Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection And Classification Of Various Image Operations Using Deep Learning Technology

As one of the main medium for information transmission, the digital imagecan be easily tampered during transmission. It is becoming more and more important to identifywhether the given image is an original image or a processed image. In this paper, we propose acompact universal feature based on spatial domain in virtue of some latest image forensic methods and design a multi-class classification scheme using the deep learning technique, to identify and furthermore classify the various normal image operations. According to the experimental results, the proposed method can well detect and classify the multiple common image post-processing operations. And the comparison with the existing feature shows the better performance of the proposed method.
Evaluating the Inherent Sensitivity of Programming Languages to Soft Errors

The choice of programming language depends on a number of factors, such as efficiency and maintainability. With the predicted increase of the soft error rate in coming years due to technological improvements and the popularization of energy-saving techniques, the responsibility of tolerating transient hardware faults is no longer solely on the hardware-side, but is becoming more and more a software problem. Therefore choices taken throughout the software development and compilation process, such as the used programming language, are now a factor that should be taken into account. This paper evaluates the impact of the programming language in the sensitivity and vulnerability to soft errors of an application, through extensive fault injection campaigns that cover 5 benchmarks over 7 programming languages, considering the most common implementation. The results show that both the programming language and workload have a significant correlation with the occurrence of failures due to soft errors. Furthermore, since execution time varied noticeably across programming languages, applications developed in less-efficient languages showed a higher vulnerability time window in which they may be affected by soft errors.
Evaluating and Accelerating High-Fidelity Error Injection for HPC

We address two important concerns for the analysis of the behavior of applications in the presence of hardware errors: (1) when is it important to model how hardware faults lead to erroneous values (instruction-level errors) with high fidelity, as opposed to using simple bit-flipping models, and (2) how to enable fast high-fidelity error injection campaigns, in particular when error detectors are employed. We present and verify a new nested Monte Carlo methodology for evaluating high-fidelity gate-level fault models and error-detector coverage, which is orders of magnitude faster than current approaches. We use that methodology to demonstrate that, without detectors, simple error models suffice for evaluating errors in 9 HPC benchmarks.
Wideband Half-Mode SIW Power Divider With Improved Output Isolation Using Slotline Isolation Technology

A wideband half-mode substrate integrated waveguide (HMSIW) power divider with bandpass-filtering response using the slotline isolation technology is presented in this paper. Resistive slot on the bottom layer is employed to improve the output isolation. To obtain filtering response, L-type slot line and two multimode SIR resonators loaded symmetrically at two sides of the input transmission line are employed to generate transmission zeros at upper stop band. In addition, filtering response is achieved by combining the inherent high-pass characteristic of the HMSIW and the TZs. The equivalent circuits are presented and analyzed by using even- and odd-mode method, and the relative design equations are derived. The measured return loss of the HMSIW power divider is about 10 dB and its isolation is greater than 15 dB from 3.8 to 5.8 GHz. All measured results show reasonable agreement with the simulated ones.
Impedance measurement of bipolar stimulation electrodes immersed in medium

We measured the impedance of bipolar stimulation electrode immersed in two media of interest for electrostimulation: extracellular fluid (ECF), and cerebrospinal fluid (CSF). Using a convenient method, the impedance magnitude and phase were measured within a frequency range from 10 Hz to 20 kHz. The impedance magnitudes were in 1 kΩ range, having capacitive character, a strong frequency dependence, and observable dependence on temperature and medium layer thickness.
Inferring Political Alignments of Twitter Users

Increasing popularity of Twitter in politics is subject to commercial and academic interest. To fully exploit the merits of this platform, reaching target audience with desired political leanings is critical. This paper extends the research on inferring political orientations of Twitter users to the case of 2017 Turkish constitutional referendum. After constructing a targeted dataset of tweets, we explore several types of potential features to build accurate machine learning based predictive models. In our experiments, three-class support vector machine (SVM) classifier trained on semantic features achieves the best accuracy score of 89.9%. Moreover, an SVM classifier trained on full text features performs better than an SVM classifier trained on hashtags, with respective accuracy scores of 89.05% and 85.9%. Relatively high accuracy scores obtained by full text features may point to differences in language use, which deserves further research.
A Technique to Handle Negation in Sentiment Analysis on Movie Reviews

Day by day there is seen large growth in sentiment rich social and electronic media. Researchers have increasing interest in vast amount of user data generated such as comments, customer reviews and opinions which can be used to extract valuable information with the help of sentiment analysis and opinion mining. The negation modifiers make the Sentiment Classification approaches suffer and they can completely distort the meaning of the discourse. Hence it becomes mandatory to handle them effectively. Our work provides an approach for the identification and handling negation in unstructured data. The paper proposes and evaluates better modified approach for negation identification in sentiment analysis than the existing identification methods. The provided data which is stored in document is fed into a vector and if the data has negation, it is treated exceptionally. Both syntactic and morphologic negation is handled using dependency parse tree and prefix algorithm respectively. We have attainted an accuracy of 92% from our work.
A Machine Learning Approach for an Early Prediction of Preterm Delivery

The preterm birth presents a major cause of the infants' deaths, or the consequent health impairments globally, with an increasing trend of the preterm rate. The enormous global burden on both families and society calls for the preventive and predictive measures. The electrohysterogram (EHG), electrical activity of uterus as measured by surface electrodes, is a noninvasive and affordable tool for effective monitoring of both pregnancy and labour. In this study, the possibility of an early prediction of preterm delivery from the EHG recordings made between 22nd and 25th week of the gestation is explored. A set of novel features, including those exploiting signal's nonstationarity, based on the predictive modelling, and empirical mode decomposition, was evaluated on 15min long EHG recordings from the publicly available Term-Preterm EHG (TPEHG) database. On average, Random Forest classifier combined with artificial sampling, tested using 10-fold cross-validation on 322 samples (38 preterm) provided for 99.23% accuracy, with 98.40%sensitivity, and area under curve of 99%. The proposed approach has an additional advantage achieving the classification improvement over shorter, 15min long EHG recordings.
Ameliorative effects of autophagy inducer, simvastatin on alcohol‐induced liver disease in a rat model

Alcoholic liver disease (ALD) encompasses a variety of liver injuries with various underlying mechanisms but still no effective treatment. So we aimed to monitor the influence of simvastatin on alcohol‐induced liver injury and elucidate the underlying mechanisms of its cytoprotective effect. Thirty male albino rats were randomly divided into five equal groups. Group 1 (control): received a standard diet; group 2: received simvastatin (10 mg kg−1 day −1) once a day orally for 8 weeks; group 3: received 20% ethanol (7.9 g kg −1 day −1) daily orally for 8 weeks; group 4: received 20% ethanol along with same simvastatin dose daily for 8 weeks; group 5: received 20% ethanol orally for 8 weeks then received the same simvastatin dose for the next 8 weeks. Serum alanine aminotransferase, aspartate aminotransferase, total cholesterol, triglycerides, high‐density lipoprotein cholesterol, and low‐density lipoprotein cholesterol were measured. Liver tissue malondialdehyde, reduced glutathione levels, and superoxide dismutase activity were estimated. B‐cell lymphoma 2 and C/EBP homologous protein levels were evaluated by enzyme linked immunosorbent assay (ELISA). Light chain 3‐II and peroxisome proliferation‐activated receptor gamma messenger RNA expression was assessed by real‐time polymerase chain reaction. Immunohistochemical staining was performed using anti‐rat tumor necrosis factor‐alpha antibody. Our results revealed that simvastatin treatment was able to ameliorate alcohol‐induced liver damage; the improved biochemical data were confirmed by histopathological evaluation. Simvastatin being an autophagy inducer was able to prevent and reverse alcohol‐induced liver changes via induction of autophagy, attenuation of oxidative stress, inflammation, and endoplasmic reticulum stress‐induced apoptosis. Therefore, our findings suggest that treatment with simvastatin may be a useful approach in the management strategy of ALD.
Exploring Customizable Heterogeneous Power Distribution and Management for Datacenter

Large-scale datacenters are facing increasing pressure of capping their carbon emission and power cost. Many leading-edge studies have started to explore server clusters running on multiple power sources. Existing approaches do not sufficiently consider the fine-grained power delivery to satisfy diverse requirements in datacenter, especially in the multi-tenant/colocation datacenter, which may yield low energy utilization. To address the emerging trend and new requirements, this article proposes a novel Datacenter inner Power Switch Network (DiPSN) to improve datacenter power efficiency and user satisfaction. DiPSN is a reconfigurable and easy-to-scale-out power architecture, which enables datacenter to distribute various power sources in a fine-grained manner. Moreover, a tailored machine learning based power source management framework is proposed for DiPSN to dynamically optimize user customized performance metrics and maximize datacenter revenue. Compared with conventional single-switch power distribution system, our DiPSN can be configured to improve solar energy utilization by 39.6 percent, reduce utility power cost by 11.1 percent and improve workload performance by 33.8 percent. Meanwhile, our design can extend battery lifetime by 9.3 percent. This work could provide valuable guidelines for designing heterogeneous power distribution architecture and management methodology in datacenters for improving user-customizable efficiency, sustainability and economy.
A Compact and High-Isolated Multiresonator-Coupled Diplexer

A compact and high-isolated microstrip diplexer by using multiple-coupled resonators with separated electric and magnetic coupling paths and dual-mode characteristic has been presented. Multiple transmission zeros have been obtained, and some of them can be controlled by the electric and the magnetic couplings. The diplexer has been fabricated and measured, and the design has been demonstrated. The developed diplexer has a measured passband insertion loss of not more than 2.15 dB, a measured isolation of more than 38 dB, and a compact circuit size of $0.66\lambda _{\mathrm{g}} \times 0.15\lambda _{\mathrm{g}}$ at the lower frequency band.
A General Framework for Implicit and Explicit Social Recommendation

Research of social recommendation aims at exploiting social information to improve the quality of a recommender system. It can be further divided into two classes. Explicit social recommendation assumes the existence of not only the users’ ratings on items, but also the explicit social connections between users. Implicit social recommendation assumes the availability of only the ratings but not the social connections between users, and attempts to infer implicit social connections between users with the goal to boost recommendation accuracy. This paper proposes a unified framework that is applicable to both explicit and implicit social recommendation. We propose an optimization framework to learn the degree of social correlation and rating prediction jointly, so these two tasks can mutually boost the performance of each other. Furthermore, a well-known challenge for implicit social recommendation is that it takes quadratic time to learn the strength of pairwise connections. This paper further proposes several practical tricks to reduce the complexity of our model to be linear to the observed ratings. The experiments show that the proposed model, with only two parameters, can significantly outperform the state-of-the-art solutions for both explicit and implicit social recommender systems.
Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations

In this work we explore the generalization characteristics of unsupervised representation learning by leveraging disentangled VAE's to learn a useful latent space on a set of relational reasoning problems derived from Raven Progressive Matrices. We show that the latent representations, learned by unsupervised training using the right objective function, significantly outperform the same architectures trained with purely supervised learning, especially when it comes to generalization.
The Code Mini-Map Visualisation: Encoding Conceptual Structures Within Source Code

Modern source code editors typically include a code mini-map visualisation, which provides programmers with an overview of the currently open source code document. This paper proposes to add a layering mechanism to the code mini-map visualisation in order to provide programmers with visual answers to questions related to conceptual structures that are not manifested directly in the code. Details regarding the design and implementation of this scope information layer, which displays additional encodings that correspond to the scope chain and information related to the scope chain within a source code document, is presented. The scope information layer can be used by programmers to answer questions such as: to which scope does a specific variable belong, and in which scope is the cursor of the source code editor currently located in. Additionally, this paper presents a study that evaluates the effectiveness of adding the scope information layer to a code mini-map visualisation in order to help programmers understand scope relationships within source code. The results of the study show that the incorporating additional layers of information onto the code mini-map visualisation can have a positive effect on code understanding.
Biogenic Synthesis of Copper and Silver Nanoparticles Using Green Alga Botryococcus braunii and Its Antimicrobial Activity

The spread of infectious diseases and the increase in the drug resistance among microbes has forced the researchers to synthesize biologically active nanoparticles. Improvement of the ecofriendly procedure for the synthesis of nanoparticles is growing day-by-day in the field of nanobiotechnology. In the present study, we use the extract of green alga Botryococcus braunii for the synthesis of copper and silver nanoparticles. The characterization of copper and silver nanoparticles was carried out by using UV-Vis spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron spectroscopy (SEM). FTIR measurements showed all functional groups having control over reduction and stabilization of the nanoparticles. The X-ray diffraction pattern revealed that the particles were crystalline in nature with a face-centred cubic (FCC) geometry. SEM micrographs have shown the morphology of biogenically synthesized metal nanoparticles. Furthermore, these biosynthesized nanoparticles were found to be highly toxic against two Gram-negative bacterial strains Pseudomonas aeruginosa (MTCC 441) and Escherichia coli (MTCC 442), two Gram-positive bacterial strains Klebsiella pneumoniae (MTCC 109) and Staphylococcus aureus (MTCC 96), and a fungal strain Fusarium oxysporum (MTCC 2087). The zone of inhibition was measured by the agar well plate method, and furthermore, minimum inhibitory concentration was determined by the broth dilution assay.
Comparison of additional minocycline versus iodopovidone pleurodesis during video-assisted thoracoscopic bleb resection for primary spontaneous pneumothorax: a propensity score-matched analysis.

Background The optimal chemical agent for additional pleurodesis during video-assisted thoracoscopic surgery (VATS) bleb resection in primary spontaneous pneumothorax (PSP) remains controversial. We compared the efficacy and safety of iodopovidone with those of minocycline for additional chemical pleurodesis during VATS bleb resection. Methods Of 332 patients who underwent VATS bleb resection, 299 patients diagnosed with PSP were enrolled in this study. The patients were divided into two groups according to the chemical agents used for additional pleurodesis (iodopovidone versus minocycline). Propensity score matching was performed on the basis of the preoperative clinical parameters. Postoperative complications, chest tube indwelling time, postoperative hospital stay, and recurrence rate were compared between the two groups. Results The median duration of follow-up was 14 months (range, 1-94 months). After propensity score matching, 94 patients from the iodopovidone group and 94 patients from the minocycline group were matched. The perioperative outcomes, including fever, prolonged air-leak, prolonged-effusion, postoperative bed-side pleurodesis, and rehospitalization because of complications, were not significantly different between the two groups. However, the amount of drainage during the first two postoperative days, duration of chest tube indwelling, and duration of hospitalization were significantly shorter in the minocycline group (P<0.001). Conclusions This study confirmed the safety of both minocycline and iodopovidone for additional pleurodesis. However, we carefully recommend minocycline over iodopovidone for pleurodesis because of faster postoperative recovery.
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

Feature pyramids are widely exploited by both the state-of-the-art one-stage object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object detectors (e.g., Mask RCNN, DetNet) to alleviate the problem arising from scale variation across object instances. Although these object detectors with feature pyramids achieve encouraging results, they have some limitations due to that they only simply construct the feature pyramid according to the inherent multiscale, pyramidal architecture of the backbones which are originally designed for object classification task. Newly, in this work, we present Multi-Level Feature Pyramid Network (MLFPN) to construct more effective feature pyramids for detecting objects of different scales. First, we fuse multi-level features (i.e. multiple layers) extracted by backbone as the base feature. Second, we feed the base feature into a block of alternating joint Thinned U-shape Modules and Feature Fusion Modules and exploit the decoder layers of each Ushape module as the features for detecting objects. Finally, we gather up the decoder layers with equivalent scales (sizes) to construct a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels. To evaluate the effectiveness of the proposed MLFPN, we design and train a powerful end-to-end one-stage object detector we call M2Det by integrating it into the architecture of SSD, and achieve better detection performance than state-of-the-art one-stage detectors. Specifically, on MSCOCO benchmark, M2Det achieves AP of 41.0 at speed of 11.8 FPS with single-scale inference strategy and AP of 44.2 with multi-scale inference strategy, which are the new stateof-the-art results among one-stage detectors. The code will be made available on https://github.com/qijiezhao/M2Det.
A Lip Reading Model Using CNN with Batch Normalization

The goal of Lip-reading is to decode and analyze the lip movements of a speaker for a said word or phrase. Variation in speaking speed, intensity and same lip sequence of distinct characters have been the challenging aspects of lip reading. In this paper we present a lip reading model for an audio-less video data of variable-length sequence frames. First, we extract the lip region from each face image in the video sequence and concatenate them to form a single image. Next, we design a twelve-layer Convolutional Neural Network with two layer of batch normalization for training the model and to extract the visual features end-to-end. Batch normalization helps to reduce the internal and external variances in various attributes like speaker's accent, lighting and quality of image frames, pace of the speaker and posture of speaking etc. We validate the performance of ourmodel on a standard audio-less video MIRACLE-VC1 dataset and compare with an existing model whichuses 16 layers CNN or more. A training accuracy of 96% and a validation accuracy of 52.9% have been attained on the proposed lip reading model.
A Node Deployment Optimization Method of WSN Based on Ant-Lion Optimization Algorithm

Node deployment optimization is one of the application points of wireless sensor networks (WSN), and it is also a hot topic that scholars have studied in recent years. Aiming at the problems that exist in WSN node deployment, such as uneven distribution of nodes and incomplete coverage, this paper proposes an optimization method for WSN node deployment based on ant-lion optimization algorithm. Firstly, this paper establishes a mathematical model to cover the target area with WSN nodes. Then, the optimization problem of node deployment is transformed into the problem of finding the maximum value of the function. Finally, the ALO algorithm is used to obtain the optimal solution of node deployment. The experimental results show this method works well in optimizing the deployment location of WSN nodes, and can increase the overall network coverage rate.