Reviewerly/exHarmony huggingface dataset:
LLLWBC: A New Low-Latency Light-Weight Block Cipher

Lightweight cipher suitable for resource constrained environment is crucial to the security of applications such as RFID, Internet of Things, etc. Moreover, in recent years low-latency is becoming more important and highly desirable by some specific applications which need instant response and real-time security. In this paper, we propose a new low-latency block cipher named LLLWBC. Similar to other known low-latency block ciphers, LLLWBC preserves the important $$\alpha $$ -reflection property, namely the decryption for a key K is equal to encryption with a key $$K \oplus \alpha $$ where $$\alpha $$ is a fixed constant. However, instead of the normally used SP-type construction, the core cipher employs a variant of generalized Feistel structure called extended GFS. It has 8 branches and employs byte-wise round function and nibble-wise round permutation iterated for 21 rounds. We choose the round permutations carefully together with a novel key schedule to guarantee the $$\alpha $$ -reflection property. This allows an efficient fully unrolled implementation of LLLWBC in hardware and the overhead of decryption on top of encryption is negligible. Moreover, because of the involutory property of extended GFS, the inverse round function is not needed, which makes it possible to be implemented in round-based architecture with a competitive area cost. Furthermore, our security evaluation shows that LLLWBC can achieve enough security margin within the constraints of security claims. Finally, we evaluate the hardware and software performances of LLLWBC on various platforms and a brief comparison with other low-latency ciphers is also presented.
An Ensemble and Multi-View Clustering Method Based on Kolmogorov Complexity

The ability to build more robust clustering from many clustering models with different solutions is relevant in scenarios with privacy-preserving constraints, where data features have a different nature or where these features are not available in a single computation unit. Additionally, with the booming number of multi-view data, but also of clustering algorithms capable of producing a wide variety of representations for the same objects, merging clustering partitions to achieve a single clustering result has become a complex problem with numerous applications. To tackle this problem, we propose a clustering fusion algorithm that takes existing clustering partitions acquired from multiple vector space models, sources, or views, and merges them into a single partition. Our merging method relies on an information theory model based on Kolmogorov complexity that was originally proposed for unsupervised multi-view learning. Our proposed algorithm features a stable merging process and shows competitive results over several real and artificial datasets in comparison with other state-of-the-art methods that have similar goals.
Optimized Round Robin Scheduling Algorithm Using Dynamic Time Quantum Approach in Cloud Computing Environment

Cloud computing refers to a sophisticated technology that deals with the manipulation of data in internet-based servers dynamically and efficiently. The utilization of the cloud computing has been rapidly increased because of its scalability, accessibility, and incredible flexibility. Dynamic usage and process sharing facilities require task scheduling which is a prominent issue and plays a significant role in developing an optimal cloud computing environment. Round robin is generally an efficient task scheduling algorithm that has a powerful impact on the performance of the cloud computing environment. This paper introduces a new approach for round robin based task scheduling algorithm which is suitable for cloud computing environment. The proposed algorithm determines time quantum dynamically based on the differences among three maximum burst time of tasks in the ready queue for each round. The concerning part of the proposed method is utilizing additive manner among the differences, and the burst times of the processes during determining the time quantum. The experimental results showed that the proposed approach has enhanced the performance of the round robin task scheduling algorithm in reducing average turn-around time, diminishing average waiting time, and minimizing number of contexts switching. Moreover, a comparative study has been conducted which showed that the proposed approach outperforms some of the similar existing round robin approaches. Finally, it can be concluded based on the experiment and comparative study that the proposed dynamic round robin scheduling algorithm is comparatively better, acceptable and optimal for cloud environment.
Simultaneous Image Encryption and Compression using Adaptive Bit Plane Quadtree-based BTC

In the digital World, huge amount of data is shared or stored every day. While sharing the data there may be a chance accessing of the data by an unauthorized persons. So In this study, by modifying standard JPEG compression algorithm, encryption and simultaneous decompression, decryption method is inspected. The proposed system deals with the computation of time and cost by combining compression and encryption by using Adaptive Bit plane Quad tree Block Truncation Coding (ABTC) technique. Here combined with edge detection and adaptive bit plane quantization, the adaptive bit plane quad tree-based BTC algorithm is used. The encryption of the data can be done with key validation technique. The main and ultimate goal is to secure the data from the unauthorized persons, to reduce the redundancy of the image and to store or transmit data in an efficient form.
Comparative Analysis of Machine Learning in Predicting the Treatment Status of COVID-19 Patients

COVID-19 has become a global pandemic that causes many deaths, so medical treatment for COVID-19 patients gets special attention, whether hospitalized or self-isolated. However, the problem in medical action is not easy, and the most frequent mistakes are due to inaccuracies in medical decision-making. Meanwhile, machine learning can predict with high accuracy. For that, or that's why this study aims to propose a data mining classification method as a machine learning model to predict the treatment status of COVID-19 patients accurately, whether hospitalized or self-isolated. The data mining method used in this research is the Random Forest (RF) and Support Vector Machine (SVM) algorithm with Confusion Matrix and k-fold Cross Validation testing. The finding indicated that the machine learning model has an accuracy of up to 94% with the RF algorithm and up to 92% with the SVM algorithm in predicting the COVID-19 patient's treatment status. It means that the machine learning model using the RF algorithm has more accurate accuracy than the SVM algorithm in predicting or recommending the treatment status of COVID-19 patients. The implication is that RF machine learning can help/replace the role of medical experts in predicting the patient's care status.
Solving Cold Start Problem for Recommendation System Using Content-Based Filtering

A recommendation system is an effective supporting tool for a user to obtain the relevant result and avoid information overloading. However, the user cold-start problem has occurred when the system does not have adequate information about the user. In this project, we propose a content-based filtering method that applies the movie genres for finding the similarities among movies. We use the Term Frequency-Inverse Document Frequency (TF-IDF) as a text mining technique for retrieving the frequent movie genres and produce the TF-IDF into a matrix. Then, we use the matrix to generate recommendation models that apply with different similarity techniques which are Cosine similarity, Pearson correlation coefficient, and Euclidean distance. As a result, the average of similarities has reached the highest of 84 percent using Cosine similarity and at most 20 percent of a not satisfied movie recommended among 30 recommendation items. The experimental results on MovieLens-20M data that apply to our approach have achieved high similarity results among movies recommended, low frequency of not-satisfied movie ratings, and it consistently improved the performance in addressing the user cold-start problem.
Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis

Background: Lung cancer is a fatal disease that kills approximately 85% of those diagnosed with it. In recent years, advances in medical imaging have greatly improved the acquisition, storage, and visualization of various pathologies, making it a necessary component in medicine today. Objective: Develop a computer-aided diagnostic system to detect lung cancer early by segmenting tumor and non-tumor tissue on Tissue Micro Array Analysis (TMA) histopathological images. Method: The prototype computer-aided diagnostic system was developed to segment tumor areas, non-tumor areas, and fundus on TMA histopathological images. Results: The system achieved an average accuracy of 83.4% and an F-measurement of 84.4% in segmenting tumor and non-tumor tissue. Conclusion: The computer-aided diagnostic system provides a second diagnostic opinion to specialists, allowing for more precise diagnoses and more appropriate treatments for lung cancer.
Heuristically accelerated FRIQ-learning

In traditional reinforcement learning, at the beginning of the learning phase the solution of the targeted problem is unknown. Based on the feedback of the environment these methods are applying a trial-and-error type search for finding possible solutions of the problem hidden in the environment. Whenever there is an existing a priori knowledge about the solution of the problem, that can be adapted, the efficiency of the learning process can be improved. In the expert knowledge-included FRIQ-learning (Fuzzy Rule Interpolation-based Q-learning) method, the expert defines the a priory knowledge in terms of state-action fuzzy rules. If the expert defined rules are containing imprecise, or incorrect information, a tuning method is required to correct the expert rules. The main goal of this paper is to propose a method that can be used to tune the a priori expert rules during the learning phase. In addition, a distance-based reduction method will be introduced briefly, which is suitable for reducing the rule-base size during the learning process. The paper also provides an application example to highlight the benefits of the proposed methods.
Integrating Prior Knowledge into Attention for Ship Detection in SAR Images

Although they have achieved great success in optical images, deep convolutional neural networks underperform for ship detection in SAR images because of the lack of color and textual features. In this paper, we propose our framework which integrates prior knowledge into neural networks by means of the attention mechanism. Because the background of ships is mostly water surface or coast, we use clustering algorithms to generate the prior knowledge map from brightness and density features. The prior knowledge map is later resized and fused with convolutional feature maps by the attention mechanism. Our experiments demonstrate that our framework is able to improve various one-stage and two-stage object detection algorithms (Faster R-CNN, RetinaNet, SSD, and YOLOv4) on two benchmark datasets (SSDD, LS-SSDD, and HRSID).
Cybersecurity among University Students from Generation Z: A Comparative Study of the Undergraduate Programs in Administration and Public Accounting in two Mexican Universities

The present study aimed to differentiate cybersecurity habits among Generation Z university undergraduate students of Public Accounting and Administration at two Mexican public universities and to identify relationships between the study participants’ self-perceived level of computer knowledge and cybersecurity habits. Data were collected using a questionnaire, which was administered to 321 and 242 students from Tamaulipas and Jalisco respectively. The results showed that students from Jalisco scored higher in knowledge regarding cybersecurity and risky practices. Similarly, weak relationships were found between participants’ habits/routines and self-perceived knowledge about cybersecurity. Our study highlights the importance of providing curricular and extracurricular cybersecurity-related training.
A survey of evolutionary algorithms for supervised ensemble learning

Abstract This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies based on which stage of the ensemble learning process is addressed by the evolutionary algorithm: the generation of base models, model selection, or the integration of outputs. The next three levels of the taxonomy further categorize studies based on methods used to address each stage. In addition, we categorize studies according to the main types of objectives optimized by the evolutionary algorithm, the type of base learner used and the type of evolutionary algorithm used. We also discuss controversial topics, like the pros and cons of the selection stage of ensemble learning, and the need for using a diversity measure for the ensemble’s members in the fitness function. Finally, as conclusions, we summarize our findings about patterns in the frequency of use of different methods and suggest several new research directions for evolutionary ensemble learning.
Linear-in-parameters neural adaptive observers for nonlinear systems in observability canonical form

Nonlinear adaptive observers that do not require knowledge of the dynamics of the system being observed are proposed. This objective is pursued by envisioning a linear-in-parameters adaptive law that allows one to estimate the system dynamics, which are assumed to be in observability canonical form, just from measurements of the output. In the case that a parametric model for the plant is known, the proposed adaptive observer asymptotically reconstructs both the state and the unknown parameters of the observed system. On the other hand, if a parametric model for the system is not available, then the reconstruction of both its state and of its model is practical. The effectiveness of these observers is validated by numerical simulations.
A Scientific Paper Recommendation Framework Based on Multi-Topic Communities and Modified PageRank

Personalized PageRank is a variant of PageRank, widely developed for citation recommendation.However, the personalized PageRank that works with a vast amount and rich scholarly data still results in information overload.Sometimes, junior scholars still need help to arrange queries quickly because of limited domain knowledge.Senior researchers need reference papers regarding a similar topic they intend to search for and related topics as a new insight.In this research, scientific citation recommendation aims to find the most influential papers with similar and related topics.Related topic papers in serendipitous perspectives are reference papers that are novel, diversified and unexpected to a user.The unexpectedness of recommended papers can be papers with different topics to queries but still relevant.To accomplish these challenges, we propose a framework of scientific citation recommendation with serendipitous perspectives.The framework includes feature extraction of an academic citation network, selection of multi-topic communities, and ranking papers in the selected multi-topic communities by modified PageRank.Papers in the chosen communities tend to link to similar and related papers.Modified PageRank is an extension of personalized PageRank, which works on multi-topic communities and manuscript queries.The experiments reveal that the proposed models outperform some models of personalized PageRank and some models of Content-Based Filtering.The multi-topic communities-based models work more effectively than the baselines if they run in a large dataset since the topic communities become more cohesive.
Hierarchical approaches to Text-based Offense Classification

Researchers working with administrative crime data often must classify offense narratives into a common scheme for analysis purposes. No comprehensive standard currently exists, nor is there a mapping tool to transform raw descriptions into offense types. This paper introduces a new schema, the Uniform Crime Classification Standard (UCCS), and the Text-based Offense Classification (TOC) tool to address these shortcomings. The UCCS schema draws from existing efforts, aiming to better reflect offense severity and improve type disambiguation. The TOC tool is a machine learning algorithm that uses a hierarchical, multilayer perceptron classification framework, built on 313,209 hand-coded offense descriptions from 24 states, to translate raw descriptions into UCCS codes. We test how variations in data processing and modeling approaches affect recall, precision, and F1 scores to assess their relative influence on model performance. The code scheme and classification tool are collaborations between Measures for Justice and the Criminal Justice Administrative Records System.
An Improved Sparrow Search Algorithm and Its Application in HIFU Sound Field

The sparrow search algorithm (SSA) is a novel swarm intelligence optimization algorithm. It has a fast convergence speed and strong global search ability. However, SSA also has many shortcomings, such as the unstable quality of the initial population, easy to fall into the local optimal solution, and the diversity of the population decreases with the iterative process. In order to solve these problems, this paper proposes an improved sparrow search algorithm (ISSA). ISSA uses Chebyshev chaotic map and elite opposition-based learning strategy to initialize the population and improve the quality of the initial population. In the process of producer location update, dynamic weight factor and Levy flight strategy are introduced to avoid falling into a local optimal solution. The mutation strategy is applied to the scrounger location update process, and the mutation operation is performed on individuals to increase the diversity of the population. In order to verify the feasibility and effectiveness of ISSA, it is tested on 23 benchmark functions. The results show that compared with other seven algorithms, ISSA has higher convergence accuracy, faster convergence speed, and stronger stability. Finally, ISSA is used to optimize the sound field of high-intensity focused ultrasound (HIFU). The results show that ISSA can effectively improve the focusing performance and reduce the influence of sound field sidelobe, which is of great benefit for HIFU treatment.
The Reality of Local Investment in Iraq and Prospects for its Development: a Case Study in the Baghdad Investment Commission

Purpose: This research is to identify the most important challenges for the local investment commissions and to develop solutions and proposals to encourage local and foreign investment in local governments in Iraq (the Iraqi provinces are irregular in the region). Theoretical Framework: This research suggests a conceptual framework for the local investment commissions in order to solve their problems, the most important of which was to identify the most critical challenges which are facing the Baghdad Investment Commission BIC and how to overcome them. Design/The methodology approach: Research involved a mixed-methods approach through two stages. During the first stage, the researcher gathered quantitative data from all investment projects that obtained Baghdad investment commission BIC from 2015 to 2021 were 168 projects. The second stage gathered qualitative data to explore the survey results more deeply by conducting individual interviews with a sub-sample of 12, 6 interviewees are from BIC and 6 interviewees are from investment organisations. Findings: The research showed several important results represented by legal and administrative challenges. Also, weak incentives and guarantees provided by The National Investment Low NIL No13 of 2006 and its amendments in 2010. The practical and social implications: This research will enable the local investment commissions how to deal with their challenges and then improve their provided services for both investors (foreign and local), particularly in Baghdad province, Iraq. The originality of research: This article provided a significant topic for businessmen and governments on international and local levels, also there is no previous research that has thoroughly searched the reality of local investment in Iraq and prospects for its development.
The Impact of Outsourcing of Hotel Housekeeping Services on Hotel Performance: a Study on 5-Star Hotels of Delhi NCR

Purpose: The current world poses a stiff competition for hotels due to the pandemic, and now hotels need to be innovative and flexible to increase competitive advantage. The attention needs to be shifted to networking and collaboration with external sources to enhance the boundaries of hotels. Theoretical framework: The study combines the various models of outsourcing in respect to the performance. Outsourcing is the business practice of engaging external service providers to accomplish in-house tasks. This has intrigued managers' interest in recent years as they assess whether it is preferable to undertake things internally or outside. Outsourcing is a commercial arrangement in which an organization controls a portion or all of the duties of a department inside an organization to an external party. Design/methodology/approach: Data collection was conducted through questionnaire approach from hotel revenue managers and housekeeping managers 5-star hotels. Findings: The study found that outsourcing increased flexibility, competitiveness, profitability and improved sales in the hotels. Research, Practical &amp; Social implications: The hospitality industry is highly focusing on the housekeeping department since the onset of pandemic due to the high standards of cleaning and hygiene maintenance. The industry now relies on outside service providers to meet the requirements. Originality/value: The current study aims to study the influence of outsourcing strategy and its application on housekeeping department on hotel performance of 5-star hotels in Delhi NCR
NEO-3DF: Novel Editing-Oriented 3D Face Creation and Reconstruction

Unlike 2D face images, obtaining a 3D face is not easy. Existing methods, therefore, create a 3D face from a 2D face image (3D face reconstruction). A user might wish to edit the reconstructed 3D face, but 3D face editing has seldom been studied. This paper presents such method and shows that reconstruction and editing can help each other. In the presented framework named NEO-3DF, the 3D face model we propose has independent sub-models corresponding to semantic face parts. It allows us to achieve both local intuitive editing and better 3D-to-2D alignment. Each face part in our model has a set of controllers designed to allow users to edit the corresponding features (e.g., nose height). In addition, we propose a differentiable module for blending the face parts and making it possible to automatically adjust the face parts (both the shapes and the locations) so that they are better aligned with the original 2D image. Experiments show that the results of NEO-3DF outperform existing methods in intuitive face editing and have better 3D-to-2D alignment accuracy (14% higher IoU) than global face model-based reconstruction. Code available at https://github.com/ubc-3d-vision-lab/NEO-3DF .