Threshold Verification Technique for Network Intrusion Detection System

Internet has played a vital role in this modern world, the possibilities and opportunities offered are limitless. Despite all the hype, Internet services are liable to intrusion attack that could tamper the confidentiality and integrity of important information. An attack started with gathering the information of the attack target, this gathering of information activity can be done as either fast or slow attack. The defensive measure network administrator can take to overcome this liability is by introducing Intrusion Detection Systems (IDSs) in their network. IDS have the capabilities to analyze the network traffic and recognize incoming and on-going intrusion. Unfortunately the combination of both modules in real time network traffic slowed down the detection process. In real time network, early detection of fast attack can prevent any further attack and reduce the unauthorized access on the targeted machine. The suitable set of feature selection and the correct threshold value, add an extra advantage for IDS to detect anomalies in the network. Therefore this paper discusses a new technique for selecting static threshold value from a minimum standard features in detecting fast attack from the victim perspective. In order to increase the confidence of the threshold value the result is verified using Statistical Process Control (SPC). The implementation of this approach shows that the threshold selected is suitable for identifying the fast attack in real time.Comment: 8 Pages, International Journal of Computer Science and Information Securit

MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models

Information extraction and textual comprehension from materials literature are vital for developing an exhaustive knowledge base that enables accelerated materials discovery. Language models have demonstrated their capability to answer domain-specific questions and retrieve information from knowledge bases. However, there are no benchmark datasets in the materials domain that can evaluate the understanding of the key concepts by these language models. In this work, we curate a dataset of 650 challenging questions from the materials domain that require the knowledge and skills of a materials student who has cleared their undergraduate degree. We classify these questions based on their structure and the materials science domain-based subcategories. Further, we evaluate the performance of GPT-3.5 and GPT-4 models on solving these questions via zero-shot and chain of thought prompting. It is observed that GPT-4 gives the best performance (~62% accuracy) as compared to GPT-3.5. Interestingly, in contrast to the general observation, no significant improvement in accuracy is observed with the chain of thought prompting. To evaluate the limitations, we performed an error analysis, which revealed conceptual errors (~64%) as the major contributor compared to computational errors (~36%) towards the reduced performance of LLMs. We hope that the dataset and analysis performed in this work will promote further research in developing better materials science domain-specific LLMs and strategies for information extraction

THE INFLUENCES OF JOB PERFORMANCE, WORK-LIFE BALANCE AND ORGANIZATIONAL JUSTICE ON EMPLOYEES’ CAREER SATISFACTION

Purpose of study: This study is conducted to identify the relationships between job performance, work-life balance and organizational justice towards employee's career satisfaction from one of the manufacturing companies in the north of Malaysia. Methodology: The study is done by utilizing a random sample of 240 employees in the company. Type of investigation is a correlation study and it is cross-sectional on time horizon. The unit of analysis is an individual level; therefore, all employees in the company have chances to serve as the participants in this study. Data has been analysed from 148 respondents. Results: Results show significant and positive relationships between job performance, work-life balance and organizational justice towards employees' career satisfaction. Organizational justice is the most significant factor in career satisfaction in this study (β=.83, p=.00). Other factors such as job performance (β=.71, p=.00), and work-life balance (β=.71, p=.00) also positively correlated with employees’ career satisfaction. Implications/Applications: These significant results imply that managers should provide good elements of justice in the company to raise the level of their employee's career satisfaction. At the same time, the employees should maintain a higher level of job performance as well as to manage the good working life balance in them

Optimizing hopfield neural network for super-resolution mapping

Remote sensing is a potential source of information of land covers on the surface of the Earth. Different types of remote sensing images offer different spatial resolution quality. High resolution images contain rich information, but they are expensive, while low resolution image are less detail but they are cheap. Super-resolution mapping (SRM) technique is used to enhance the spatial resolution of the low resolution image in order to produce land cover mapping with high accuracy. The mapping technique is crucial to differentiate land cover classes. Hopfield neural network (HNN) is a popular approach in SRM. Currently, numerical implementation of HNN uses ordinary differential equation (ODE) calculated with traditional Euler method. Although producing satisfactory accuracy, Euler method is considered slow especially when dealing with large data like remote sensing image. Therefore, in this paper several advanced numerical methods are applied to the formulation of the ODE in SRM in order to speed up the iterative procedure of SRM. These methods are an improved Euler, Runge-Kutta, and Adams-Moulton. Four classes of land covers such as vegetation, water bodies, roads, and buildings are used in this work. Results of traditional Euler produces mapping accuracy of 85.18% computed in 1000 iterations within 220-1020 seconds. Improved Euler method produces accuracy of 86.63% computed in a range of 60-620 iterations within 20-500 seconds. Runge-Kutta method produces accuracy of 86.63% computed in a range of 70-600 iterations within 20-400 seconds. Adams-Moulton method produces accuracy of 86.64% in a range of 40-320 iterations within 10-150 seconds

Accelerated Design of Chalcogenide Glasses through Interpretable Machine Learning for Composition Property Relationships

Chalcogenide glasses possess several outstanding properties that enable several ground breaking applications, such as optical discs, infrared cameras, and thermal imaging systems. Despite the ubiquitous usage of these glasses, the composition property relationships in these materials remain poorly understood. Here, we use a large experimental dataset comprising approx 24000 glass compositions made of 51 distinct elements from the periodic table to develop machine learning models for predicting 12 properties, namely, annealing point, bulk modulus, density, Vickers hardness, Littleton point, Youngs modulus, shear modulus, softening point, thermal expansion coefficient, glass transition temperature, liquidus temperature, and refractive index. These models, by far, are the largest for chalcogenide glasses. Further, we use SHAP, a game theory based algorithm, to interpret the output of machine learning algorithms by analyzing the contributions of each element towards the models prediction of a property. This provides a powerful tool for experimentalists to interpret the models prediction and hence design new glass compositions with targeted properties. Finally, using the models, we develop several glass selection charts that can potentially aid in the rational design of novel chalcogenide glasses for various applications.Comment: 17 pages, 8 figure

In vitro antiproliferative and antioxidant activities of the extracts of Muntingia calabura leaves.

The in vitro antiproliferative and antioxidant activities of the aqueous, chloroform and methanol extracts of Muntingia calabura leaves were determined in the present study. Assessed using the 3,(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) (MTT) assay, the aqueous and methanol extracts of M. calabura inhibited the proliferation of MCF-7, HeLa, HT-29, HL-60 and K-562 cancer cells while the chloroform extract only inhibited the proliferation of MCF-7, HeLa, HL-60 and K-562 cancer cells. Interestingly, all extracts of M. calabura, which failed to inhibit the MDA-MB-231 cells proliferation, did not inhibit the proliferation of 3T3 (normal) cells, indicating its safety. All extracts (20, 100 and 500 μg/ml) were found to possess antioxidant activity when tested using the DPPH radical scavenging and superoxide scavenging assays with the methanol, followed by the aqueous and chloroform, extract exhibiting the highest antioxidant activity in both assays. The total phenolic content for the aqueous, methanol and chloroform extracts were 2970.4 ± 6.6, 1279.9 ± 6.1 and 2978.1 ± 4.3 mg/100 g gallic acid, respectively. In conclusion, the M. calabura leaves possess potential antiproliferative and antioxidant activities that could be attributed to its high content of phenolic compounds, and thus, needs to be further explored

Synthesis and characterization of (zinc-layered-gallate) nanohybrid using structural memory effect

The memory effect of calcined zinc hydroxide nitrate, with gallate anion solutions, was studied. The layered hydroxide salt material, zinc hydroxide nitrate was heat-treated at 150–800 °C. XRD analysis showed the growth of the calcined materials in both thickness and diameter occurring simultaneously with increasing calcination temperature. Surface area analysis confirmed this growth. The rehydration behavior of the calcined material was investigated by placing the material in a solution containing gallate anions. The best result for layered hydroxide salt phase reconstruction was obtained for a sample heated at 500 °C and treated with 0.1 mol L−1 anion. PXRD analysis showed the formation of a layered structure material after rehydration process. FTIR and TG confirmed the formation of the host–guest nanohybrid material produced

Historical and current legislations of Taman Negara National Park Peninsular Malaysia

The study was conducted to discuss the historical and current legislation pertaining to the establishment and administration of the Taman Negara National Park, Peninsular Malaysia. Established in 1938 and 1939 as King George V National Park, the park was named Taman Negara National Park after independent in 1957. Estimated to be 130 million years old and with an area of 4,343 sq kilometers, the highest mountain in the peninsular, Gunung Tahan (2,187 meter) is allocated in the area. Taman Negara National Park is a combination of three protected areas in three states, Taman Negara Pahang National Park, Taman Negara Kelantan National Park and Taman Negara Terengganu National Park. Currently all the three states has its own legislation, namely Taman Negara Enactment (Pahang) No.2, 1939 [En.2 of 1938], Taman Negara Enactment (Kelantan) No.14, 1938 [En.14 of 1938] and Taman Negara Enactment (Terengganu) No.6, 1939 [En.6 of 1358]. In Malaysia, some laws are federal legislation. Others are state enactments. Not all legislation enacted will apply to the whole Peninsular, the state of Sabah and Sarawak. To provide for the establishment and control of National Parks and for matters connected herewith, the Federal National Parks Act (Act 226) was introduced in 1980. This federal act shall not apply to the three states. Since this is the constitutional position, constraints especially on uniformity of laws either to promote or enforced, particularly in respect matters stated and List 1 Federal List (Ninth Schedule of Article 74, 77 Legislative Lists), List II – State List (Article 95B (1) (a) and List III – Concurrent List (Article 95B (1) (b) often exists. Thus, there are some matters which the National Parks fall under the legislative authority of both the Federal and State Governments. However, forestry and land fall under the jurisdiction and legislative authority of the state in accordance with the Concurrent List of the Ninth Schedule. The areas of jurisdiction of Federal and State Governments as defined in the Constitution lead to non-uniform implementation of rules and regulations between states. The objective of this paper is to review the laws and legislation pertaining to the management of the National Park in Peninsular Malaysia. Specifically the constraints arises between the federal and states jurisdiction toward the management of land and conservation of the protected area

Elucidating the constitutive relationship of calcium–silicate–hydrate gel using high throughput reactive molecular simulations and machine learning

Prediction of material behavior using machine learning (ML) requires consistent, accurate, and, representative large data for training. However, such consistent and reliable experimental datasets are not always available for materials. To address this challenge, we synergistically integrate ML with high-throughput reactive molecular dynamics (MD) simulations to elucidate the constitutive relationship of calcium–silicate–hydrate (C–S–H) gel—the primary binding phase in concrete formed via the hydration of ordinary portland cement. Specifically, a highly consistent dataset on the nine elastic constants of more than 300 compositions of C–S–H gel is developed using high-throughput reactive simulations. From a comparative analysis of various ML algorithms including neural networks (NN) and Gaussian process (GP), we observe that NN provides excellent predictions. To interpret the predicted results from NN, we employ SHapley Additive exPlanations (SHAP), which reveals that the influence of silicate network on all the elastic constants of C–S–H is significantly higher than that of water and CaO content. Additionally, the water content is found to have a more prominent influence on the shear components than the normal components along the direction of the interlayer spaces within C–S–H. This result suggests that the in-plane elastic response is controlled by water molecules whereas the transverse response is mainly governed by the silicate network. Overall, by seamlessly integrating MD simulations with ML, this paper can be used as a starting point toward accelerated optimization of C–S–H nanostructures to design efficient cementitious binders with targeted properties