Miracle-dz/UniFAR-FTUs huggingface dataset:
Copy-move Image Forgery Detection Based on Gabor Descriptors and K-Means Clustering

At present, popularity of using image as the fundamental media of information is growing. Rapid development of technology brings effective image processing tools available and makes image forgery very easy. As an outcome, it turns into a complicated issue in late time. In that case, validating the legitimacy and integrity of digital images is ending up progressively vital issue. The most challenging region-duplication forgery is made by copying some portion of an image and pasting on different region of the same image. This study proposes an efficient region-duplication forgery detection technique. This research is categorized into segment-based region duplication forgery detection method. The design of the algorithm based on image segmentation and using Gabor descriptors and K-Means clustering. Initially, the image is segmented using normalized cut (NCut) segmentation technique. Then, applied Gabor Filters to extract image features and cluster similar features using KMeans clustering algorithm. Finally, comparing the clustering regions with the given threshold value will decide image authenticity. Experiment results proves the strength of the proposed method against various post-processing attacks such as rotation, scale, blurring and JPEG compression. A comparison with existing image forgery detection algorithms demonstrates that the proposed algorithm gives better performance.
Marine Mammal Lung Dynamics when Exposed to Underwater Explosion Impulse

Very little is known about marine mammal susceptibility to primary blast injury (PBI) except in rare cases of opportunistic studies. As a result, traditional analysis techniques relied on methods developed more than 30 years ago using terrestrial mammals as surrogates. Modeling tools available today have the computing power to vastly improve calculation of safe ranges and injury zones from underwater explosions (UNDEX) employing morphologically accurate proxies with material properties similar to marine mammal tissues. The Dynamic System Mechanics Advanced Simulation (DYSMAS) fluid–structure interaction (FSI) software is being used to simulate the complex phenomena of UNDEX, shock wave, and bubble pulse propagation through the water and transmission of energy to a cetacean focusing on the dynamic response of the thoracic cavity and air‐filled lungs to a shock wave. The approach integrates fluid and structural analyses with the material properties of blubber, bone, and muscle using marine mammal morphometrics to eliminate unnecessary assumptions made during more traditional approaches to analysis developed before these types of data and computational power were available. DYSMAS analyses of a 1D gas bubble surrounded by water was found to closely match the classical bubble dynamics models. Further, DYSMAS models of a spherical gas bubble surrounded by tissue and rib structure demonstrate a global radial oscillation of the gas bubble, but also show significant local deflection and material strain in response to the UNDEX loading. The intended result of the investigation is an improved and scientifically defensible understanding of the effects of UNDEX on marine mammals. Anat Rec, 2018. © 2018 Wiley Periodicals, Inc. Anat Rec, 302:718–734, 2019. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
Transferrable End-to-End Learning for Protein Interface Prediction

While there has been an explosion in the number of experimentally determined, atomically detailed structures of proteins, how to represent these structures in a machine learning context remains an open research question. In this work we demonstrate that representations learned from raw atomic coordinates can outperform hand-engineered structural features while displaying a much higher degree of transferrability. To do so, we focus on a central problem in biology: predicting how proteins interact with one another--that is, which surfaces of one protein bind to which surfaces of another protein. We present Siamese Atomic Surfacelet Network (SASNet), the first end-to-end learning method for protein interface prediction. Despite using only spatial coordinates and identities of atoms as inputs, SASNet outperforms state-of-the-art methods that rely on hand-engineered, high-level features. These results are particularly striking because we train the method entirely on a significantly biased data set that does not account for the fact that proteins deform when binding to one another. Demonstrating the first successful application of transfer learning to atomic-level data, our network maintains high performance, without retraining, when tested on real cases in which proteins do deform.
MR Brain Tumor Classification and Segmentation Via Wavelets

Timely, accurate detection of magnetic resonance (MR) images of brain is most important in the medical analysis. Many methods have already explained about the tumor classification in the literature. This paper explains the method of classifying MR brain images into normal or abnormal (affected by tumor), abnormality segments present in the image. This paper proposes DWT-discrete wavelet transform in first step to extract the image features from the given input image. To reduce the dimensions of the feature image principle component Analysis (PCA) is employed. Reduced extracted feature image is given to kernel support vector machine (KSVM) for processing. The data set has 90 brain MR images (both normal and abnormal) with seven common diseases. These images are used in KSVM process. Gaussian Radial Basis (GRB) kernel is used for the classification method proposed and yields maximum accuracy of 98% compared to linear kernel (LIN). From the analysis, compared with the existing methods GRB kernel method was effective. If this classification finds abnormal MR image with tumor then the corresponding part is separated and segmented by thresholding technique.
Amelanotic Malignant Melanoma with Dense Pleural Thickening Mimicking Malignant Mesothelioma

A 51-year-old man was diagnosed with stage IIC nodular malignant melanoma (T4bN0M0) of the right upper arm. The tumor was treatment-refractory, and left-sided pleural effusion emerged 1.5 years later. Aspiration of pleural fluid revealed abundant amelanotic, atypical cells that resembled epithelial malignant mesothelioma or lung adenocarcinoma cells; these cells were positive for melanoma-associated antigen recognized by T cells (MART-1)/Melan-A, HMB-45, and S-100 on immunocytochemistry. Thoracic computed tomography (CT) revealed marked diffuse pleural thickening in the left hemithorax that mimicked malignant mesothelioma; thus, the present report describes the unique cytological and radiological findings of this case.
Continuous Backward-to-Forward Beam-Scanning Conformal Leaky-Wave Antenna

A periodic substrate integrated waveguide (SIW) structure based conformal leaky-wave antenna (LWA) is presented. The LWA is capable of scanning its beam from near backfire, backward endfire, through the broadside to the forward direction. The -10-dB reflection coefficient bandwidth of the antenna is 7.428 - 10.47 GHz. The gain of the antenna is greater than 10 dBi throughout the beam scan range from -84° to +19°with a variation of source frequency from 7.3 to 10.3 GHz.
Design Method of Primary Transmitting Coil for Realizing Large Uniform Magnetic Field Distribution

In view of the problems existing in the existing wireless charging technology, this paper focused on the anti-offset performance of the charging area and system. Based on the principle of electromagnetics, the current density formula which can generate the uniform axial magnetic field strength was calculated, and the current density of the coil surface was combined, A new coil structure and a primary transmitting coil design method capable of realizing large uniform magnetic field distribution were proposed. The transmitting coil was systematically modeled by the COMSOL5.3 RF module and its axial spatial magnetic field strength and uniformity were simulated and analyzed, and multiple coil stitching superposition methods were further determined. The multi-dimensional visual electromagnetic measuring device was used to scan the axial magnetic field intensity of the primary transmitting coil surface, and compared with the simulation data, it was proved that the magnetic field distribution of the large primary transmitting coil formed after the splicing and superposition met the uniformity requirement.
A High-Efficiency 35GHz Rectenna with compact structure for rectenna arrays

A 35GHz rectenna with high efficiency and compact structure is proposed in this paper. A patch antenna fed by the coupling slot is employed to receive power from free space. The structure of designed rectifier is compact and printed on the bottom side of the antenna. The input low-pass or band-pass filter of the rectifier is removed. Instead, a short ended microstrip line is introduced as a band stop structure to block the harmonics and match the impendence. This circuit configuration highly simplifies the total topology and is suitable for rectenna arrays applications. Furthermore, removing the input filters reduced the insertion loss and thus improved the microwave-to-dc (MW-DC) conversion efficiency. The rectenna is designed, fabricated and measured, a maximum MW-DC efficiency of 53.2% is achieved for a power density of 51.2 mW/cm2 with the dc load 250 $\Omega$ at 35 GHz.
Accelerated long‐range corrected exchange functional using a two‐gaussian operator combined with one‐parameter progressive correlation functional [LC‐BOP(2Gau)]

Recently, we proposed a simple yet efficient method for the computation of a long‐range corrected (LC) hybrid scheme [LC‐DFT(2Gau)], which uses a modified two‐Gaussian attenuating operator instead of the error function for the long‐range HF exchange integral. This method dramatically reduced the computational time while maintaining the improved features of the LC density functional theory (DFT). Here, we combined an LC hybrid scheme using a two‐Gaussian attenuating operator with one‐parameter progressive correlation functional and Becke88 exchange functional with varying range‐separation parameter values [LC‐BOP(2Gau) with various μ values of 0.16, 0.2, 0.25, 0.3, 0.35, 0.4, and 0.42] and demonstrated that LC‐BOP(2Gau) reproduces well the thermochemical and frontier orbital energies of LC‐BOP. Additionally, we revised the scaling factors of the Gaussian multipole screening scheme for LC‐DFT(2Gau) to correspond to the angular momentum of orbitals, which decreased the energy deviations from the energy with the no‐screening scheme. © 2018 Wiley Periodicals, Inc.
Perineural Invasion is a Strong Prognostic Moderator in Ampulla of Vater Carcinoma: A Meta-analysis

Objective Ampulla of Vater carcinoma (AVC) has a broad spectrum of different prognoses. As such, new moderators of survival are urgently needed. We aimed at clarifying the prognostic role of perineural invasion in AVC. Methods Using PubMed and SCOPUS databases, we conducted the first systematic review and meta-analysis on this topic. Results Analyzing 29 articles for a total of 2379 patients, we found that the presence of perineural invasion increased the risk of all-cause mortality more than 2 times (relative risk [RR], 2.07; 95% confidence interval [CI], 1.78–2.42 [P < 0.0001]; hazard ratio [HR], 2.72; 95% CI, 1.86–3.97 [P < 0.0001]), of cancer-specific mortality more than 6 times (RR, 6.12; 95% CI, 3.25–11.54 [P < 0.0001]; HR, 6.59; 95% CI, 2.29–3.49 [P < 0.0001]), and of recurrence more than 2 times (RR, 2.63; 95% CI, 1.89–3.67 [P < 0.0001]; HR, 2.54; 95% CI, 1.24–5.21 [P = 0.01]). Conclusions Perineural invasion is strongly associated with a poorer prognosis in AVC, influencing both survival and risk of recurrence. It should be reported in the final pathology report and should be taken into account by future oncologic staging systems, identifying a group of AVC with a more malignant biological behavior.
Skin tests in patients with delayed and immediate hypersensitivity to heparins: A case series

Section of Clinical, Allergological and Venereological Dermatology, Department of Medicine, University of Perugia, Perugia, Italy Correspondence Professor Luca Stingeni, Section of Clinical, Allergological and Venereological Dermatology, Department of Medicine, University of Perugia, 06129 Perugia, Italy. Email: luca.stingeni@unipg.it
Identification and Prioritization of urban issues from Smart City data

Government should be able to publish and interact better for offering e-government services effectively to their netizens. In this context publish refers to delivering information through web pages and interact refers to allowing citizens to provide feedback and suggestions. Developing a model for understanding and prioritizing urban governance issues from these user-generated contents is an important research issue. This paper proposes a four-step solution approach to prioritizing urban governance issues from user-generated contents using Aspect Based Sentiment Analysis (ABSA). First, a novel lexicon-based approach is used for selecting trigrams as aspect phrase and pruning the list using cross-domain stop words. Second, the opinionated sentences are extracted using the identified aspects. Third, polarity and subjectivity of the aspects and corresponding comments along with the bag-of-words for the aspects are considered as the features for learning the category of the urban issue. Fourth, the sentiment score of a specific category is used to prioritize the urban issues. The proposed methodology is applied to a real user-generated content extracted from a discussion forum on smart cities from an e-governance portal in India. Our research demonstrates that Aspect Based Sentiment Analysis (ABSA) with necessary modifications can be utilized to extract issues, suggestions, and ideas from crowd-sourced citizen-generated content (Social Data). Sentiment analysis can be used to prioritize them as well. Analysis of the data for the candidate city shows that while the citizens are happy about educational facilities in the city they are highly concerned about health issues. A deeper investigation revealed the health issues are due to high degree of carbon emission, lack of solid waste management system and lack of sanitation among the others.
Embracing the Dark Side: Computational Approaches to Unveil the Functionality of Genes Lacking Biological Annotation in Drug-Induced Liver Injury

In toxicogenomics, functional annotation is an important step to gain additional insights into genes with aberrant expression that drive pathophysiological mechanisms. Nevertheless, there exists a gap on annotation of these genes which often hampers the interpretation of results and limits their applicability in translational medicine. In this study, we evaluated the coverage of functional annotations of differentially expressed genes (DEGs) induced by 10 selected compounds from the TG-GATEs database identified as high- or no-risk in causing drug-induced liver injury (most-DILI or no-DILI, respectively) using in vitro human data. Functional roles of DEGs not present in the most common biological annotation databases – termed “dark genes” – were unveiled via literature mining and via the identification of shared regulatory transcription factors or signaling pathways. Our results demonstrated that there were approximately 13% of dark genes induced by these compounds in vitro and we were able to obtain additional relevant information for up to 76% of those. Using interactome data from several sources, we have uncovered genes such as LRBA, and WDR26 as highly connected in the protein network that play roles in drug response. Genes such as MALAT1, H19, and MIR29C – whose links to hepatotoxicity have been confirmed – were identified as markers for the most-DILI group and appeared as top hits across all literature-based mining methods. Furthermore, we investigated the potential impact of dark genes on liver toxicity by identifying their rat orthologs in combination with their correlation to drug-induced liver pathologies observed in vivo following chemical exposure. We identified a set of important regulatory transcription factors of dark genes for all most-DILI compounds including E2F1 and JUND with supporting evidences in literature and we found Magee1 correlated with chemically induced bile duct hyperplasia and adverse responses at 29 days in rats in vivo. In conclusion, in this study we show the potential role of these poorly annotated genes in mechanisms underlying hepatotoxicity and offer a number of computational approaches that may help to minimize current gaps in gene annotation and highlight their values as potential biomarkers in toxicological studies.
Dipasperoside B, a New Trisiridoid Glucoside from Dipsacus asper

Dipasperoside B (1), isolated from the root of Dipsacus asper, was determined to be a first example of naturally occurring iridoid glucoside trimer with an unusual skeleton featuring oxaloacetic acid derived moiety coupled with a secoiridoid nucleus in the central part, on the basis of spectroscopic data. Dipasperoside B (1) revealed the inhibitory activity against nitric oxide (NO) production in a lipopolysaccharide (LPS)-activated murine macrophage RAW264.7 cell line, with an IC50 value of 21.3 μM, identical to a positive control, NG-monomethyl-L-arginine (L-NMMA, IC50 22.6 μM), without any significant cytotoxicity.
An Analytical Study of Mental Rotation Activity Based on Feature Descriptor Patterns

The chief objective of this paper is to investigate the differences in Feature Descriptor patterns during mental rotation of five different objects for both healthy as well as brain diseased subjects. Thus, electroencephalographic (EEG) activity was measured during mental rotation of objects by various angles with respect to its present orientation. Source localization using eLORETA inferred an enhanced activation of pre-frontal and frontal lobe regions during mental rotation activity. Experimental analysis also confirmed maximal activation of lower alpha frequency band while performing this cognitive task. Differential Evolutionary (DE) algorithm has been implemented to select the optimal features which are represented using the Feature Descriptor diagrams. These diagrams infer that the feature patterns are distinct and vary from object to object. Moreover, these patterns orient by $45^{\mathrm {o}}$ for $90^{\mathrm {o}}$ mental rotation and by $75^{\mathrm {o}}$ for $180^{\mathrm {o}}$ mental rotation of the presented objects. However, there exists an inconsistency in the Feature descriptor diagrams for patients suffering from pre-frontal lobe amnesia and Alzheimer's disease. It is also found that these diagrams remain unaffected during mental rotation which infers their incapability to perform such a cognitive task. Hence, this work can be effectively utilized to detect people suffering from memory related disorder.
Manual Hippocampal Subfield Segmentation Using High-Field MRI: Impact of Different Subfields in Hippocampal Volume Loss of Temporal Lobe Epilepsy Patients

In patients with temporal lobe epilepsy (TLE), presurgical magnetic resonance imaging (MRI) often reveals hippocampal atrophy, while neuropathological assessment indicates the different types of hippocampal sclerosis (HS). Different HS types are not discriminated in MRI so far. We aimed to define the volume of each hippocampal subfield on MRI manually and to compare automatic and manual segmentations for the discrimination of HS types. The T2-weighted images from 14 formalin-fixed age-matched control hippocampi were obtained with 4.7T MRI to evaluate the volume of each subfield at the anatomical level of the hippocampal head, body, and tail. Formalin-fixed coronal sections at the level of the body of 14 control cases, as well as tissue samples from 24 TLE patients, were imaged with a similar high-resolution sequence at 3T. Presurgical three-dimensional (3D) T1-weighted images from TLE went through a FreeSurfer 6.0 hippocampal subfield automatic assessment. The manual delineation with the 4.7T MRI was identified using Luxol Fast Blue stained 10-μm-thin microscopy slides, collected at every millimeter. An additional section at the level of the body from controls and TLE cases was submitted to NeuN immunohistochemistry for neuronal density estimation. All TLE cases were classified according to the International League Against Epilepsy's (ILAE's) HS classification. Manual volumetry in controls revealed that the dentate gyrus (DG)+CA4 region, CA1, and subiculum accounted for almost 90% of the hippocampal volume. The manual 3T volumetry showed that all TLE patients with type 1 HS (TLE-HS1) had lower volumes for DG+CA4, CA2, and CA1, whereas those TLE patients with HS type 2 (TLE-HS2) had lower volumes only in CA1 (p ≤ 0.038). Neuronal cell densities always decreased in CA4, CA3, CA2, and CA1 of TLE-HS1 but only in CA1 of TLE-HS2 (p ≤ 0.003). In addition, TLE-HS2 had a higher volume (p = 0.016) and higher neuronal density (p < 0.001) than the TLE-HS1 in DG + CA4. Automatic segmentation failed to match the manual or histological findings and was unable to differentiate TLE-HS1 from TLE-HS2. Total hippocampal volume correlated with DG+CA4 and CA1 volumes and neuronal density. For the first time, we also identified subfield-specific pathology patterns in the manual evaluation of volumetric MRI scans, showing the importance of manual segmentation to assess subfield-specific pathology patterns.
Electropalatography Contact Pattern in the Production of Consonant /m/ Among Malay Speakers

A nasal consonant is the part of the manner of articulation in the Malay language. Nasal consonant consisting of bilabial consonant /m/, alveolar consonant /n/, postalveolar consonant /ŋ/ and velar consonant /ŋ/. This paper presents the study of the contact pattern in the production of consonant /m/ using Electropalatography. Nine Malay speakers involved in this study. In detecting the contact pattern during the production of consonant /m/, Reading palate, which consists of 62 electrodes were used. The data were analyzed using Articulate Assist TM Version 1.18. The result indicates that during the production of consonant /m/ there was more contact at the posterior of the hard palate compared to other zones of the palate. Additionally, the Molar Class of the subjects does not affect the pattern production of consonant /m/.
Towards a benefits dependency network for DevOps based on a systematic literature review

DevOps as a new way of thinking for software development and operations has received much attention in the industry, while it has not been thoroughly investigated in academia yet. The objective of this study is to characterize DevOps by exploring its central components in terms of principles, practices and their relations to the principles, challenges of DevOps adoption, and benefits reported in the peer‐reviewed literature. As a key objective, we also aim to realize the relations between DevOps practices and benefits in a systematic manner. A systematic literature review was conducted. Also, we used the concept of benefits dependency network to synthesize the findings, in particular, to specify dependencies between DevOps practices and link the practices to benefits. We found that in many cases, DevOps characteristics, ie, principles, practices, benefits, and challenges, were not sufficiently defined in detail in the peer‐reviewed literature. In addition, only a few empirical studies are available, which can be attributed to the nascency of DevOps research. Also, an initial version of the DevOps benefits dependency network has been derived. The definition of DevOps principles and practices should be emphasized given the novelty of the concept. Further empirical studies are needed to improve the benefits dependency network presented in this study.