Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Network Intrusion Detection with a Hybrid Adaptive Neuro Fuzzy Inference System and AVO-based Predictive Analysis

— Protecting data and computer systems, as well as preserving the accessibility, integrity, and confidentiality of vital information in the face of constantly changing cyberthreats, requires the vital responsibility of detecting network intrusions. Existing intrusion detection models have limits in properly capturing and interpreting complex patterns in network behavior, which frequently leads to difficulties in robust feature selection and a lack of overall intrusion detection accuracy. The drawbacks of current methods are addressed by a unique approach to network intrusion detection presented in this paper. This framework discusses the difficulties presented by changing cyberthreats and the critical requirement for efficient intrusion detection in a society growing more networked by the day. Using a Hybrid Adaptive Neuro Fuzzy Inference System and African Vulture Optimization model with Min-Max normalization and data cleaning on the NSL-KDD dataset, the methodology outlined here overcomes issues with complex network behavior patterns and improves feature selection for precise identification of potential security threats. This approach meets the need for an effective intrusion detection system. Python software is used to implement the suggested model since it is flexible and reliable. The results show a notable improvement in accuracy, with the Hybrid Adaptive Neuro Fuzzy Inference System and African Vulture Optimization model surpassing previous approaches significantly and obtaining an exceptional accuracy rate of 99.3%. The accuracy of the proposed model was improved by African Vulture Optimization, rising from 99.2% to 99.3%. When compared to Artificial Neural Network (78.51%), Random Forest (92.21%), and Linear Support Vector Machine (97.4%), this amazing improvement is clear. When compared to other techniques, the suggested model exhibits an average accuracy gain of about 20.79%.
Responsabilidade social das empresas, duas perspectivas

A atitude socialmente responsável das empresas está relacionada com a consciência social e com a compreensão da importância dos problemas sociais. Comportamentos empresariais são questionados e avaliados quanto à sua adequação em termos das responsabilidades das empresas perante a sociedade. Esta investigação analisa a atitude de contabilistas perante a responsabilidade social das empresas, recolhida através de um inquérito. Em primeiro lugar procedeu-se a uma análise descritiva dos dados e num plano multivariado recorreram-se a técnicas para analisar a interdependência de variáveis, como a análise em componentes principais e análise fatorial. Verifica-se que os contabilistas identificaram duas visões da responsabilidade social e apresentaram diferentes atitudes e posições face a estas visões, assentes em diferentes fundamentos éticos. Confirma-se que a responsabilidade social das empresas pode ser influenciada por modelos desenvolvidos por indivíduos que integram as empresas e representar a relação que estes pretendem que a empresa desenvolva com a sociedade.
Rare Event Discovery And Event Change Point In Biological Data Stream

Outlier detection is currently an important and active research problem in many fields and is involved in numerous applications. This paper applies minimum volume ellipsoid (MVE) with principle component analysis (PCA) extension, a powerful algorithm for detecting multivariate outliers. If the data points exceed the cut-off value, the mahalanobis distance is used for the outliers. The paper also compares the performance of the suggested frame work with statistical methods to demonstrate its validity through simulation and experimental applications for incident detection in the field of bioinformatics. The observable results are obtained from biological samples such as glucose, acids and protein synthesis and are shown in the graphs.
How do I sound? Vowel production of multilingual students at Ma Chung University

This study aims to evaluate the Indonesian, English, and Mandarin vowel production of Ma Chung University students, a prominent Indonesian institution with a strong emphasis on workforce readiness. Given the university's language offerings encompassing Indonesian, English, and Mandarin, we employed a descriptive qualitative approach in acoustic-phonetic theory. Our respondent pool comprised of 15 PPBM (Chinese language major) students selected based on intermediate proficiency levels in all three languages, as indicated by TOEIC and HSK scores. First and second formant data for monophthongs were recorded in PRAAT tools and the data is compiled on Microsoft Excel, forming the foundation for Vowel Space Area (VSA). The data reveals a notable phenomenon: students' native language (Indonesian) influences their pronunciation of both the second (English) and third (Mandarin) languages. Particularly, English displays a distinct pattern of compressed vowel spread compared to the standard VSA table due to the lingering impact of L1 VSA. This effect accounts for observed vowel anomalies in student pronunciation. Importantly, this L1 and L2 influence is evident in their attempts to articulate L3 (Mandarin) sounds, exemplified by the significant deviation of /ə/ from the VSA. Moreover, students navigate a linguistic shift by modifying phonemes to align with their L1, inadvertently resulting in substantial deviations during English and Mandarin speech production.
Strengthening AES Security through Key-Dependent ShiftRow and AddRoundKey Transformations Utilizing Permutation

—AES (Advanced Encryption Standard) is a widely applied block cipher standard in the United States, used in various security applications today. Currently, there are numerous research endeavors aimed at making AES block ciphers dynamic to improve their security against contemporary strong attacks. The most common dynamic approach involves the dynamization of AES block transformations, including SubByte, ShiftRow, AddRoundKey, and MixColumn operations. The combination of these transformations has also been explored and proposed. However, to the best of our knowledge, the dynamic combination of AddRoundKey and ShiftRow transformations remains unexplored. Therefore, in this paper we introduce algorithms for generating key-dependent AddRoundKey and ShiftRow transformations based on permutations. Subsequently, these key-dependent transformations are applied to AES to create dynamic AES block ciphers. Security analysis and evaluation of NIST’s statistical criteria are performed, and the entropy of AES and dynamic AES is assessed. From our findings, it is evident that dynamic AES block ciphers can significantly enhance AES security and meet stringent randomness criteria, similar to AES.
El seguro de capacitación y empleo. Interpretación teórica y resultados de su implementación en la ciudad de Mar del Plata, Argentina

En este articulo se pretende estudiar la implementacion de una herramienta de proteccion contra el desempleo en Mar del Plata, Argentina. Se lo examina a traves de una adaptacion del marco liberal igualitario y, despues, con base en la propuesta de Roemer, se analiza la significatividad estadistica de los tipos definidos en relacion a los logros educativos. Finalmente, mediante un modelo logit binomial, se calcula la probabilidad de conseguir trabajo. Los resultados demuestran la importancia de las circunstancias personales en la definicion de los tipos, y que la probabilidad de trabajar es mayor para los varones y creciente respecto al tipo analizado.
ODFM: Abnormal Traffic Detection Based on Optimization of Data Feature and Mining

—The booming of computer networks and software applications has led to an explosive growth in the potential damage caused by network attacks. Efficient detection of abnormal traffic in networks is appealing for facilely mastering the traffic tracking and locating for network usage at low resource cost. High quality abnormal traffic detection of Internet becomes particularly relevant during the automated services of multiple application situations. This paper proposes a novel abnormal traffic detection algorithm called ODFM based on the optimization of data feature and mining. Specially, we develop a feature selection strategy to reduce the feature analysis dimension, and set a peer-to-peer (P2P) traffic identification module to filter and mine the related service traffic to reduce the amount of data detection and facilitate the abnormal traffic detection. Experimental results demonstrate that the proposed algorithm greatly improves the detection accuracy, which verifies its effectiveness and competitiveness in the general tasks of abnormal network traffic detection.
Aftershocks of the 1966 Parkfield-Cholame, California, earthquake: A detailed study

abstract Hypocenters and magnitudes of more than 600 aftershocks of the 1966 Parkfield-Cholame earthquake were determined from recordings of a dense network of portable seismograph stations operated in the epicentral region from 3 to 82 days after the main shock. Hypocenters were virtually confined to a nearly vertical zone extending downward from the zone of visible ground fracturing at the Earth9s surface to a depth of 12 to 14 km. Aftershocks were concentrated in patches on the slip surface, with large numbers of events in depth ranges of 2 to 4 km and 8 to 10 km and relatively few at depths of 5 to 7 km. First-motion patterns suggest that simple, nearly horizontal right-lateral strike-slip displacement was the source for an overwhelming majority of the aftershocks. The coefficient “ b ” in the frequency versus magnitude equation appears to be depth-dependent: it is about − 0.6 for events between 8- and 10-km depth but averages about −0.95 for events at other depths. Individual stations with consistently late P -wave arrivals were also found to record abnormally large amplitudes, with both anomalies increasing, apparently, with increasing thickness of sediments beneath the site.
Emotion Recognition in Psychology of Human-robot Interaction

The field of Human-Robot Interaction (HRI) has garnered significant attention in recent years, with researchers and practitioners seeking to understand the psychological aspects underlying the interactions between humans and robots. One crucial area of focus within HRI is the psychology of emotion recognition, which plays a fundamental role in shaping the dynamics of human-robot interaction. This paper provides an overview of the background of psychology in the context of human-robot interaction, emphasizing the significance of understanding human emotions in this domain. The concept of emotion recognition, a key component of human psychology, is explored in detail, highlighting its relevance in the context of human-robot interaction. Emotion recognition allows robots to perceive and interpret human emotions, enabling them to respond appropriately and enhance the quality of interaction. The role of emotion recognition in HRI is examined from a psychological standpoint, shedding light on its implications for the design and development of effective human-robot interfaces. Furthermore, this paper delves into the application of machine learning techniques for emotion recognition in the context of human-robot interaction. Machine learning algorithms have shown promise in enabling robots to recognize and respond to human emotions, thereby contributing to more natural and intuitive interactions. The utilization of machine learning in emotion recognition reflects the intersection of psychology and technological advancements in the field of HRI. Finally, the challenges associated with emotion recognition in HRI are discussed, encompassing issues such as cross-cultural variations in emotional expression, individual differences, and the ethical implications of emotion detection. Addressing these challenges is pivotal in advancing the understanding and implementation of emotion recognition in human-robot interaction, underscoring the interdisciplinary nature of this endeavor. In conclusion, this paper underscores the critical role of emotion recognition in the psychology of human-robot interaction, emphasizing its potential to revolutionize the way humans and robots engage with each other. By integrating insights from psychology, machine learning, and technology, advancements in emotion recognition have the potential to pave the way for more empathetic and responsive human-robot interactions, offering new avenues for research and practical applications in this burgeoning field.
The Relationship Between PRECISE-DAPT Score and Spontaneous Reperfusion of Infarct-Related Artery in Patients with ST-Segment Elevation Myocardial Infarction.

OBJECTIVE Spontaneous reperfusion (SR) presence and a low PREdicting bleeding Complications In patients undergoing Stent implantation and subsEquent Dual Anti Platelet Therapy (PRECISE-DAPT) score in patients with acute coronary syndrome have been associated with favorable clinical outcomes. This study aimed to investigate the relationship between SR and this score. METHODS The study included 436 patients with ST-segment elevation myocardial infarction (STEMI) who underwent primary percutaneous coronary intervention (PCI). Thrombolysis in myocardial infarction (TIMI) III blood flow presence in the infarct-related artery (IRA) before primary percutaneous coronary intervention (PCI) was defined as SR. Patients were categorized into two groups based on the presence (n = 49) or absence (n = 387) of SR. The PRECISE-DAPT score was computed for each patient using the web-based calculator. RESULTS The group with SR had a lower frequency of hyperlipidemia and a higher ejection fraction (EF) at admission. Conversely, the group without SR presented with higher values of glucose, troponin, creatine kinase-myocardial band (CK-MB), and PRECISE-DAPT score. The no-reflow phenomenon and elevated SYNergy between PCI with TAXUS and Cardiac Surgery (SYNTAX-I) scores were higher in the group without SR than in the one with SR. Multivariate regression analysis indicated that a high PRECISE-DAPT score was an independent predictor of the absence of SR (odds ratio: 0.96, P = 0.04). CONCLUSION The PRECISE-DAPT score is an independent predictor of the presence of spontaneous reperfusion in patients who experienced STEMI.
Type 2 myocardial infarction.

The study by Montone et al. provides important insights into the possible clinical utility of invasive coronary provocative tests in patients with myocardial infarction and non-obstructive coronary arteries. Unfortunately, the use of the term type 2 myocardial infarction (T2MI) throughout their manuscript and particularly in Figure 1 is not in agreement with the universal definition of myocardial infarction. May we ask the authors to revise their wording accordingly in the online version. While there is an on-going controversy whether the presence of coronary artery disease should be (re)introduced as a criterion for T2MI with recent data strongly in favour of this addition, there is unequivocal consensus that cardiac disorders with a pathophysiology different from supply demand mismatch including cardiotoxicity, myocarditis, and takotsubo cardiomyopathy should not be termed T2MI. We consider the distinction of myocardial infarction (MI) from other causes of cardiomyocyte injury of major clinical relevance in order to avoid the inappropriate initiation of therapies shown to be beneficial in patients with MI such as intensive platelet inhibition to patients with other disorders in whom such therapy would induce more harm than benefit. The incorrect terminology applied even by experts such as Montone et al. further highlights the important educational tasks for the task force of the universal definition of myocardial infarction. In addition, it is important to reiterate current guidelines that both a type 1 myocardial infarction (T1MI) mechanism (plaque rupture, ulceration, fissuring, and coronary dissection) may underlie MI in patients with non-obstructive coronary arteries, as well as T2MI mechanisms. May we ask the authors to discuss their clinical algorithm on how to do this important differential diagnosis. Again, this differentiation has major clinical implications as patients with T1MI would be expected to benefit from intense platelet inhibition whereas patients with T2MI would not.
AV4GAInsp: An Efficient Dual-Camera System for Identifying Defective Kernels of Cereal Grains

Grain Appearance Inspection (GAI) is a pre-requisite for grain quality determination, providing guidance for grain processing, storage, and trade. GAI is routinely performed by trained inspectors who are required to visually inspect cereal grains for each individual kernel. Since grain kernels (e.g., wheat, rice) are tiny with heterogeneous shapes and appearance, manually performing GAI is time-consuming and error-prone. This letter presents a machine vision-based customization of an automated system for grain appearance inspection, called AV4GAInsp, which consists of a device and an analysis framework. The device is equipped with an elaborate feeding module and a capturing module for automatically pre-processing grain kernels and efficiently acquiring high-quality images for these kernels. The framework employs deep convolutional neural networks to process these captured images to classify the kernels as normal or defective. We also built and released a large-scale dataset, named GrainDet, that includes over $\text{140}\text{K}$ images for three types of grains: wheat, sorghum, and rice. Comprehensive experiments are conducted to validate the efficacy and performance of our AV4GAInsp system, achieving an average F1-score of 98.4% and excelling at inspection efficiency by over 20× speedup. Kappa statistic tests are performed to confirm the consistency between our system and human experts. It is expected that AV4GAInsp will alleviate inspectors' workloads and inspire further research in smart agriculture.
Eight-Element Dual-Band Multiple-Input Multiple-Output Mobile Phone Antenna for 5G and Wireless Local Area Network Applications

This paper proposes an eight-element dual-band multiple-input multiple-output (MIMO) antenna that operates in the fifth generation (5G), n78 (3400–3600 MHz), and WLAN (5275–5850 MHz) bands to accommodate the usage scenarios of 5G mobile phones. The eight antenna elements are printed on two long frames, which significantly reduce the usage of the internal space of the mobile phone. Each antenna element is printed on both surfaces of one frame, which consists of a radiator on the internal surface and a defected ground plane on the outer surface. The radiator is a rectangular ring fed by a 50 Ω microstrip line which is printed on the top surface of the system board. A parasitic unit is printed on the outer surface of each frame, which is composed of an inverted H-shaped and four L-shaped patches. Each parasitic unit is connected to the internal surface of the frames through a via, and then it is connected to a 1.5 mm wide microstrip line on the top surface of the system board, which is connected to the ground plane on the bottom surface of the system board by a via. Four L-shaped slots, four rectangular slots, and four U-shaped slots are etched onto the system board, which provides good isolation between the antenna elements. Two merged rectangular rings are printed on the center of each frame, which improves the isolation further. The return loss is better than 6 dB, and the isolation between the units is better than 15 dB in the required working frequency bands. In addition, the use of a defected ground structure not only makes the antenna element obtain better isolation but also improves the overall working efficiency. The measurement results show that the proposed MIMO antenna structure can be an ideal solution for 5G and WLAN applications.
A New Method for Revealing Traffic Patterns in Video Surveillance using a Topic Model

—Research on video surveillance systems, for instance, in intelligent transportation systems, has advanced due to the growing requirement for monitoring, control, and intelligent management. One of the next issues is extracting patterns and automatically classifying them, given the volume of data produced by these systems. In this study, a theme approach was utilized to translate visual patterns into visual words in order to reveal and extract traffic patterns at crossings. The supplied video is first cut up into segments. The optical flux technique is then used to determine the clips' optical flux characteristics, which are based on a lot of local motion vector data, and translate them into visual words. The thin-group thematic coding method is then used to teach traffic patterns to the proposed system using a non-probable thematic model. By responding to a behavioral query like "Where is a vehicle going? " these patterns convey observable motion that can be utilized to characterize a scene. The results of applying the suggested method to the QM_UL video database demonstrated that the suggested method can accurately identify and depict significant traffic patterns such as left turns, right turns, and intersection crossings.
Altered Gene Expression of the Parasitoid Pteromalus puparum after Entomopathogenic Fungus Beauveria bassiana Infection

Both parasitoids and entomopathogenic fungi are becoming increasingly crucial for managing pest populations. Therefore, it is essential to carefully consider the potential impact of entomopathogenic fungi on parasitoids due to their widespread pathogenicity and the possible overlap between these biological control tools during field applications. However, despite their importance, little research has been conducted on the pathogenicity of entomopathogenic fungi on parasitoids. In our study, we aimed to address this knowledge gap by investigating the interaction between the well-known entomopathogenic fungus Beauveria bassiana, and the pupal endoparasitoid Pteromalus puparum. Our results demonstrated that the presence of B. bassiana significantly affected the survival rates of P. puparum under laboratory conditions. The pathogenicity of B. bassiana on P. puparum was dose- and time-dependent, as determined via through surface spraying or oral ingestion. RNA-Seq analysis revealed that the immune system plays a primary and crucial role in defending against B. bassiana. Notably, several upregulated differentially expressed genes (DEGs) involved in the Toll and IMD pathways, which are key components of the insect immune system, and antimicrobial peptides were rapidly induced during both the early and late stages of infection. In contrast, a majority of genes involved in the activation of prophenoloxidase and antioxidant mechanisms were downregulated. Additionally, we identified downregulated DEGs related to cuticle formation, olfactory mechanisms, and detoxification processes. In summary, our study provides valuable insights into the interactions between P. puparum and B. bassiana, shedding light on the changes in gene expression during fungal infection. These findings have significant implications for the development of more effective and sustainable strategies for pest management in agriculture.