Miracle-dz/UniFAR-FTUs huggingface dataset:
Transform Domain Techniques combined with LBP for Tampered Image Detection using Machine Learning

Detection of tampered images is very important for ensuring the authenticity of visual information in the domain of digital image forensics. In the context of machine learning, this research proposes methods for detecting tampered images using transform domain techniques such as discrete cosine transform (DCT), discrete wavelet transform (DWT). These techniques are combined with local binary pattern (LBP), which is effective at capturing local information. Dimensionality reduction is achieved with the help of principal component analysis (PCA) to reduce the number of features and analyzed the performance in terms of accuracy and time. Experiments have been conducted on CASIA 2.0 image tampering detection dataset which consists copy-move and splicing forgery images. Various classifiers, including Support Vector Machine (SVM), Random Forest (RF), XG Boost, and Light GBM, are used to train machine learning models on the retrieved features. Based on the accuracy, precision, recall, fl-score, and execution time of these classifiers, the effectiveness of tamper detection is evaluated.
Towards Natural and Intuitive Human-Robot Collaboration based on Goal-Oriented Human Gaze Intention Recognition

The objective of this paper is to introduce a new method for predicting human gaze intention using a headmounted display, with the aim of enabling natural and intuitive collaboration between humans and robots. Human eye gaze is strongly linked to cognitive processes and can facilitate communication between humans and robots. However, accurately identifying the goal-directed object through human intention remains challenging. This study focuses on developing a method to differentiate between goal and non-goal gaze by creating an area of interest (AOI) on each object through the goaldirected gaze. The Microsoft HoloLens 2 was used to simulate the robot using real-time gaze data in augmented reality (AR). The methods with and without AOI were compared through pickand-place robot manipulation through human gaze prediction. The AOI method resulted a maximum improvement of 19% in the F1 score compared to the baseline method. The results yield strong evidence on intuitiveness and usefulness that the use of pre-defined AOI allows improved performance to predict gaze intention that has the potential to be applied in various fields, where human-robot collaboration can enhance efficiency and productivity.
Diverse Characteristics of U.S. Summer Heat Waves

AbstractHeat waves are climate extremes having significant environmental and social impacts. However, there is no universally accepted definition of a heat wave. The major goal of this study is to compare characteristics of continental U.S. warm season (May–September) heat waves defined using four different variables—temperature itself and three variables incorporating atmospheric moisture—all for differing intensity and duration requirements. To normalize across different locations and climates, daily intensity is defined using percentiles computed over the 1979–2013 period. The primary data source is the U.S. Historical Climatological Network (USHCN), with humidity data from the North American Regional Reanalysis (NARR) also tested and utilized. The results indicate that heat waves defined using daily maximum temperatures are more frequent and persistent than when based on minimum temperatures, with substantial regional variations in behavior. For all four temperature variables, heat waves based on dail.
A Novel Approach for Detecting Defects of Random Textured Tiles Using Gabor Wavelet

In this paper we address the problem of detecting different type of defects on random textured tiles. We adopt Gabor wavelet for analysis of random textured surfaces. Unlike the existing methods which arrange the Gabor filter Bank in such a way that the half-magnitude contour of neighboring filters in frequency domain touch each other, we allows the bandwidth of filters to vary. This flexibility enables the designed filter bank to detect different types of defects. We demonstrate that the proposed method detects defects on random textured tiles better than the conventional Gabor wavelet method. Key word: Texture analysis Defect Detection Random texture tiles Gabor wavelet
Probabilistic grammars for music

We investigate whether probabilistic parsing techniques from Natural Language Processing (NLP) can be used for musical parsing. As in NLP, the main problem in music is ambiguity: several different structures may be compatible with a musical sequence while a listener typically hears only one structure. Our best probabilistic parser can correctly predict 85.9% of the phrases for a test set of 1,000 folksongs from the Essen Folksong Collection.
Assessment of the Surface and Groundwater Quality in Ismailia Canal and its Adjacent Area.

Ismailia Canal, one of the main branches of the Nile River in Egypt, is considered as one of the most important irrigations and drinking water source for Ismailia, Port Said and Suez governorates. The aim of the present study is to determine the water quality in the area of investigation to follow up on the long-term changes in the physical and chemical characteristic of water to find out the present status of the water quality of Ismailia Canal.In order to achieve effluent concentration regulations, the study advises improving the control of garbage dumped into the channel. Set in Law 48/ 1982 for the protection of the Nile River and its waterways against pollution.The widely applied solution to this problem is based on keeping heavy metals below the permissible limits in soil and agricultural crops. The outcome of this study can provide key information on heavy metals, which is useful in achieving sustainable agricultural management. So, water samples were collected from different dimensions and directions from Ismailia Canal, and chemical properties of the collected Surface and groundwater were determined the appropriateness of water for irrigation using various parameters like pH, electrical conductivity, sodium absorption ratio, and water quality index were assessed. The results of samples analysis showed that pH values ranged from 7.1 to 8.1, and electrical conductivity values fluctuated between 0.334 and
Multiple M. tuberculosis Phenotypes in Mouse and Guinea Pig Lung Tissue Revealed by a Dual-Staining Approach

A unique hallmark of tuberculosis is the granulomatous lesions formed in the lung. Granulomas can be heterogeneous in nature and can develop a necrotic, hypoxic core which is surrounded by an acellular, fibrotic rim. Studying bacilli in this in vivo microenvironment is problematic as Mycobacterium tuberculosis can change its phenotype and also become acid-fast negative. Under in vitro models of differing environments, M. tuberculosis alters its metabolism, transcriptional profile and rate of replication. In this study, we investigated whether these phenotypic adaptations of M. tuberculosis are unique for certain environmental conditions and if they could therefore be used as differential markers. Bacilli were studied using fluorescent acid-fast auramine-rhodamine targeting the mycolic acid containing cell wall, and immunofluorescence targeting bacterial proteins using an anti-M. tuberculosis whole cell lysate polyclonal antibody. These techniques were combined and simultaneously applied to M. tuberculosis in vitro culture samples and to lung sections of M. tuberculosis infected mice and guinea pigs. Two phenotypically different subpopulations of M. tuberculosis were found in stationary culture whilst three subpopulations were found in hypoxic culture and in lung sections. Bacilli were either exclusively acid-fast positive, exclusively immunofluorescent positive or acid-fast and immunofluorescent positive. These results suggest that M. tuberculosis exists as multiple populations in most conditions, even within seemingly a single microenvironment. This is relevant information for approaches that study bacillary characteristics in pooled samples (using lipidomics and proteomics) as well as in M. tuberculosis drug development.
Multi-Class Document Classification using LayoutLMv1 and V2

In the age of information explosion, efficient and accurate information retrieval has become a pivotal task across numerous domains, from finance to healthcare and beyond. The LayoutLM model has enhanced the capabilities of existing NLP techniques by enabling them to extract information from complexly structured documents automatically. In this study, we employ a subset of the RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset, consisting of 400,000 pictures of data organized into 16 groups. Out of the 16 classes, this subset includes all the classes but there was a limit set to 200 images per class which makes the total amount of images as 3200. Accordingly, 1920, 640, and 640 images make up the training, validation, and testing sets. This dataset is a rich collection of documents with diverse structures and content to demonstrate the effectiveness of our proposed method. LayoutLM, with its unique capability to analyze and understand document structures, has been a pivotal component of our methodology. We utilized LayoutLMv2 and version 1 for the purpose of classifying the documents into their respective categories and comparing the results accordingly. Accordingly, the two versions' accuracies are 80.94 and 68.75 percents.
The long-term sustainability of household bio-sand filtration

The introduction of intermittently operated slow sand filters, suitable for use at household level, is gaining momentum in the developing world. An estimated 100,000 bio-sand filters are already in use, providing improved drinking water to more than half a million people. Laboratory and field research has shown that bio-sand filters are capable of impressive reductions of turbidity and pathogen levels. However, long-term sustainability, social acceptance and appropriateness have not been well documented. An evaluation was therefore conducted in rural Kenya to measure the performance of filters introduced 4 years previously. Measuring turbidity and E.coli removal rates, maintenance practices and user perceptions, the study found all but one filter were still in use with over 70% producing water of good quality. Underperformance of some filters pointed at poor maintenance habits. As a ‘point-of-use’ water treatment solution, bio-sand filtration seems to be very appropriate, effective and cheap. Strategies to introduce this promising technology at a large scale need to be seriously investigated.
Using Genetic Algorithms to Detect Intrusions for IoT Systems

The purpose of this paper is to develop an intrusion detection system for IoT systems using deep learning and the genetic algorithm to optimize (or, more precisely, compress) the model. To meet the security requirements of IoT systems, we aim to design a powerful IDS system capable of detecting novel attacks, with a lightweight architecture and reduced classification time. We proposed two neural network architecture-based systems: the multi-layer perceptron and the auto-encoder model. The NSL-KDD dataset is utilized for the evaluation of these two systems.
Efficient Computation of the Shapley Value for Game-Theoretic Network Centrality

The Shapley value--probably the most important normative payoff division scheme in coalitional games--has recently been advocated as a useful measure of centrality in networks. However, although this approach has a variety of real-world applications (including social and organisational networks, biological networks and communication networks), its computational properties have not been widely studied. To date, the only practicable approach to compute Shapley value-based centrality has been via Monte Carlo simulations which are computationally expensive and not guaranteed to give an exact answer. Against this background, this paper presents the first study of the computational aspects of the Shapley value for network centralities. Specifically, we develop exact analytical formulae for Shapley value-based centrality in both weighted and unweighted networks and develop efficient (polynomial time) and exact algorithms based on them. We empirically evaluate these algorithms on two real-life examples (an infrastructure network representing the topology of the Western States Power Grid and a collaboration network from the field of astrophysics) and demonstrate that they deliver significant speedups over the Monte Carlo approach. For instance, in the case of unweighted networks our algorithms are able to return the exact solution about 1600 times faster than the Monte Carlo approximation, even if we allow for a generous 10% error margin for the latter method.
A Trigger Enzyme in Mycoplasma pneumoniae: Impact of the Glycerophosphodiesterase GlpQ on Virulence and Gene Expression

Mycoplasma pneumoniae is a causative agent of atypical pneumonia. The formation of hydrogen peroxide, a product of glycerol metabolism, is essential for host cell cytotoxicity. Phosphatidylcholine is the major carbon source available on lung epithelia, and its utilization requires the cleavage of deacylated phospholipids to glycerol-3-phosphate and choline. M. pneumoniae possesses two potential glycerophosphodiesterases, MPN420 (GlpQ) and MPN566. In this work, the function of these proteins was analyzed by biochemical, genetic, and physiological studies. The results indicate that only GlpQ is an active glycerophosphodiesterase. MPN566 has no enzymatic activity as glycerophosphodiesterase and the inactivation of the gene did not result in any detectable phenotype. Inactivation of the glpQ gene resulted in reduced growth in medium with glucose as the carbon source, in loss of hydrogen peroxide production when phosphatidylcholine was present, and in a complete loss of cytotoxicity towards HeLa cells. All these phenotypes were reverted upon complementation of the mutant. Moreover, the glpQ mutant strain exhibited a reduced gliding velocity. A comparison of the proteomes of the wild type strain and the glpQ mutant revealed that this enzyme is also implicated in the control of gene expression. Several proteins were present in higher or lower amounts in the mutant. This apparent regulation by GlpQ is exerted at the level of transcription as determined by mRNA slot blot analyses. All genes subject to GlpQ-dependent control have a conserved potential cis-acting element upstream of the coding region. This element overlaps the promoter in the case of the genes that are repressed in a GlpQ-dependent manner and it is located upstream of the promoter for GlpQ-activated genes. We may suggest that GlpQ acts as a trigger enzyme that measures the availability of its product glycerol-3-phosphate and uses this information to differentially control gene expression.
Pathological evidence reveals cattle traction in North China by the early second millennium BC

The use of cattle labour in antiquity is a worldwide well-discussed topic among researchers as it can shed light on the possible development trajectories of our communities over the past several millennia. Zooarchaeology can play a vital role in illuminating the history of cattle traction through observed pathologies on cattle bones linked to traction activity. Systemic zooarchaeological investigation is still underdeveloped in China, one of the likely early beneficiaries of animal labour exploitation in the world. Here, we apply the pathological index (PI) method, first developed by Bartosiewicz et al. on European assemblages, to Chinese Bronze Age cattle bones. Our results first confirm the wide applicability of the PI method with the involvement of Chinese control samples, which holds the potential to be applied as an effective tool in a larger geographical region. Our results also confirm the importance of cattle traction for the Late Shang states (c. 1300–1046 BC) as previously proposed on the basis of disputed interpretations of oracle bone inscriptions as showing cattle ploughing, but also show that light cattle traction practices likely developed in China in the Bronze Age Erlitou (c. 1750–1530 BC) and Early Shang (c. 1600–1300 BC) periods. Cattle traction use in the Chinese Bronze Age may have facilitated the introduction and subsequent cultivation in China of wheat, an exotic cereal.
Molecular Identification of GemellaSpecies from Three Patients with Endocarditis

ABSTRACT Gemella morbillorum and Gemella haemolysansare opportunistic pathogens which cause endocarditis and other severe infections. We report on three patients with endocarditis, one with endocarditis caused by G. haemolysans and two with endocarditis caused by G. morbillorum. The paucity of reports concerning these bacteria is probably related to the difficulties associated with their identification. For example, one of the strains reported in this study was originally sent to our laboratory with a preliminary characterization as a short “gram-negative” coccobacillus, highlighting the specific problem associated with Gram staining of these bacteria. The usefulness of 16S rRNA gene amplification, partial sequencing, and comparison of the nucleotide sequence to those in databases when standard phenotypic identification schemes are not helpful is emphasized. We also suggest that the use of simple tests, such as testing susceptibility to vancomycin for gram-negative bacteria and colistin for gram-positive bacteria, could prevent misinterpretation of Gram staining in gram-variable bacteria such as Gemella spp.
Matrilysin expression in human prostate carcinoma

The metalloproteinases, a multigene family of metal‐requiring enzymes, have been suggested to play a role in tumor invasion and metastasis. Previously, we demonstrated that human primary prostate tumors express higher levels of matrilysin and gelatinase A mRNA than normal prostate does. In the study presented here, we used in situ hybridization and immunohistochemical staining of serial sections of paraffin‐embedded primary prostate tumors to compare the sites of matrilysin and gelatinase A expression and protein localization. These results confirmed the epithelial nature of matrilysin expression and protein localization. In contrast, gelatinase A mRNA was localized to the interstitial stroma, whereas the protein was associated with the epithelial tumor cells. In situ hybridization was also used to demonstrate that gelatinase B expression was restricted to macrophages infiltrating the tumors. Proteins isolated from an additional set of frozen tumor specimens were analyzed by western blotting to determine the relative amounts of matrilysin in the active and proenzyme forms. The western analyses demonstrated that in all cases in which matrilysin was detected, at least some of the enzyme was in the active form. These results are discussed with respect to the possible role these enzymes may play in prostate tumor progression. © 1996 Wiley‐Liss, Inc.
Simple and fault-tolerant key agreement for dynamic collaborative groups

Secure group communication is an increasingly popular research area having received much attention in recent years. The fundamental challenge revolves around secure and efcient group key management. While centralized methods are often appropriate for key distribution in large groups, many collaborative group settings require distributed key agreement techniques. This work investigates a novel approach to group key agreement by blending binary key trees with DiAEe-Hellman key exchange. The resultant protocol suite is very simple, secure and fault-tolerant. Moreover, its eAEciency surpasses that of prior art.
AMNet: Introducing an Adaptive Mel-Spectrogram End-to-End Neural Network for Heart Sound Classification

The cardiovascular diseases (CVDs) cause tremendous deaths yearly. The Mel-spectrogram is widely used as a tool to analyse the heart sound, which facilitate a cheap and efficient diagnosis of CVDs. Nevertheless, the amplitude and frequency responses of the Mel filter banks remain constant, limiting its function to frequency selection. We propose an adaptive Melspectrogram end-to-end neural network (AMNet) for a better characterisation and classification of heart sound in the work. The core of the adaptive Mel-spectrograms (AMel) lies in an adaptive Mel filter banks whose frequency characteristics remain the same as the original Mel-spectrogram (OMel) and amplitude is learnt by the backropagation algorithm. The AMNet learns the raw audio representation directly and outputs the classification results. It reaches 43.5% Unweighted Average Recall (UAR) and surpasses the model with the OMel and the baseline by 6% UAR. It is demonstrated that the AMel characterises the heart sound more effectively.
UvA-DARE (Digital Academic Repository) Link-based influence maximization in networks of health promotion

The influence maximization problem (IMP) as classically formulated is based on the strong assumption that “chosen” nodes always adopt the new product. In this paper we propose a new influence maximization problem, referred to as the “Link-based Influence Maximization Problem” (LIM), which differs from IMP in that the decision variable of the spreader has changed from choosing an optimal seed to selecting an optimal node to influence in order to maximize the spread. Based on our proof that LIM is NP-hard with a monotonic increasing and submodular target function, we propose a greedy algorithm, GLIM, for optimizing LIM and use numerical simulation to explore the performance in terms of spread and computation time in different network types. The results indicate that the performance of LIM varies across network types. We illustrate LIM by applying it in the context of a Dutch national health promotion program for prevention of youth obesity within a network of Dutch schools. GLIM is seen to outperform the other methods in all network types at the cost of a higher computation time. These results suggests that GLIM may be utilized to increase the effectiveness of health promotion programs.
Antioxidant and antimicrobial activities of polyphenols from ethnomedicinal plants of Nigeria

The antioxidant properties and antimicrobial potential of three ethnomedicinal plants, (Momordica charanta, Senna alata and Nauclea lafifolia) extracted with acetone were investigated. Polyphenols from the medicinal plants were screened for their antioxidant and antimicrobial activities against pathogenic micro organisms (Staphylococcus aureus, Streptococcus pyogenes, Esherichia coli and Candida albicans). The medicinal plants displayed different polyphenols contents and antioxidant activities. In addition, varying antimicrobial susceptibility patterns were exhibited. The highest amount of total phenolic compounds was shown by S. alata and the lowest one was M. charanta. The extract of S. alata showed the highest antioxidant activity. Some micro organisms (S. aureus and C. albicans) were susceptible to the polyphenol extracts with minimum inhibitory concentration values between 1.25 to 5.00 mg/ml while other microorganisms (S. pyogenes and E. coli) appeared to be resistant to the extracts. The results suggested that these plants are not only potential sources of phenolic antioxidants but also potentially, good source of antimicrobial agents.