Miracle-dz/UniFAR-FTUs huggingface dataset:
Stepping ahead based hybridization of meta-heuristic model for solving Global Optimization Problems

Intelligent optimization algorithms based on swarm principles have been widely researched in recent times. The Firefly Algorithm (FA) is an intelligent swarm algorithm for global optimization problems. In literature, FA has been seen as one of the efficient and robust optimization algorithm. However, the solution search space used in FA is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although, there are a lot of modifications and hybridizations of FA with other optimizing algorithms, there is still a room for improvement. Therefore, in this paper, we first propose modification of FA by introducing a stepping ahead parameter. Second, we design a hybrid of modified FA with Covariance Matrix Adaptation Evolution Strategy (CMAES) to improve the exploitation while containing good exploration. Traditionally, hybridization meant to combine two algorithms together in terms of structure only, and preference was not taken into account. To solve this issue, preference in terms of user and problem (time complexity) is taken where CMAES is used within FA’s loop to avoid extra computation time. This way, the structure of algorithm together with the strength of the individual solution are used. In this paper, FA is modified first and later combined with CMAES to solve selected global optimization benchmark problems. The effectiveness of the new hybridization is shown with the performance analysis.
A Level Set Based Unified Framework for Pulmonary Nodule Segmentation

This letter introduces a unified framework for accurate segmentation of five different types of pulmonary nodules, namely, solid, juxtapleural, juxtavascular, part solid and ground glass by designing a contrast-adaptive shape-driven level set algorithm. Most of the existing methods have targeted segmenting few specific types of nodules. Variability of shapes along with poor contrast make pulmonary nodule segmentation an extremely challenging problem. To deal with low contrast, a contrast-adaptive term, based on intensities, is incorporated to guide the evolution of level set. A shape term is further introduced for accurate segmentation of different pulmonary nodules having varying shapes. Experiments on the publicly available LIDC/IDRI dataset clearly reveal that our method achieves promising results as compared to several state-of-the-art competitors.
Mutual Aid Among Sensors: An Emergency Function for Sensor Networks

A large number of wireless sensor nodes in a certain area results in high contention. Inevitably, the transmissions of any possible critical data packets may fail due to collisions. In this letter, we introduce an aspect of human intelligence in wireless sensor networks, influenced by cooperative networking, which enhances the timely delivery of critical data. Mutual aid among sensors is an emergency out-of-the-box medium access control function for IEEE 802.15. 4-2020. Specifically, the network coordinator detects critical data packets and sets an emergency flag to its next beacon to inform the nodes that they may overhear data packets. When a node overhears a critical data packet from a neighboring node, it switches to sleep mode and stays idle until the end of the superframe. Thus, interference is mitigated locally and temporarily. Simulation results, using the CC2650 radio parameters in OMNeT++, show that interference is reduced significantly, in favor of the timely delivery of critical data packets.
Tamil News Clustering Using Word Embeddings

News aggregators support the readers to view news from multiple news providers via a single point. At the moment, the only news aggregator that supports Tamil news is Google news, which has some noticeable shortages. In this study, Term Frequency–Inverse Document Frequency and word embedding (fastText) document representation techniques were experimented with one pass and affinity propagation clustering algorithms to news title, as well as title and body in order to implement a news aggregator for the Tamil language. For this study we collected data from nine different news providers. When fastText was applied with one pass algorithm to news title and body, it managed to beat other approaches to achieve an average pairwise F-score of 81% with respect to manual clustering. Also, we were able to create a Tamil fastText word embedding model using more than 21 million words.
TGF‐β induces fibronectin synthesis through a c‐Jun N‐terminal kinase‐dependent, Smad4‐independent pathway

Transforming growth factor‐β (TGF‐β) exerts its effects on cell proliferation, differentiation and migration in part through its modulation of extracellular matrix components, such as fibronectin and plasminogen activator inhibitor‐1 (PAI‐1). Although the SMAD family of proteins recently has been shown to be a key participant in TGF‐β signaling, other signaling pathways have also been shown to be activated by TGF‐β. We report here that c‐Jun N‐terminal kinase (JNK), a member of the MAP kinase family, is activated in response to TGF‐β in the human fibrosarcoma HT1080‐derived cell line BAHgpt. Stable expression of dominant‐negative forms of JNK1 and MKK4, an upstream activator of JNK, results in loss of TGF‐β‐stimulated fibronectin mRNA and protein induction, while having little effect on TGF‐β‐induced levels of PAI‐1. The human fibronectin promoter contains three CRE elements, one of which has been shown to bind a c‐Jun–ATF‐2 heterodimer. Utilizing a GAL4 fusion trans‐reporting system, we demonstrate a decrease in transactivating potential of GAL4–c‐Jun and GAL4–ATF‐2 in dominant‐negative JNK1‐ and MKK4‐expressing cells. Finally, we show that TGF‐β‐induced fibronectin synthesis is independent of Smad4. These results demonstrate that TGF‐β‐mediated fibronectin induction requires activation of JNK which in turn modulates the activity of c‐Jun and ATF‐2 in a Smad4independent manner.
Fast line detection by the local polar coordinates using a window

The Hough transform is known as a technique for detecting image components (primitives) such as lines or circles. We previously proposed a “primitive detection method using local polar coordinates,” which can quickly detect primitives by reducing the dimensionality of the vote array of this Hough transform. However, this method has the property that the primitive detection speed is affected by the position and quantity of noise that is contained in the image (this property is called “noise dependency”), and this property had become a problem when the method was actually used. Therefore, in this paper, we propose a solution for this noise dependency problem. The proposed method sets a window in the vicinity of a local pole origin and determines whether or not a local pole can be set according to an operation (multiple votes per pixel) within this window. From various types of experimental results, we show that the proposed method is highly effective even though it is simple. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(6): 43–52, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20359
Detection of Hazardous Information Based on HTML Elements

In this paper, we propose high-speed, accurate algorithms for detecting hazardous Web pages. Our algorithms automatically choose strings that appear especially in HTML elements of hazardous Web pages. We use these strings in combination as features of SVMs (support vector machines), and detect hazardous Web pages. Since our algorithms do not rely on the text parts of Web pages, they can detect Web pages that existing text-based algorithms have difficulty in detecting. By conducting a large-scale performance evaluation with real hazardous Web pages, we showed that the hybrid algorithms of our algorithms and existing text-based algorithms increase the precision of existing text-based algorithms alone by 9.3%.
Dual Inheritance Evolution Strategy for Deep Neural Network Optimization

Deep neural networks (DNNs) need intensive tuning of their configurations such as network structures and learning conditions. The tuning is a type of black-box optimization problem where evolutionary algorithms are applicable. A distinctive property in evolutionary optimization of DNN configurations is that there is a double structure in the optimization; the evolutionary algorithm optimizes a chromosome representing the DNN configuration while an individual DNN with the configuration learns from training data typically by back-propagation. With an aim to obtain better-optimized DNNs by evolutionary algorithms, we propose a dual inheritance evolution strategy based on an analogy to human brain evolution where gene and culture co-evolves. The proposed method is an extension of a conventional evolution strategy by introducing an additional pass to directly propagate culture or knowledge from ancestor DNNs to descendant DNNs by integrating teacher-student learning. We apply the proposed method to the automatic tuning of an end-to-end neural network-based speech recognition system. Experimental results show that the proposed method produces a smaller model with higher recognition performance than a baseline optimization based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES).
Machine Learning Assisted New Product Setup

In the past, concepts like critical area analysis, have been successfully implemented to predict random and systematic layout induced effects. This has enabled semiconductor companies to have an initial estimate as to how a fixed process will respond to a variety of different designs. However, as the number of individual products increases, along with a reduction in the total number of wafers per product, it becomes increasingly difficult to determine which process parameters will lead to the highest possible yield for each individual product. We have outlined a methodology using machine learning that combines process and design data to greatly reduce the time needed for setting up new products. We have shown that similar designs (based on our feature extraction) behave similarly in the fab, thus allowing us to construct models that can eventually be used to find the optimal process conditions for a given design. Due to the nature or process optimization, this methodology also explores the use of SHAPley additive explanations (SHAP) in order to “interface” with existing human and physical explanations of the observations, thus providing a mechanism to assess the quality and reliability of the numerically derived models.
Highly-available services using the primary-backup approach

The authors derive lower bounds and the corresponding optimal protocols for three parameters for synchronous primary-backup systems. They compare their results with similar results for active replication in order to determine whether the common folklore on the virtues of the two approaches can be shown formally. They also extend some of their results to asynchronous primary-backup systems. They implement an important subclass of primary-backup protocols that they call 0-blocking. These protocols are interesting because they introduce no additional protocol related delay into a failure-free service request. Through implementing these protocols the authors hope to determine the appropriateness of their theoretical system model and uncover other practical advantages or limitations of the primary-backup approach. <<ETX>>
A Surrogate-Assisted Clustering Particle Swarm Optimizer for Expensive Optimization Under Dynamic Environment

In recent years, surrogate-assisted evolutionary algorithms have been developed for expensive optimization. However, a majority of applications are dynamic optimization problems in the real-world. In this paper, therefore, a surrogate-assisted clustering particle swarm optimizer is proposed for expensive dynamic optimization. In the proposed method, several clusters are first created by affinity propagation clustering, and then local radial basis function (RBF) surrogates are built based on the neighbor evaluated points for each cluster. Finally, in each cluster, the local RBF assists particle swarm optimizer to search the most promising point, which is evaluated by real objective function. To track dynamic environment, the points with best exact fitness in each cluster are added into new cradle swarm, if environmental change has occurred. A variety of experiments have been conducted on the moving peaks benchmark (MPB) with 500 change frequency in each environment. The experimental results have demonstrated that the proposed approach has a good performance.
Non-integer order control of a buck-boost converter with ultracapacitor

Ultracapacitors are devices that their behaviour are suitable modelled using fractional(non-integer)-order models, thus for their control the fractional-order control methodology is needed. In this paper, non-integer order control is applied to improve the performance of a buck-boost dc-dc power converter with ultracapacitor-based on state feedback techniques. The approach is based on stabilization in the Lyapunov's sense for linear rational order systems.
The living with dysarthria group: implementation and feasibility of a group intervention for people with dysarthria following stroke and family members.

BACKGROUND The broad life implications of acquired dysarthria are recognized, but they have received little attention in stroke management. Reports of group therapy, which may be a suitable approach to intervention, are not available for stroke-related dysarthria. AIMS To examine the operational feasibility of and response to a new eight-session weekly group intervention programme, Living with Dysarthria, designed for people with chronic dysarthria following stroke and their main communication partners. METHODS & PROCEDURES The target participation was for programme completion by two groups of eight people with dysarthria (PWD) and available family members (FMs) or carers. An active recruitment strategy was undertaken from the speech and language therapy case records for the previous 6 years in two hospitals with combined annual stroke admissions of over 500 people. Twelve PWD and seven FMs were recruited (group 1: seven PWD and four FMs; group 2: five PWD and three FMs). Speech intelligibility, communication effectiveness, general well-being, quality of communication life, and knowledge of stroke and dysarthria were assessed pre- and post-programme. Each PWD and FM also set an individual goal and rated their achievement of this on a 0-10 scale. OUTCOMES & RESULTS Recruitment to the programme was lower than anticipated and below target. The 12 PWD were recruited from 62 initial contacts, which was the total number who according to available information met the criteria. The programme was viable: it ran to plan, with only minor content alterations, in community accommodation, and with good participant engagement. Group median score changes were in a positive direction for all measures and effect sizes ranged from 0.17 (quality of communication life) to 0.46 (intelligibility). Significant post-programme changes were present for intelligibility and knowledge of stroke and dysarthria (p= 0.05). Participants' ratings of goal achievements ranged from 6 (some change) to 10 (a lot of change). CONCLUSIONS & IMPLICATIONS The recruitment experience revealed a take-up rate of around 20% from PWD following stroke, informing future planning. The participant engagement and performance results from the piloting of the programme indicate that the Living with Dysarthria programme is viable and has potential for effecting positive change. Further testing is justified.
Design of spherical loop antenna with Ultra-Wide bandwidth

In this paper, we propose a spherical loop antenna with ultra-wide bandwidth. The proposed antenna has the characteristic of the Ultra-Wide Band (UWB) using self-complementary structure. The designed antenna combines a loop antenna with a slot antenna. The proposed antenna is globular shape like a ball. The dimension of the antenna is 0.267λL, where λL is one wavelength at 0.2 GHz, in diameter and has more than 100:1 bandwidth.
FPGA implementation of digital controller for DC-DC buck converter

This paper describes a complete digital controlled dc-dc buck converter performed by FPGA circuitry. All tasks as are analog to digital conversion, control algorithm and pulse width modulation were implemented in the FPGA. This approach enables high-speed dynamic response and programmability of the controller without external passive components and more the controller structure could be changed from linear to non-linear easily also without external components. The applied algorithm enables the switching frequency of 100 kHz.
Hypocholesterolemic effect of red pitaya (Hylocereus sp. ) on hypercholesterolemia induced rats

This study was carried out to evaluate the total phenolic content and anti-oxidant activity of methanolic extract of red pitaya, and hypocholesterolemic effect of red pitaya (Hylocereus sp.) on lipid profiles status on hypercholesterolemia induced rats. From the analysis, total phenolic content in red pitaya is 46.06 ± 1.77 mg GAE/100 g fresh weight and antioxidant activity is 76.10% using FTC method. TBA analysis also showed red pitaya extract had high antioxidant effect (72.90%). An in-vivo study also showed red pitaya has hypocholesterolemic effect on induced hypercholesterolemia rats. After 11 weeks of study, total blood cholesterol significant decrease (p<0.05) in the groups supplement with red pitaya. The total cholesterol (TC) level for group PF1 were reduced from 3.356 mmol/L to 1.707 mmol/L (49.14%), group PF2 reduced from 3.435 mmol/L to 1.487 mmol/L (56.72%) and group PF3 reduced from 3.448 mmol/L to 1.412 mmol/L (59.06%) as compare to baseline respectively. The mean total cholesterol level in both negative (N – 5.12%) and positive controls (group HC – 13.79%) were not significantly different (p<0.05). The mean triglycerides (TG) level for all groups had shown a reduction (p<0.05) with value of 23.87% (group N), 22.674% (group HC), 42.81% (group PF1), 52.82% (group PF2) and 59.52% (group PF3) as compare to baseline levels. The mean HDL level increased by about 2.12% (group N), 19.31% (group PF1), 21.93% (group PF2) and much higher increase in group PF3 (34.42%). The mean LDL decreased by about 39.06% (PF3), 15.10% (PF2), 1.50% (PF1) and 4.33% (group N). The positive control has showed significantly increase with the mean value for 25.68%. In conclusion, all groups that received red pitaya supplementation has high antioxidant properties and showed a good results in managing of lipid profile. It was suggested that the consumption of red pitaya demonstrated the potential to reduce dyslipidemia and play a role in the prevention of cardiovascular disease.
Chemotherapy Effect on Neutrophils Function in Leukemic Patients

Neutrophils are chief phagocytic leukocytes, which form an essential part of the innate immune system that plays a key role in defense against living pathogens. Neutropenia has been considered to be the most important risk factor for severe infection during chemotherapy. In this study, the activity of neutrophils' function in adult leukemic patients before and after chemotherapy induction has been investigated. Whole blood samples were obtained from 32 leukemic patients with different cases, 8 patients from each: acute lymphatic leukemia, acute myeloid leukemia, chronic lymphatic leukemia and chronic myeloid leukemia. Immunophenotyping test was used to count T-lymphocytes, B-lymphocytes and Natural-killer lymphocytes. The mean results of the immunophenotyping test were in normal range so the count of these cells was not affected by chemotherapy. An oxidative burst test was used to quantify the neutrophils functions. There was significant differences between leukemia groups after chemotherapy (F = 3.923, p = 0.01). Acute lymphatic leukemia was the lower significant (p = 0.003) compared to the controls. There was no significant difference between the other cases and the controls. Acute lymphatic leukemia case was the most affected by chemotherapy.
TYPE 2 DIABETES MELLITUS - THE EPIDEMIC OF THE 21 ST CENTURY: THE INDIAN SCENARIO

Diabetes mellitus is the commonest metabolic abnormality in the world. Type 2 diabetes (Noninsulin dependent diabetes, NIDDM) is the commonest form of diabetes constituting nearly 90% of the diabetic population in any country. The prevalence of type 2 diabetes varies in different geographic regions and also in different ethnic groups. Prevalence of type 2 diabetes is increasing in most of the countries, especially in developing countries such as India [1]. The first authentic data on the prevalence of diabetes in India came from the multicentric study conducted by the Indian Council of Medical Research (ICMR) in the early seventies. This study reported a prevalence of 2.3% in the urban and 1.5% in the rural areas [2]. The criteria used in this study were different from those set by the WHO expert Committee on Diabetes Mellitus [3].
ON SOME OPTIMUM CHEMICAL BALANCE WEIGHING DESIGNS FOR v+1 OBJECTS

Definition 1.1. A weighing design is said to be optimum if X'X=nln. The problem is to choose X in such a way that the variance factors are minimized. Several methods of constructing X are available in the literature. Dey(1971), Saha(1975), Kageyama and Saha(1983)and others have shown how optimum chemical balance weighing designs can be constructed from the incidence matrices of balanced incomplete block(BIB) designs for p=v objects. Saha and Kageyama(1984)have constructed optimum chemical balance weighing designs for v+10bjects in 4(r-a)weighings from incidence matrices of BIB designs for v treatments. In the presented paper we study another method of constructing the design matrix Xfor an optimum chemical balance weighing design for v+10bjects. It is based on incidence matrices of BIB designs for v treatments.
A Low-Sensitivity Recurrent Neural Network

The problem of high sensitivity in modeling is well known. Small perturbations in the model parameters may result in large, undesired changes in the model behavior. A number of authors have considered the issue of sensitivity in feedforward neural networks from a probabilistic perspective. Less attention has been given to such issues in recurrent neural networks. In this article, we present a new recurrent neural network architecture, that is capable of significantly improved parameter sensitivity properties compared to existing recurrent neural networks. The new recurrent neural network generalizes previous architectures by employing alternative discrete-time operators in place of the shift operator normally used. An analysis of the model demonstrates the existence of parameter sensitivity in recurrent neural networks and supports the proposed architecture. The new architecture performs significantly better than previous recurrent neural networks, as shown by a series of simple numerical experiments.
Direct parametric control approach to robust integrated relative position and attitude control for non-cooperative rendezvous

In this paper, the integrated relative position and attitude nonlinear second-order model for non-cooperative rendezvous with the parameter uncertainty has been established, which consists of orbit dynamical model in the line of coordinate system and attitude dynamical model described by Euler angle, furthermore we consider the gravity discrepancy item to be the uncertain parameter, which be neglected generally, the 6 degree of freedom(6DOF) model complete in the sense that no approximation is taken. Based on the eigenstructure assignment and direct parametric control approach, we design the robust control law for non-cooperative rendezvous in the final phase subject to parameter uncertainty. The control law is constructed with the closed-loop poles system state and the design freedom parameters. Through solving an optimization problem, we obtain free parametric to calculate the control law. A numerical simulation demonstrates the effect of the designed control strategy.