Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Explainable Multi-view Representations for Malware Authorship Attribution

Malware poses an ever-growing threat to organizations, governments, and institutions. To effectively combat its proliferation and development by threat actors, identifying the authors of malware is a crucial step. Existing efforts in malware authorship attribution often focus on extracting stylistic features from source code. However, malware source code is frequently limited in availability. There is a strong need to develop effective methods to directly extract a robust and salient representation from malware binaries. This representation should adeptly capture the inherent characteristics of the binaries to facilitate the identification of malware authors. In our work, we introduce an approach that leverages multi-view graph representation learning for malware authorship attribution. We extract various intermediate representations from the binary, including control flow graphs and function call graphs. These representations are encoded using GNN-based functions and fused to create a comprehensive malware representation. Additionally, we introduce GNN-explainability approaches to identify subgraphs indicative of class membership. Our experiments on a recent benchmark demonstrate significant performance enhancements in authorship attribution.
Intrusion Detection in IoT Platform Using Tuna Swarm Optimization with Long Short-Term Memory

The Internet of Things (IoT) is a dynamic and delightful research field in this emerging technology. It can be globally connected with many IoT devices and exchange a large amount of data. However, the threats also developed and misguided the entire network behaviour. This article proposes an Intrusion Detection System (IDS) using the proposed Long Short-Term Memory (LSTM) along with the Tuna Swarm Optimization (TSO) to fine tune the hyperparameters and helps to enhance the detection accuracy of attacks that takes place in IoT environment. The data is obtained from NSL-KDD data set and it is pre-processed using Synthetic Minority Over-Sampling Technique (SMOTE) and it is fed into the stage of feature extraction which is performed using the Recursive Feature Elimination (RFE). After this, the feature selection is performed using optimization-based feature selection approach based on Moth Flame Optimization (MFO) and finally, the detection of attack takes place with the help of TSO-LSTM where the results exhibited by the LSTM are fine-tuned with the help of TSO which helps to enhance the detection accuracy of the model. The detection accuracy of the proposed TSO-LSTM is 99.98% which is comparably higher than the existing approaches such as DBN, SMO-HPSO, CNN-LSTM and BFO-RF with detection accuracy of 99.79%, 99.17%,98.8% and 99.96% respectively.
Vagus Nerve Stimulation in Patients With Refractory Epilepsy: a case series

Approximately 33% of patients with epilepsy do not respond to treatment with a single antiepileptic drug. Many of these patients can benefit from neurosurgical treatment. However, not all patients are candidates for surgery, and in these cases vagus nerve stimulation (VNS) presents as a good therapeutic option. Objectives: Evaluation of efficiency, tolerability and safety in the control of seizures after VNS implantation in patients from a reference hospital. Methods: cross-sectional study examined 20 patients who underwent implantation of the VNS in our facilities between 2007 and 2014. Proportions and chi-square test were applied (statistical significance level set to p ≤ 0.05). Results: Mean follow-up was 31.3 months. Seizure frequency decreased in 55% of the patients. Among them, 11 patients believed that VNS had improved their quality of life. The mean reduction reported by other patients was 78.3%. More than 30% reported no adverse effects. About 70% of patients no longer required hospitalizations and 95% reported reduction in the number of hospitalizations. Conclusion: In this population, VNS proved to be a good option in the treatment of refractory epilepsy and also in providing a significant improvement in quality of life, (reduction of seizures and hospitalizations), mood, attention and memory, which directly reflects on their social and cognitive.
An Early and Smart Detection of Corn Plant Leaf Diseases Using IoT and Deep Learning Multi-Models

Plant leaf diseases have various causes, leading to severe disorders. The early and accurate detection and classification of these diseases are fundamental for fostering healthy crop production. In recent years, smart agricultural systems have garnered significant attention due to their capability to enhance efficiency by deploying sensor networks and Internet of Things (IoT) devices that collect and analyze environmental data. However, traditional plant disease detection methods are manual, time-consuming, and often need help handling the data’s complexity and dynamism. These manual methods do not use heterogeneous data to make better decisions. Corn holds significant importance yet it faces numerous diseases that include main three diseases such as blight, common rust, and grey leaf spot. The advancement of computer technology has led to a pivotal focus on corn leaf diseases classification application based on deep learning. Convolutional Neural Networks (CNNs) have revealed remarkable achievements within Precision Agriculture (PA) due to their ability to enhance information. To this end, this work introduces a CNN-based architecture, the Multi-Model Fusion Network (MMF-Net). Its primary objective is to classify diseases within the realm of PA. MMF-Net integrates multi-contextual features using RL-block and PL-blocks 1 & 2, thus effectively combining different model streams trained on heterogeneous data. The RL-block uses spatial range to process coarse grained images to convolve the local context, while PL-block 1 extracts fine-grained global context by expanding the perceptual area of images. PL-block 2 deals with real-life environmental parameters as features. The extracted features are syndicated using multiple classifiers that ensemble three individual blocks at the decision level to improve the accuracy. After fusion, it uses adaptively the majority voting scheme to generate the final decision probability score of the base model. Multiple experiments are conducted involving the corn leaf diseases dataset and a real-life numerical dataset, generating an impressive 99.23% accuracy in the classification of corn leaf diseases. Overall, MMF-Net provides a promising and smart solution to identify plant leaf diseases in PA effectively.
Subsurface architecture of two tropical alpine desert cinder cones that hold water

Basaltic lava is generally porous and cannot hold water to form lakes. Here we investigate two impermeable cinder cones in the alpine desert of Maunakea volcano, Hawaii. We present the results of the first ever geophysical survey of the area around Lake Waiau, the highest lake on the Hawaiian Islands, and establish the existence of a second body of standing water in a nearby cinder cone, Pu‘upōhaku (~4000 m above sea level), which has a sporadic pond of water. Based on unpublished field notes from Alfred Woodcock (*1905–†2005) spanning the years 1966–1977, more recent observations, and our own geophysical survey using electric resistivity tomography, we find that perched groundwater resides in the crater perennially to a depth of 2.5 m below the surface. Hence, Pu‘upōhaku crater hosts a previously unrecognized permanent body of water, the highest on the Hawaiian Islands. Nearby Lake Waiau is also perched within a cinder cone known as Pu‘uwaiau. Among other hypotheses, permafrost or a massive block of lava were discussed as a possible cause for perching the water table. Based on our results, ground temperatures are too high and specific electric resistivity values too low to be consistent with either ice‐rich permafrost or massive rock. Fine‐grained material such as ash and its clay‐rich weathering products are likely the impermeable material that explains the perched water table at both study sites. At Pu‘uwaiau we discovered a layer of high conductivity that may constitute a significant water reservoir outside of the lake and further be responsible for perching the water toward the lake.
IstraŽivanje korisnika u Narodnoj knjinici Dubrovnik

User studies are popular way of measuring success in many libraries. These studies often help libraries to plan the work organization and to improve the quality of their services. In Dubrovnik Public Library, due to the recent informatization, also needs certain changes in its present organisation and performing services to the users. The pilot-survey made in summer 2000 among the library users shows their satisfaction with the present situation and their expectations of the library in the future. The used model and methodology in this pilot-survey can serve for the needs of the next complete user study which perhaps will contribute to achieve clearer answers to the questions imposed to the libraries as they are faced with the new changes and challenges.
A Hybrid Meandered Line-Slot Patch Antenna for On- and In-Metal RFID Tag Design

A novel hybrid patch antenna that is embeddable in metal is proposed for radio frequency identification (RFID) tagging applications. The antenna features a simple design, consisting of a meandered line and a complementary slot surrounding a shorted radiating patch. This hybrid patch antenna can easily achieve high compactness by tuning the dimensions of the meandered line. The complementary slot is also playing a crucial role in enhancing the broadside radiation by promoting fringing fields from the radiating patch. As a result, the tag antenna becomes less sensitive to the cavity walls at surrounding. The proposed tag antenna, with dimensions of 30 × 30 × 3.2 mm3, can achieve high realized gain of −0.6 dBi when placed on a 200 × 200 × 20 mm3 metal block or in a 100 × 100 × 10 mm3 recessed metallic cavity. Although reducing the cavity’s size and depth can cause antenna’s realized gain to deteriorate to −2.5 dBi, the antenna’s resonant frequency remains stable at 0.915 GHz across all simulations.
Infrared and Visible Image Fusion via Variational Bayesian Approximation Method

The integration of infrared and visible images has become increasingly relevant in the field of remote sensing technologies and image processing applications. This technique has proven beneficial in scenarios such as thermal source monitoring and anomaly detection for industrial machines. The fusion of these two images allows for the creation of a compound image that contains important information from both sources, resulting in an enhanced image with rich background details and clear targets. This paper presents a model for image fusion and probabilistic generation of infrared and visible images. The variational Bayesian approximation method is utilized to fuse the information from both images. The proposed method also introduces a hierarchical prior model using wavelet transform and a variational Bayesian inference algorithm to achieve information fusion. The proposed method provides a feasible and effective solution for health monitoring applications of high-speed direct-drive blowers in industrial scenarios.
Distribution and ecophysiology of Acacia species in south western zone of Saudi Arabia

The thesis is a description of the patterns and distribution of Acacia trees in Saudi Arabia and an evaluation of the morphological and physiological responses of seedlings to limitation in soil water availability. Species examined were Acacia ehrnbergiana Hayne, Acacia asak (Forssk.) Willd, and Acacia negrii Pinchi-Sermoli. The objective was to investigate the environmental factors controlling the distribution of these species in natural communities in south western Saudi Arabia. Fifteen major community types constitute the major part of the natural vegetation of the study area and are dominated by the Acacia species: A. negrii, A. gerradii, A. asak, A. seyal, A. nubica, A. homulosa, A. ehrnbergiana, A. laeta, and A. tortilis. Also present are Ziziphus spina-christi, Juniperus procera, Tamarix aphylla, Hyphaen thebaica, Olea europea and Ficus salicifolia. The form of trees in the region is controlled by rainfall and by factors affecting the distribution and the availability of water, such as topography and soil texture. Acacia species showed clear differences along these environmental gradients. Juniperus procera has the highest number of trees per site whilst Tamarix aphylla has the lowest density. In the laboratory, seeds may be treated to enhance germination. Seed scarified by abrasion or acid treatment, and seed exposed to a heat shock showed stimulated germination. The duration of exposure to moist conditions required to make the testa permeable and to initiate germination of the seeds was ranked as follows: A. ehrnbergiana > A. tortilis > A. seyal > A. asak > A. negrii. The effect of drought on water relations was studied on plants grown in long soil columns for Acacia negrii, A. ehrnbergiana and A. asak. Water deficit resulted in active osmotic adjustment in leaves, with decreases in osmotic potential at Ri! l and zero turgor, and increased bulk elastic modulus and leaf dry weight to turgid weight ratio. Stomatal conductance was correlated with soil water status. The effects of different water regimes on the root growth development of three Acacia species was studied. This showed they had the ability to grow tap roots which penetrate deep into the water table. The total weight of the root for the dry and intermediate treatments were higher than for the wet treatment (control), and both had greater root lengths than the wet treatment. The increase in root weight was mainly due to a substantial shift in assimilates allocated in favour of roots with total biomass being unaffected. Photosynthetic rates in Acacia negrii and Acacia ehrnbergiana seedlings decreased with declining soil water content. Photosynthesis was reduced by 60% and 55% for Acacia negrii and Acacia ehrnbergiana respectively after 8 days of the drought period. The more mesic Acacia negrii exhibited higher water use efficiency
Prevalence of Metabolic Syndrome among Patients with Acute Myocardial Infarction at Al-Ramadi Teaching Hospital, Ramadi City, Iraq

Background: Acute myocardial infarction (AMI) and metabolic syndrome (MeS) are two major health problems worldwide. In recent years, MeS has been introduced as a signiﬁcant risk factor for AMI. Objectives: To estimate the prevalence of MeS and its components among patients with AMI. Materials and methods: This was a cross-sectional study conducted at Al-Ramadi Teaching Hospital during a period from January to December 2022, including a total of 100 patients with AMI. The deﬁnition of MeS was established based on the criteria of the modiﬁed National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III). Results: The mean age of patients was 58.6 ± 7.1 (range: 41–81 years). Males represented 55% of the studied group. Clustering of MeS components among cases revealed that 57% had less than three components while 43% had 3 or more criteria for MeS diagnosis, giving a prevalence rate of 43% for MeS. Only 5 cases had none of these components. MeS was relatively more prevalent in younger age groups and females. The mean values of all components of MeS were signiﬁcantly higher in the MeS group than in the non-MeS group (P-value < 0.05). Conclusion: MeS was an important risk factor for AMI. The study revealed a high prevalence of MeS among individuals with AMI and it was higher among women than men.
Does water context influence behaviour and attitudes to water conservation?

Many rural and urban areas around the world are facing challenges to the supply of water. A key method of addressing water shortage is water conservation. The success of conservation measures depends on public support and behaviour change. While it is known that the public is generally supportive of water conservation measures, little is known about the dependence of water conservation attitudes and behaviour on geographical location and the water situation at specific locations. The present study investigates whether individual attitudes to water conservation, and reported participation in water conservation behaviours, differ between two Australian locations that vary significantly in their water situation. The first location, Darwin, is an urban location with a water surplus, whereas the second location, the Mallee in north-western Victoria, is a rural environment that has experienced an extended period of drought. Results indicate that there are significant differences in attitude and participation between the two areas. Significantly more people from the water-scarce location are supportive of most water conservation behaviours, and they are significantly more likely to state that they participate in water conservation behaviours. Implications for water policy are discussed.
A New Formula for Faster Computation of the K-Fold Cross-Validation and Good Regularisation Parameter Values in Ridge Regression

In the present paper, we prove a new theorem, resulting in an update formula for linear regression model residuals calculating the exact k-fold cross-validation residuals for any choice of cross-validation strategy without model refitting. The required matrix inversions are limited by the cross-validation segment sizes and can be executed with high efficiency in parallel. The well-known formula for leave-one-out cross-validation follows as a special case of the theorem. In situations where the cross-validation segments consist of small groups of repeated measurements, we suggest a heuristic strategy for fast serial approximations of the cross-validated residuals and associated Predicted Residual Sum of Squares (<inline-formula> <tex-math notation="LaTeX">$PRESS$ </tex-math></inline-formula>) statistic. We also suggest strategies for efficient estimation of the minimum <inline-formula> <tex-math notation="LaTeX">$PRESS$ </tex-math></inline-formula> value and full <inline-formula> <tex-math notation="LaTeX">$PRESS$ </tex-math></inline-formula> function over a selected interval of regularisation values. The computational effectiveness of the parameter selection for Ridge- and Tikhonov regression modelling resulting from our theoretical findings and heuristic arguments is demonstrated in several applications with real and highly multivariate datasets.
Case studies in fisheries self-governance

This FAO Fisheries Technical Paper documents 32 case studies and four syntheses (Canada, Japan, New Zealand and the United States of America) on the role of industry in the governance and management of fisheries. The studies are drawn from ongoing practice in Europe, North America, Japan and Australasia. The types of fisheries cover those for crustaceans, fish, molluscs and echinoderms. In general the scale of the fisheries tends to be small, which has been one of the reasons attributed to their success. In all but one case it is clear that well-defined fishery rights have contributed to the success of the programmes though the initiative for development and adoption of the programmes covers a range of institutional causes. The case studies are intended to inform and provide potential models that may be used in other fisheries. Townsend, R. ; Shotton, R. ; Uchida, H. (eds). Case studies in fisheries self-governance. FAO Fisheries Technical Paper. No. 504. Rome, FAO. 2008. 45 p.
Potensi pemanfaatan teknologi 5g guna mendukung pembelajaran daring

Pandemi Covid-19 sangat berpengaruh terhadap beberapa sektor. Salah satunya adalah sektor pendidikan. Dunia pendidikan mengalami perubahan yang sangat signifikan semenjak pandemi Covid-19 melanda. Proses kegiatan belajar mengajar luring beralih menjadi pembelajaran daring. Maka pendidikan saat ini sangat membutuhkan akses jaringan yang reliabel. Hadirnya teknologi 5G berpotensi untuk menjawab tantangan tersebut. Teknologi 5G menjanjikan kecepatan yang mencapai 1 Gbps, latency sub-milisekon dan jangkauan sinyal yang lebih baik. Selain itu, teknologi 5G juga mendukung Augmented Reality atau Virtual Reality yang dapat mendukung kegiatan pembelajaran berbasis teknologi dalam rangka digitalisasi dunia pendidikan. Untuk mencapai hal tersebut, dibutuhkan bandwidth yang lebih lebar, round trip time yang lebih kecil dan penggunaan antenna Massive-MIMO. Paper ini bertujuan untuk mengetahui potensi pemanfaatan Teknologi 5G dan teknologi pembangunnya guna mendukung pembelajaran daring yang reliabel. Sehingga pembelajaran daring dapat dilakukan dimanapun, tanpa hambatan dan gangguan internet.
A Study on Employee Turnover and Motivating Factors in Safaricom Ltd

The study aimed to investigate the relationship between on-the-job training and employee turnover in Safaricom Ltd, employing a case study research strategy. The research targeted 67 employees from job groups 4 and 5, selected through stratified proportionate sampling, with the sole research tool being a questionnaire. The findings revealed the significance of on-the-job training in Safaricom Ltd, emphasizing the need for organizational investment. However, it also highlighted that such training often leads to employee turnover due to factors such as the pursuit of higher pay, competition from other organizations seeking trained skills, aspirations for promotion and improved working conditions, and the breach of psychological contracts post-training. The consequences of turnover for organizations include revenue loss, delays in achieving organizational goals, diminished competitiveness, and skill shortages. Importantly, the study established a clear connection between on-the-job training and employee turnover, leading to several recommendations. Managers are urged to thoroughly understand the impact of on-the-job training on turnover before initiating such programs, facilitating the implementation of measures to mitigate turnover consequences. These measures may include budgeting for increased pay, implementing strategies to discourage turnover, and revising employment contracts. Additionally, well-planned training programs can anticipate turnover by preparing for suitable replacements, and organizations can minimize losses associated with training-related turnover by hiring individuals with existing requisite skills.
A Study of Climate Variability and Socioeconomic Impact on Tourism Industry of Nepal

In Nepal, tourism is the second largest industry after agriculture. Furthermore, Nepal offers numerous opportunities for adventurous outdoor recreation, especially in the Himalayas, and attracts tourists from all over the World. However, future prosperity of Nepal’s tourism industry might be affected by a changing climate and a socioeconomic environment. A log-level regression model is employed to estimate the impact of climatic and socioeconomic factors on the number of international tourists visiting Nepal. Climatic estimates indicated that an increase in temperature did not have a significant impact on the numbers of international tourist arrival. Results show that the number of international tourists visiting Nepal was positively associated with GDP and population growth, inflation rate, and an exchange rate. These results help policy makers for facilitating growth of tourism industry and its adaptability to climate change in Nepal.
Flash ADC‐based digital LDO with non‐linear decoder and exponential‐ratio array

This Letter presents a digital low-dropout regulator (LDO) that achieves fast transient response with a flash ADC-based parallel comparison. A non-linear decoder is employed in the flash ADC to further enhance the transient response. In the design of the power switch array, an exponential-ratio array (ERA) is adopted for high load driving capacity. The proposed digital LDO implemented in 65-nm CMOS achieves a load range of 0.04-82.7 mA when the input voltage and the output voltage are 1.0 and 0.9 V, respectively. The digital LDO achieves a settling time of 6 μs with a load step of 48 mA, exhibiting the state-of-the-art normalised settling time for the clock frequency of 1 MHz.