Reviewerly/exHarmony huggingface dataset:
Conception of efficient key-dependent binary diffusion matrix structures for dynamic cryptographic algorithms

The existing static cryptographic algorithms suffer from different performance and security challenges. These limitations are attributed to their fixed structure, where the substitution and diffusion primitives maintain the same values throughout the process. In this paper, we present a new framework of a dynamic binary diffusion matrix with flexible dimensions (n×n). The proposed solution replaces the static diffusion primitives, in existing symmetric and un-keyed cryptographic algorithms, with dynamic primitives. We define six different dynamic and flexible binary diffusion forms, four that are invertible, and two that are non-invertible, and hence, they could be used for different security services. However, using a single diffusion form does not always guarantee the required cryptographic properties. To that effect, we propose a binary multiplication scheme of a dynamic primary matrix and its transposed form, which yields the desired efficiency, and provides good resistance against recent implementation attacks, yet without degrading the system performance. We conduct security and performance analyses to validate the effectiveness of the proposed solution. The results confirm the cryptographic performance in terms of the linear branch number and the number of fixed points. In this context, the best-obtained branch number is 4 for n=8 and 5 for n=12 for the invertible forms, while for the non-invertible ones, the best branch number is 3 for n=8 and 4 for n=12. Moreover, in terms of the number of fixed points, the obtained numbers are very close to 0 for the invertible and non-invertible forms.
Cost-Weighted TF-IDF: A Novel Approach for Measuring Highway Project Similarity Based on Pay Items’ Cost Composition and Term Frequency

State highway agencies (SHAs) often need to cluster or bundle projects in accordance with their scope similarity for various construction management tasks, including historical data-driven time, cost estimating, and project bundling. Conventionally, SHAs categorize similar projects into work types based on subjective judgment about the similarity between major pay items. A few quantitative methods for project similarity determination are found in the literature, but they mostly use one single source of information, either the cost contribution of pay items or the keywords of pay items descriptions, for measuring project similarity. This paper presents the first attempt to integrate multiple information sources for project similarity measurement. This research proposes a novel cost-weighted term frequency-inverse document frequency (CW-TF-IDF) method that incorporates the cost information of pay items into the traditional TF-IDF word embedding method to measure project similarity. The effectiveness of the proposed method in supporting project clustering and bundling was tested using the historical bid data collected from an SHA. The findings showed that the CW-TF-IDF method significantly improves project clustering performance compared to the most recent state-of-the-art method. The CW-TF-IDF method also showed its outperformance in project bundling as it yielded a cosine similarity of over 0.9 for most of the bundled projects in the testing data. This proposed method is expected to help SHAs accurately identify similar projects and eventually improve their project management effectiveness.
Compressed computations using wavelets for hidden Markov models with continuous observations

Compression as an accelerant of computation is increasingly recognized as an important component in engineering fast real-world machine learning methods for big data; c.f., its impact on genome-scale approximate string matching. Previous work showed that compression can accelerate algorithms for Hidden Markov Models (HMM) with discrete observations, both for the classical frequentist HMM algorithms-Forward Filtering, Backward Smoothing and Viterbi-and Gibbs sampling for Bayesian HMM. For Bayesian HMM with continuous-valued observations, compression was shown to greatly accelerate computations for specific types of data. For instance, data from large-scale experiments interrogating structural genetic variation can be assumed to be piece-wise constant with noise, or, equivalently, data generated by HMM with dominant self-transition probabilities. Here we extend the compressive computation approach to the classical frequentist HMM algorithms on continuous-valued observations, providing the first compressive approach for this problem. In a large-scale simulation study, we demonstrate empirically that in many settings compressed HMM algorithms very clearly outperform the classical algorithms with no, or only an insignificant effect, on the computed probabilities and infered state paths of maximal likelihood. This provides an efficient approach to big data computations with HMM. An open-source implementation of the method is available from https://github.com/lucabello/wavelet-hmms.
THE COMPARISON OF THE EFFECTS OF THRESHOLDING METHODS ON SEGMENTATION USING THE MOTH FLAME OPTIMIZATION ALGORITHM

Segmentation is an important preprocessing step that directly affects the success in image processing applications. There are many methods and approaches used for the segmentation process. Thresholding is a frequently used approach among these methods. There are several suggested approaches to thresholding. In this study, six different thresholding approaches were used as the fitness functions using the moth flame algorithm and the results obtained from these approaches were compared. In experimental studies, seven different threshold levels of 10 different images were studied. In comparisons made with three different metrics, it was seen that the Otsu method was generally more successful. It has also been observed that the minimum cross entropy and Renyi entropies can be used as alternatives.
A Novel Neural Network-Based Recommender System for Drug Recommendation

With the advancement of Machine Learning, recommender systems have emerged with the aim of improving the user experience in a world where data and available alternatives are tremendously growing. Employing Natural Language Processing with such systems can provide them with a sense of empowerment, given that most of the users' opinions are reflected through reviews. Artificial Neural Networks, the core of Deep Learning, have sparked a lot of interest in many research fields, owing to the appealing property of learning feature representations out of nowhere. To that end, this paper presents a novel hybrid recommender system that is based on Natural Language Processing and Artificial Neural Networks. The proposed model is evaluated and compared with a similar model, where the advantages of the proposed model are clearly presented. The paper is concluded by highlighting research opportunities that can be done in the future.
Image and Video Style Transfer Based on Transformer

The essence of image style transfer is to generate images that both maintain in the original content image and present the effect with artistic features under the guidance of style images. Deep learning’s quick rise has resulted in even more achievements in image style transfer, an otherwise popular study area. Nevertheless, due to the limitations of Convolutional Neural Networks (CNN), extracting and retaining the input images is problematic. Therefore, image style transfer based on traditional CNN is biased in the representation of content images. To address the above problems, this paper proposes STLTSF (Style Transfer based on Transformer), a transformer-based method that may achieve image style transfer based on the long-range dependencies of the input images. Compared with traditional visual transformers, STLTSF has two different transformer encoders, one for generating domain-specific content and the other for generating styles. We may convert the encoder to a decoding method that can be styled based on content sequences by using numerous layers of transformers. The suggested STLTSF approach outperforms traditional CNN-based methods in both qualitative and quantitative experiments.
Verified Density Compilation for a Probabilistic Programming Language

This paper presents ProbCompCert, a compiler for a subset of the Stan probabilistic programming language (PPL), in which several key compiler passes have been formally verified using the Coq proof assistant. Because of the probabilistic nature of PPLs, bugs in their compilers can be difficult to detect and fix, making verification an interesting possibility. However, proving correctness of PPL compilation requires new techniques because certain transformations performed by compilers for PPLs are quite different from other kinds of languages. This paper describes techniques for verifying such transformations and their application in ProbCompCert. In the course of verifying ProbCompCert, we found an error in the Stan language reference manual related to the semantics and implementation of a key language construct.
COMBINING GREY WOLF OPTIMIZATION AND WHALE OPTIMIZATION ALGORITHM FOR BENCHMARK TEST FUNCTIONS

Many optimization problems have been successfully addressed using metaheuristic approaches. These approaches are frequently able to choose the best answer fast and effectively. Recently, the use of swarm-based optimization algorithms, a kind of metaheuristic approach, has become more common. In this study, a hybrid swarm-based optimization method called WOAGWO is proposed by combining the Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO). This method aims to realize a more effective hybrid algorithm by using the positive aspects of the two algorithms. 23 benchmark test functions were utilized to assess the WOAGWO. By running the proposed approach 30 times, the mean fitness and standard deviation values were computed. These results were compared to WOA, GWO, Ant Lion Optimization algorithm (ALO), Particle Swarm Optimization (PSO), and Improved ALO (IALO) in the literature. The WOAGWO algorithm, when compared to these algorithms in the literature, produced the optimal results in 5 of 7 unimodal benchmark functions, 4 of 6 multimodal benchmark functions, and 9 of 10 fixed-dimension multimodal benchmark functions. Therefore, the suggested approach generally outperforms the findings in the literature. The proposed WOAGWO seems to be promising and it has a wide range of uses.
Two-frame Phase-shifting Interferometry by Minimizing the Residual-background

Abstract Demodulation of two random phase shift fringe patterns is an ill-posed problem. Existing methods often need to remove the fringe background by pre filtering in the frequency, which takes more time, and have a great influence on the phase reconstruction because of the frequency aliasing, these limits the application of two frame phase-shift interferometry in actual production. In this case, this paper based on the idea of Statistical Average, proposes a Statistical Average technology for phase extraction of two frame randomly phase-shifted interferograms from rough to precision without pre filtering. The proposed methods based on minimizing the residual background, firstly estimates the average background by calculating the average for all pixels, and then adding and subtracting the two frames with the background removed. The phase shift and phase are solved by using the properties of trigonometric functions. Then the amplitude is solved using the obtained phase and phase shift. Finally, the background is solved and the original light intensity is updated. Compared with existing methods, our method has the advantages such as rapid convergence and quick calculation by offering the right initial value. The accuracy of my proposed method is approximately double that of existing methods. Simulation and experimental results show that our method is a practical method of two frame phase shift interferences, both simple and reliable.
Garbage-Collection Safety for Region-Based Type-Polymorphic Programs

Region inference offers a mechanism to reduce (and sometimes entirely remove) the need for reference-tracing garbage collection by inferring where to insert allocation and deallocation instructions in a program at compile time. When the mechanism is combined with techniques for reference-tracing garbage collection, which is helpful in general to support programs with very dynamic memory behaviours, it turns out that region-inference is complementary to adding generations to a reference-tracing collector. However, region-inference and the associated region-representation analyses that make such a memory management strategy perform well in practice are complex, both from a theoretical point-of-view and from an implementation point-of-view. In this paper, we demonstrate a soundness problem with existing theoretical developments, which have to do with ensuring that, even for higher-order polymorphic programs, no dangling-pointers appear during a reference-tracing collection. This problem has materialised as a practical soundness problem in a real implementation based on region inference. As a solution, we present a modified, yet simple, region type-system that captures garbage-collection effects, even for polymorphic higher-order code, and outline how region inference and region-representation analyses are adapted to the new type system. The new type system allows for associating simpler region type-schemes with functions, compared to original work, makes it possible to combine region-based memory management with partly tag-free reference-tracing (and generational) garbage-collection, and repairs previously derived work that is based on the erroneous published results.
Beyond Backtracking: Connections in Fine-Grained Concurrent Separation Logic

Concurrent separation logic has been responsible for major advances in the formal verification of fine-grained concurrent algorithms and data structures such as locks, barriers, queues, and reference counters. The key ingredient of the verification of a fine-grained program is an invariant, which relates the physical data representation (on the heap) to a logical representation (in mathematics) and to the state of the threads (using a form of ghost state). An invariant is typically represented as a disjunction of logical states, but this disjunctive nature makes invariants a difficult target for automated verification. Current approaches roughly suffer from two problems. They use backtracking to introduce disjunctions in an uninformed manner, which can lead to unprovable goals if an appropriate case analysis has not been made before choosing the disjunct. Moreover, they eliminate disjunctions too eagerly, which can cause poor efficiency. While disjunctions are no problem for automated provers based on classical (i.e., non-separating) logic, the challenges with disjunctions are prominent in the study of proof automation for intuitionistic logic. We take inspiration from that area—specifically, based on ideas from connection calculus , we design a simple multi-succedent calculus for separation logic with disjunctions featuring a novel concept of a connection . While our calculus is not complete, it has the advantage that it can be extended with features of the state-of-the-art concurrent separation logic Iris (such as modalities, higher-order quantification, ghost state, and invariants), and can be implemented effectively in the Coq proof assistant with little need for backtracking. We evaluate the practicality on 24 challenging benchmarks, 14 of which we can verify fully automatically.
cuCatch: A Debugging Tool for Efficiently Catching Memory Safety Violations in CUDA Applications

CUDA, OpenCL, and OpenACC are the primary means of writing general-purpose software for NVIDIA GPUs, all of which are subject to the same well-documented memory safety vulnerabilities currently plaguing software written in C and C++. One can argue that the GPU execution environment makes software development more error prone. Unlike C and C++, CUDA features multiple, distinct memory spaces to map to the GPU’s unique memory hierarchy, and a typical CUDA program has thousands of concurrently executing threads. Furthermore, the CUDA platform has fewer guardrails than CPU platforms that have been forced to incrementally adjust to a barrage of security attacks. Unfortunately, the peculiarities of the GPU make it difficult to directly port memory safety solutions from the CPU space. This paper presents cuCatch, a new memory safety error detection tool designed specifically for the CUDA programming model. cuCatch combines optimized compiler instrumentation with driver support to implement a novel algorithm for catching spatial and temporal memory safety errors with low performance overheads. Our experimental results on a wide set of GPU applications show that cuCatch incurs a 19% runtime slowdown on average, which is orders of magnitude faster than state-of-the-art debugging tools on GPUs. Moreover, our quantitative evaluation demonstrates cuCatch’s higher error detection coverage compared to prior memory safety tools. The combination of high error detection coverage and low runtime overheads makes cuCatch an ideal candidate for accelerating memory safety debugging for GPU applications.
Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.
Formalization of RRG Syntax

The goal of this chapter is to present RRG as a formalized grammatical theory which puts emphasis on mathematical and logical rigour. It is shown how the syntactic dimension of RRG can be formally expressed as a tree rewriting grammar consisting of elementary tree templates that can be combined with larger syntactic units by three modes of composition: substitution, adjunction and wrapping. Special attention is given to the structure and derivation of extraction constructions and complex sentences.
The Structure and Semantics of Complex Sentences

This chapter offers an in-depth treatment of clause linkage and complex sentences in RRG. First, it discusses and exemplifies each nexus–juncture type, adducing evidence from a wide range of languages. Then, it introduces the notion of syntactic and semantic cohesiveness in clause linkage and makes relevant generalizations and predictions.
Collaborative neurodynamic optimization for solving nonlinear equations

A distributed optimization method for solving nonlinear equations with constraints is developed in this paper. The multiple constrained nonlinear equations are converted into an optimization problem and we solve it in a distributed manner. Due to the possible presence of nonconvexity, the converted optimization problem might be a nonconvex optimization problem. To this end, we propose a multi-agent system based on an augmented Lagrangian function and prove that it converges to a locally optimal solution to an optimization problem in the presence of nonconvexity. In addition, a collaborative neurodynamic optimization method is adopted to obtain a globally optimal solution. Three numerical examples are elaborated to illustrate the effectiveness of the main results.
Automatic Irony and Sarcasm Detection in Russian Sentences: Baseline Methods

The paper describes experiments performed on two sets of manually annotated data. The task of irony and sarcasm detection in Russian sentences was solved using baseline classifiers, i. e., BERT, Bi-LSTM, SVM, Random Forest, Logistic Regression. The best achieved F1-score for each classifier was 0.76, 0.73, 0.66, 0.64, 0.68 respectively. The results achieved by BERT and Bi-LSTM classifiers are comparable with the results from the articles describing the application of similar approaches for English language. Analysis of the results allowed to conclude that transferring the word context improves classification metrics and refinement of training data allows to improve the classifier's performance.
Designing A Secure Food Supply Chain System using Blockchain in Agricultural IoT

One of the important areas of research that needs a global attention is the agribusiness and its network of supply chains. Internet of Things (IoT) based smart models of this system facilitates all the participants in several means to make the life easy. Blockchain can play an inevitable role to enhance the security, to protect the end users from the potential health risks, and to avoid the financial frauds. Therefore, some blockchain based food supply systems have been developed to provide the transparency and immutability. However, the security of such existing systems can be optimized with improved integrity, confidentiality, latency, authentication delay, throughput, and computational time. In this direction, a blockchain and IoT enabled secure and smart agricultural food supply chain system has been devised and presented in this paper. The suggested model performs better than the current plans in terms of trust, authentication delay, computational time, and throughput.
Metaheuristic Optimization Algorithms and recent applications: A comprehensive survey

Optimization is the process of searching optimal solution of complex problems more efficiently. There is a class of problems which is called NP (nondeterministic polynomial) problem which means that solution of NP problem based on metaheuristic approach may be verified in polynomial time which might take exponential number of steps to find the solution. To solve such problems, we may use special type of algorithm which is called Meta Heuristic (MA’s) algorithm. MA’s are able to provide near optimal solution by introducing randomization technique with deterministic approach. In this paper, we have discussed some meta heuristic algorithms and their applications in research areas.