across Multiple Units in Complex Organizations

A cyber-risk assessment conducted in a large organization may lead to heterogeneous results due to the subjectivity of certain aspects of the evaluation, especially those concerning the negative consequences (impact) of a cyber-incident. To address this problem, we propose an approach based on the identification of a set of sensitivity features, i.e. certain attributes of the assets or processing activities that are strongly related to the levels of impact of cyber-incidents. We apply our approach to revise the results of a Data Protection Impact Assessment, a mandatory activity for complying with GDPR, conducted in a medium-to-large organization of the Italian Public Administration, and we obtain encouraging results.
Deep Variational Method with Attention for High-Definition Face Generation

We present a method based on four different neural network architectures to generate realistic high-resolution faces. Our model consists of four modules: a convolutional Variational Auto-Encoder (VAE), a convolutional pix2pix network, a super-resolution transformer, and a cross-scaling module. Our work combines a variational model with an attention model based on transformers to improve the quality of generated high-definition images that looks realistic. Our method’s performance is demonstrated by experiments with a High-Quality Faces dataset.
A Text Multi-label Classification Scheme Based on Resampling and Ensemble Learning

The medical dispute cases are professional and closely related to medicine. Therefore, the mediation of cases in practice depends heavily on similar historical cases. Multi-label classification of legal documents can efficiently filter irrelevant historical cases, which helps to recommend the similar historical cases faster and better. However, the imbalance and label symbiosis of the data set directly affect multi-label classification of legal documents. Therefore, a multi-label classification scheme based on resampling and ensemble learning is presented in this paper The scheme includes two parts: in the first part, in order to reduce the impact of label symbiosis on resampling, a resampling algorithm based on the average sparsity of the label set is proposed improve the imbalance of the data set; in the second one, a multi-label classification algorithm based on ensemble learning is proposed to train multiple base classifiers and combine each base classifier with a voting strategy of one vote. It can effectively improve the effect of multi-label classification. The experimental results show that the scheme proposed in this paper can improve the effect of multi-label classification and is not only suitable for legal documents but also applicable for other text data sets with imbalanced classes and label symbiosis problems.
Ergonomic Evaluation of the Touch Screen in the Cockpit Under Stationary and Vibration Conditions

Compared with the traditional input mode, the touch screen has the advantages of convenient operation and saving cockpit space. As the touch screen began to appear in the aircraft cockpit, the impact of turbulence on touch screen operation has become a major challenge for touch screen application. In order to explore the influence of turbulence on touch screen operation, we designed touch screen input experiments, simulated the cockpit vibration and static environment, and studied the touch screen input mode. We compared the effects of different button sizes and button spacing on completing the input task. The results show that appropriate button layout is helpful to reduce the adverse effect of vibration on touch screen operation, and the reason is studied combined with the analysis of eye movement data.
Shrinking the Kernel Attack Surface Through Static and Dynamic Syscall Limitation

Linux Seccomp is widely used by the program developers and the system maintainers to secure the operating systems, which can block unused syscalls for different applications and containers to shrink the attack surface of the operating systems. However, it is difficult to configure the whitelist of a container or application without the help of program developers. Docker containers block about only 50 syscalls by default, and lots of unblocked useless syscalls introduce a big kernel attack surface. To obtain the dependent syscalls, dynamic tracking is a straight-forward approach but it cannot get the full syscall list. Static analysis can construct an over-approximated syscall list, but the list contains many false positives. In this paper, a systematic dependent syscall analysis approach, sysverify, is proposed by combining static analysis and dynamic verification together to shrink the kernel attack surface. The semantic gap between the binary executables and syscalls is bridged by analyzing the binary and the source code, which builds the mapping between the library APIs and syscalls systematically. To further reduce the attack surface at best effort, we propose a dynamic verification approach to intercept and analyze the security of the invocations of indirect-call-related or rarely invoked syscalls with low overhead.
Vibrating Particles System Algorithm for Solving Classification Problems

Big data is a term that refers to a set of data that, due to its largeness or complexity, cannot be stored or processed with one of the usual tools or applications for data management, and it has become a prominent word in recent years for the massive development of technology.Almost immediately thereafter, the term "big data mining" emerged, i.e., mining from big data even as an emerging and interconnected field of research.Classification is an important stage in data mining since it helps people make better decisions in a variety of situations, including scientific endeavors, biomedical research, and industrial applications.The probabilistic neural network (PNN) is a commonly used and successful method for handling classification and pattern recognition issues.In this study, the authors proposed to combine the probabilistic neural network (PPN), which is one of the data mining techniques, with the vibrating particles system (VPS), which is one of the metaheuristic algorithms named "VPS-PNN", to solve classification problems more effectively.The data set is eleven common benchmark medical datasets from the machine-learning library, the suggested method was tested.The suggested VPS-PNN mechanism outperforms the PNN, biogeography-based optimization, enhanced-water cycle algorithm (E-WCA) and the firefly algorithm (FA) in terms of convergence speed and classification accuracy.
Region Separable Stereo Matching

Convolutional neural networks (CNNs) have shown attractive performance for stereo matching. However, spatially shared convolution weights of CNN-based methods usually face a dilemma that the convolution weights suitable for aggregating contextual information in smooth regions often blur local matching details of textured regions and vice versa. This paper tries to find a way out of the dilemma via a novel region separable stereo matching (RSSM) method, which is universally applicable to CNN stereo models based on 4D cost volumes and can greatly improve the accuracy and efficiency of existing models. The key idea of our method is to automatically group image pixels into regions according to the gradients, and then construct and process the respective cost volume of each region separately. To perform cost aggregation, we propose a two-stage network consisted of regional grouping aggregation (RGA) and regional fusion aggregation (RFA). In RGA, convolutions are grouped in channel-wise, and each group of convolutions learn dedicated weights for the corresponding region via regional supervision. Through RGA, each group of convolutions can extract the most representative features from the corresponding region. In RFA, we combine matching clues of all convolution groups from RGA to output the final prediction map. We further extend the idea of regional grouping to feature extraction and modify the skip connection in aggregation networks to better adapt our method to stereo matching models. Experimental results on five public datasets show that our method can significantly improve several state-of-the-art 3D CNN based stereo models.
Improve Chinese Spelling Check by Reevaluation

Chinese Spelling Check (CSC) aims to detect and correct the spelling errors in Chinese. Most Chinese spelling errors are misused semantically, phonetically or graphically similar characters. Previous state-of-the-art works on the CSC task pursue transitions from misspelled sentences to correct sentences directly. However, the spelling errors, especially the continuous incorrect characters, usually confuse the meaning of the semantic context. It is difficult to make correct modifications for CSC models based on the error contextual information. To address this issue, we propose a simple but effective pipeline for CSC by searching the most appropriate candidate sentences as the original correct sentence. Specifically, candidate sentences are generated based on possible error characters with the confusion set. Then we reevaluate the candidate sentences to find the best in terms of character probabilities and similarity compared to the original error characters. Besides, we extend the widely used confusion set (The code and data are available at https://github.com/zuoyecihua/CSC. ). Simply applying the confusion set as a filter will bring large performance improvement. The experimental results show that our approach outperforms previous methods and performs well on bi-gram errors.
Digital Chain of Custody for CBRNE Events: Custody Transfer Governance

The specification of standardised processes with information related to the custody of digital evidence related to CBRNE events is a crucial issue today. The integrity of the evidence must be maintained, from its initial discovery to the subsequent submission to the court. This means it is essential to follow standardised procedures to ensure data quality while preserving the chain of custody. Each link (or step) in the chain (or process) is essential to ensure the legitimacy, integrity, and inspection of the collected CBRNE samples. The paper stipulates that each piece of evidence should be considered from the perspective of source, creator, time, location, and reason to guide the digital evidence review process. This means that as long as one of the intervenient actors keeps the digital evidence, a digital chain of custody (dCoC) will be established. The guidelines provided for the dCoC workflow are designed to establish basic practices that participants in forensic operations should follow as part of standard operating procedures for effective and reliable audits of custody transfers.
Predicting Solar PV Generation Using Weather Station Data

Electric power generated from Solar Photovoltaic (PV) panels is estimated to have increased globally by 22% in 2019, to 720 TWh [5]. It is now considered the third-largest renewable energy technology after wind and hydro powers. The primary reason for this growth is the need to utilize free energy resources that are also environmentally clean. PV-generated power, however, is uncertain and varies from time to time and season to season. Dealing with this uncertainty requires having predictive and forecasting models that accurately estimate generated power from historical data. This paper reports on an in-progress research project that explores weather-related variables such as humidity, temperature, and wind speed and uses them to predict and forecast generated power using a dataset collected over three years by a weather station at Southeast New Mexico College in Carlsbad, New Mexico. Various predictive and forecasting models are built, trained, and evaluated. The goal is to explore these variables and report on what makes a good predictive model and how such a model behaves over time.
Automatic Liver Tumor Segmentation in CT Modalities Using MAT-ACM

In the recent days, the segmentation of Liver Tumor (LT) has been demanding and challenging.The process of segmenting the liver and accurately spotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image.The intensity similarities of the neighboring organs of the liver create difficulties during liver segmentation.The manual segmentation does not provide an accurate segmentation because the results provided by different medical experts can vary.Also, this manual technique requires a large number of image slices and time for segmentation.To solve these issues, the Fully Automatic Segmentation (FAS) technique is proposed.In this proposed Multi-Angle Texture Active Contour Model (MAT-ACM) method, the input Computed Tomography (CT) image is preprocessed by Contrast Enhancement (CE) with Non-Linear Mapping Technique (NLMT), in which the liver is differentiated from its neighbouring soft tissues with related strength.Then, the filtered images are given as the input to Adaptive Edge Modeling (AEM) with Canny Edge Detection (CED) technique, which segments the Liver Region (LR) from the given CT images.An AEM with a CED model is implemented, which increases the convergence speed of the iterative process for decreasing the Volumetric Overlap Error (VOE) is 6.92% rates when compared with the traditional Segmentation Techniques (ST).Finally, the Liver Tumor Segmentation (LTS) is developed by applying the MAT-ACM, which accurately segments the LR from the segmented LRs.The evaluation of the proposed method is compared with the existing LTS methods using various performance measures to prove the superiority of the proposed MAT-ACM method.