Copy-Move Geometric Tampering Estimation Through Enhanced SIFT Detector Method

Digital picture forgery detection has recently become a popular and sig-nificant topic in image processing.Due to advancements in image processing and the availability of sophisticated software,picture fabrication may hide evidence and hinder the detection of such criminal cases.The practice of modifying origi-nal photographic images to generate a forged image is known as digital image forging.A section of an image is copied and pasted into another part of the same image to hide an item or duplicate particular image elements in copy-move forgery.In order to make the forgeries real and inconspicuous,geometric or post-processing techniques are frequently performed on tampered regions during the tampering process.In Copy-Move forgery detection,the high similarity between the tampered regions and the source regions has become crucial evidence.The most frequent way for detecting copy-move forgeries is to partition the images into overlapping square blocks and utilize Discrete cosine transform(DCT)com-ponents as block representations.Due to the high dimensionality of the feature space,Gaussian Radial basis function(RBF)kernel based Principal component analysis(PCA)is used to minimize the dimensionality of the feature vector repre-sentation,which improves feature matching efficiency.In this paper,we propose to use a novel enhanced Scale-invariant feature transform(SIFT)detector method called as RootSIFT,combined with the similarity measures to mark the tampered areas in the image.The proposed method outperforms existing state-of-the-art methods in terms of matching time complexity,detection reliability,and forgery location accuracy,according to the experimental results.The F1 score of the proposed method is 92.3%while the literature methods are around 90%on an average.

The relationship between age,acceptable noise level,and listening effort in middle-aged and older-aged individuals

Objective:The purpose of the study was to evaluate listening effort in adults who experience varied annoyance towards noise.Materials and methods:Fifty native Kannada-speaking adults aged 41e68 years participated.We evaluated the participant's acceptable noise level while listening to speech.Further,a sentence-final wordidentification and recall test at 0 dB SNR(less favorable condition)and 4 dB SNR(relatively favorable condition)was used to assess listening effort.The repeat and recall scores were obtained for each condition.Results:The regression model revealed that the listening effort increased by 0.6%at 0 dB SNR and by 0.5%at 4 dB SNR with every one-year advancement in age.Listening effort increased by 0.9%at 0 dB SNR and by 0.7%at 4 dB SNR with every one dB change in the value of Acceptable Noise Level(ANL).At 0 dB SNR and 4 dB SNR,a moderate and mild negative correlation was noted respectively between listening effort and annoyance towards noise when the factor age was controlled.Conclusion:Listening effort increases with age,and its effect is more in less favorable than in relatively favorable conditions.However,if the annoyance towards noise was controlled,the impact of age on listening effort was reduced.Listening effort correlated with the level of annoyance once the age effect was controlled.Furthermore,the listening effort was predicted from the ANL to a moderate degree.

Introduction to Synchronized Kinematic and Electromagnetic Mechanics

Introduction to fundamental physics according to the parallel harmonization of kinematic and electromagnetic mechanics, in accordance with Wilhelm Wien’s project, which involved the integration in kinematic mechanics of the mass increase of the electron as a function of its velocity, as measured by Walter Kaufmann with his bubble-chamber experiments, and analyzed and confirmed by H. A. Lorentz and all the leading edge physicists who then re-analyzed this data.

Application of LiDAR Data for Deep Learning Based Near Crash Prediction at Signalized Intersection

Near crash events are often regarded as an excellent surrogate measure for traffic safety research because they include abrupt changes in vehicle kinematics that can lead to deadly accident scenarios. In this paper, we introduced machine learning and deep learning algorithms for predicting near crash events using LiDAR data at a signalized intersection. To predict a near crash occurrence, we used essential vehicle kinematic variables such as lateral and longitudinal velocity, yaw, tracking status of LiDAR, etc. A deep learning hybrid model Convolutional Gated Recurrent Neural Network (CNN + GRU) was introduced, and comparative performances were evaluated with multiple machine learning classification models such as Logistic Regression, K Nearest Neighbor, Decision Tree, Random Forest, Adaptive Boost, and deep learning models like Long Short-Term Memory (LSTM). As vehicle kinematics changes occur after sudden brake, we considered average deceleration and kinematic energy drop as thresholds to identify near crashes after vehicle braking time . We looked at the next 3 seconds of this braking time as our prediction horizon. All models work best in the next 1-second prediction horizon to braking time. The results also reveal that our hybrid model gathers the greatest near crash information while working flawlessly. In comparison to existing models for near crash prediction, our hybrid Convolutional Gated Recurrent Neural Network model has 100% recall, 100% precision, and 100% F1-score: accurately capturing all near crashes. This prediction performance outperforms previous baseline models in forecasting near crash events and provides opportunities for improving traffic safety via Intelligent Transportation Systems (ITS).

Eating Patterns and Daily Dietary Recall of Primary School Pupils: An Empirical Evidence from A School Feeding Scheme

The objective of this study was to experimentally evaluate children’s daily food memory and eating habits.The study found that the gender and school location had an impact on the nutritional condition of primary school students as well as the school food scheme.The investigations were based on three hypotheses and three research questions.In this study,the Eating Habits and Daily Dietary Recall Scale was the tool utilized to gather data(EPDDRS).Four experts—three from the department of vocational education and one lecturer in test and measurement evaluation—validated the instrument’s face.The dependability indices of EPDDRS were calculated using Cronbach’s Alpha.While delivering the instruments,the researcher used the direct administration and retrieval approach.58 instructors and a sample size of 1240 students were selected using a systematic random selection approach.The obtained data was examined using mean and standard deviation to address the research objectives,and the null hypotheses were tested using t-test statistics and Analysis of variance(ANOVA)at the 0.05 level of significance.The main conclusions of this study were that the school meal program had a favorable impact on the students’nutritional status.Also,a balanced ration of nutrient-dense meals that were suitably varied was supplied for the students via the school food program.Also,the findings revealed a substantial difference in the mean assessments of male and female students about their eating patterns.On the school meal program’s dietary recall list,students from high,middle,and low socioeconomic status differ significantly.Recommendations were given to the government,schools,and parents based on the study’s findings.The study’s shortcomings were discussed,and recommendations for more research were made.

“SQ3R”学习策略的功能分析

"SQ3R"学习策略具有定向、指序、选式、反馈和助动等功能。五者结合能帮助学习者提高学习效率,让学习者产生成功感,激发学习兴趣和积极性。"SQ3R"学习法,不仅帮助学生积极参与到大学英语的教学活动中去,而且帮助其养成课外阅读的习惯,找到获取信息的捷径,使其不再是被动的信息接受者,而是自主的阅读者。

基于校园卡消费预测学生挂科情况

旨在通过校园卡消费来预测学生挂科情况。首先通过对消费数据和成绩数据的预处理,再结合辅导员的反馈,提出作息习惯对成绩有很大影响的合理猜想;然后运用多种数据挖掘模型来预测学生是否存在挂科风险。研究结果表明,通过使用一卡通消费数据,能比较准确地预测学生是否存在挂科风险,其中朴素贝叶斯模型的预测效果最好,Recall超过80%。

工作犬的RECALL能力训练

所谓RECALL是指犬在训练和使用中，具有在训导员的口令刺激下，能随时终止作业而迅速回到训导员身边的能力。RECALL具体表现为训导员为了防止突然出现的危险或错误，而将犬及时地唤回。例如在搜爆犬使用过程中，如果犬对某处有明显兴奋反应，这时训导员为了防止犬触及爆炸物，而将犬及时唤回；又如在扑咬过程中，训导员如果在放出犬以后，突然意识到被扑咬对象是“无辜”的，

An Efficient Content-Based Image Retrieval System Using kNN and Fuzzy Mathematical Algorithm

The implementation of content-based image retrieval(CBIR)mainly depends on two key technologies:image feature extraction and image feature matching.In this paper,we extract the color features based on Global Color Histogram(GCH)and texture features based on Gray Level Co-occurrence Matrix(GLCM).In order to obtain the effective and representative features of the image,we adopt the fuzzy mathematical algorithm in the process of color feature extraction and texture feature extraction respectively.And we combine the fuzzy color feature vector with the fuzzy texture feature vector to form the comprehensive fuzzy feature vector of the image according to a certain way.Image feature matching mainly depends on the similarity between two image feature vectors.In this paper,we propose a novel similarity measure method based on k-Nearest Neighbors(kNN)and fuzzy mathematical algorithm(SBkNNF).Finding out the k nearest neighborhood images of the query image from the image data set according to an appropriate similarity measure method.Using the k similarity values between the query image and its k neighborhood images to constitute the new k-dimensional fuzzy feature vector corresponding to the query image.And using the k similarity values between the retrieved image and the k neighborhood images of the query image to constitute the new k-dimensional fuzzy feature vector corresponding to the retrieved image.Calculating the similarity between the two kdimensional fuzzy feature vector according to a certain fuzzy similarity algorithm to measure the similarity between the query image and the retrieved image.Extensive experiments are carried out on three data sets:WANG data set,Corel-5k data set and Corel-10k data set.The experimental results show that the outperforming retrieval performance of our proposed CBIR system with the other CBIR systems.

Validity of long-term and short-term recall of occupational sitting time in Finnish and Chinese office workers

Background:As sedentary behavior is a global health issue,there is a need for methods of self-reported sitting assessment.The accuracy and reliability of these methods should also be tested in various populations and different cultural contexts.This study examined the validity of longterm and short-term recall of occupational sitting time in Finnish and Chinese subgroups.Methods:Two cohort groups of office-based workers(58.6%female,age range 22
67 years)participated:a Finnish group(FIN,n=34)and a Chinese group(CHI,n=36).Long-term(past 3-month sitting)and short-term(daily sitting assessed on 5 consecutive days)single-item measures were used to assess self-reported occupational sitting time.Values from each participant were compared to objectively measured occupational sitting time assessed via thigh-mounted accelerometers,with Spearman’s rho(r)used to assess validity and the Bland-Altman method used to evaluate agreement.Coefficients of variation depicted day-to-day variability of time spent on sitting at work.Results:In the total study sample,the results showed that both long-term and short-term recall correlated with accelerometer-derived sitting time(r=0.532,95%confidence intervals(CI):0.336
0.684,p<0.001;r=0.533,95%CI:0.449
0.607,p<0.001,respectively).Compared to objectively measured sitting time,self-reported occupational sitting time was 2.4%(95%CI:
0.5%to 5.3%,p=0.091)and 2.2%(95%CI:0.7%
3.6%,p=0.005)greater for long-term and short-term recall,respectively.The agreement level was within the range
21.2%to 25.9%for long-term recall,and
24.2%to 28.5%for short-term recall.During a 5-day work week,day-to-day variation of sitting time was 9.4%§11.4%according to short-term recall and 10.4%§8.4%according to accelerometry-derived occupational sitting time.Conclusion:Overall,both long-term and short-term self-reported instruments provide acceptable measures of occupational sitting time in an office-based workplace,but their utility at the individual level is limited due to large variability.

Contemporary perspective on endogenous myocardial regeneration

Considering the complex nature of the adult heart, it is no wonder that innate regenerative processes, while maintaining adequate cardiac function, fall short in myocardial jeopardy. In spite of these enchaininglimitations, cardiac rejuvenation occurs as well as restricted regeneration. In this review, the background as well as potential mechanisms of endogenous myocardial regeneration are summarized. We present and analyze the available evidence in three subsequent steps. First, we examine the experimental research data that provide insights into the mechanisms and origins of the replicating cardiac myocytes, including cell populations referred to as cardiac progenitor cells(i.e., c-kit+ cells). Second, we describe the role of clinical settings such as acute or chronic myocardial ischemia, as initiators of pathways of endogenous myocardial regeneration. Third, the hitherto conducted clinical studies that examined different approaches of initiating endogenous myocardial regeneration in failing human hearts are analyzed. In conclusion, we present the evidence in support of the notion that regaining cardiac function beyond cellular replacement of dysfunctional myocardium via initiation of innate regenerative pathways could create a new perspective and a paradigm change in heart failure therapeutics. Reinitiating cardiac morphogenesis by reintroducing developmental pathways in the adult failing heart might provide a feasible way of tissue regeneration. Based on our hypothesis "embryonic recall", we present first supporting evidence on regenerative impulses in the myocardium, as induced by developmental processes.

New focused crawling algorithm

Focused carawling is a new research approach of search engine. It restricts information retrieval and provides search service in specific topic area. Focused crawling search algorithm is a key technique of focused crawler which directly affects the search quality. This paper first introduces several traditional topic-specific crawling algorithms, then an inverse link based topic-specific crawling algorithm is put forward. Comparison experiment proves that this algorithm has a good performance in recall, obviously better than traditional Breadth-First and Shark-Search algorithms. The experiment also proves that this algorithm has a good precision.

An Overview and Perspectives On Bidirectional Intelligence: Lmser Duality, Double IA Harmony,and Causal Computation

Advances on bidirectional intelligence are overviewed along three threads,with extensions and new perspectives.The first thread is about bidirectional learning architecture,exploring five dualities that enable Lmser six cognitive functions and provide new perspectives on which a lot of extensions and particularlly flexible Lmser are proposed.Interestingly,either or two of these dualities actually takes an important role in recent models such as U-net,ResNet,and Dense Net.The second thread is about bidirectional learning principles unified by best yIng-yAng(IA)harmony in BYY system.After getting insights on deep bidirectional learning from a bird-viewing on existing typical learning principles from one or both of the inward and outward directions,maximum likelihood,variational principle,and several other learning principles are summarised as exemplars of the BYY learning,with new perspectives on advanced topics.The third thread further proceeds to deep bidirectional intelligence,driven by long term dynamics(LTD)for parameter learning and short term dynamics(STD)for image thinking and rational thinking in harmony.Image thinking deals with information flow of continuously valued arrays and especially image sequence,as if thinking was displayed in the real world,exemplified by the flow from inward encoding/cognition to outward reconstruction/transformation performed in Lmser learning and BYY learning.In contrast,rational thinking handles symbolic strings or discretely valued vectors,performing uncertainty reasoning and problem solving.In particular,a general thesis is proposed for bidirectional intelligence,featured by BYY intelligence potential theory(BYY-IPT)and nine essential dualities in architecture,fundamentals,and implementation,respectively.Then,problems of combinatorial solving and uncertainty reasoning are investigated from this BYY IPT perspective.First,variants and extensions are suggested for AlphaGoZero like searching tasks,such as traveling salesman problem(TSP)and attributed graph matching(AGM)that are turned into Go like problems with help of a feature enrichment technique.Second,reasoning activities are summarized under guidance of BYY IPT from the aspects of constraint satisfaction,uncertainty propagation,and path or tree searching.Particularly,causal potential theory is proposed for discovering causal direction,with two roads developed for its implementation.

Application of ″Black Box″ to fault diagnosis of rotating machine

Presents the successful application of an accident recalling system in the Linyuan refine oil works as part of a rotating machine vibration state monitoring and fault diagnosis system which consists of vibration pre processor,comparator and plus generator, and system gives the CPU of vibration state monitoring and fault diagnose system an interrupt plus when the vibration amplitude exceed a dangerous level to enable it to sample and store the vibration data and gets the accident data timely because the interval between the happening of accident and the beginning of sampling are shorter than 1 ms.

Spanish Version of Test MIS (*) with Delayed Memory Recall Normative Values and Results in a Population with Mild Cognitive Impairment

Background: Cognitive impairment becomes more common with ageing and may benefit from intervention. In a Spanish speaking population, detection of cognitive impairment by a general practitioner in Primary Care can be a problem, as many of the standard tests target English speaking populations. The Memory Impairment Screen (MIS-A) is a validated test using English words to detect Alzheimer’s Disease (AD) and other dementias. We have modified this test to suit a Spanish speaking population and added a new component, delayed recall. We have called our new test the Memory Impairment Screen with Delayed Recall (MIS-D). Objectives: 1) To test a Spanish version of MIS-A and MIS-D. 2) To assess the discriminative validity of MIS-D as a screening tool for the amnestic variant of Mild Cognitive Impairment (aMCI) in a group of Spanish speaking people aged 65 years old and over. Methods: A case-control study of a cohort of 739 native Spanish speaking residents of Buenos Aires aged 65 years old and over, of whom 436 were healthy controls and 303 had a diagnosis of aMCI. Measurements: Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NVP) were estimated for MIS-D and MIS-A. Results: Normative values for MIS-A and MIS-D were obtained from the control population. Both age and education significantly affected these values (p < 0.0001). Control participants showed significant differences for both modalities, MIS-A and MIS-D. The cut-off for MIS-A should be 7.5 and for MIS-D, 5.5. Comparison between control population and aMCI population using ROC curve gave a result of 5.5 in MIS-D, with 97% specificity and 76% sensitivity. Conclusion: MIS-D was positively predictive of aMCI, with 97% specificity and 76% sensitivity in a sample of Spanish speaking patients aged 65 years old and over in Buenos Aires.

Intraoperative Awareness with Recall Due to Inadvertent Use of Activated Charcoal Filters

A 57-year-old woman underwent abdominal surgery with a subarachnoid block supplemented by “light” general endotracheal anesthesia consisting of a propofol infusion and a sub-MAC concentration of sevoflurane. The previous case in the same operating room had involved a malignant hyperthermia-susceptible patient, and charcoal filters had been placed in the breathing circuit as a precautionary measure. Because it had not been used on the evening beforehand, the circuit with filters was left in situ with a strip of tape indicating that it was clean. The woman’s anesthesiologist assumed that these filters were heat and moisture exchanger filters in an unused circuit and therefore did not remove them. Subsequently, the patient had awareness with intraoperative recall. This case highlights the potential for inadvertent use of activated charcoal filters with potentially catastrophic results. Such unintended utilization of these products likely can be minimized by improved labeling techniques.

Radiation recall dermatitis with dabrafenib and trametinib: A case report

BACKGROUND Radiation recall dermatitis has been defined as the "recalling" by skin of previous radiation exposure in response to the administration of certain response-inducing drugs. Although the phenomenon is relatively well known in the medical world,an exact cause has not been documented.CASE SUMMARY Here, we report the rare occurrence of radiation recall dermatitis after palliative radiotherapy for bone metastases in a metastatic melanoma patient treated with a combination of dabrafenib and trametinib.CONCLUSION We present a case of radiation recall dermatitis after completion of palliative radiotherapy while being treated with a combination of dabrafenib and trametinib. This is a very rare toxic event, and there is insufficient data to describe prevention strategies. Increased awareness and reporting of cases will help to better explain the association between targeted therapy and the radiation recall phenomenon.

Awareness during emergence from anesthesia: Features and future research directions

The anesthesia awareness with recall(AAWR) phenomenon represents a complication of general anesthesia consisting of memorization of intraoperative events reported by the patient immediately after the end of surgery or at a variable distance from it. Approximately 20% of AAWR cases occur during emergence from anesthesia. Clinically, these unexpected experiences are often associated with distress especially due to a sense of paralysis. Indeed, although AAWR at the emergence has multiple causes, in the majority of cases the complication develops when the anesthesia plan is too early lightened at the end of anesthesia and there is a lack of use, or misuse, of neuromuscular monitoring with improper management of the neuromuscular block. Because the distress caused by the sense of paralysis represents an important predictor for the development of severe psychological complications, the knowledge of the phenomenon, and the possible strategies for its prophylaxis are aspects of considerable importance. Nevertheless, a limited percentage of episodes of AAWR cannot be prevented. This paradox holds also during the emergence phase of anesthesia which represents a very complex neurophysiological process with many aspects yet to be clarified.

Efficient Feature Selection and ML Algorithm for Accurate Diagnostics

Machine learning algorithms have been deployed in numerous optimization,prediction and classification problems.This has endeared them for application in fields such as computer networks and medical diagnosis.Although these machine learning algorithms achieve convincing results in these fields,they face numerous challenges when deployed on imbalanced dataset.Consequently,these algorithms are often biased towards majority class,hence unable to generalize the learning process.In addition,they are unable to effectively deal with high-dimensional datasets.Moreover,the utilization of conventional feature selection techniques from a dataset based on attribute significance render them ineffective for majority of the diagnosis applications.In this paper,feature selection is executed using the more effective Neighbour Components Analysis(NCA).During the classification process,an ensemble classifier comprising of K-Nearest Neighbours(KNN),Naive Bayes(NB),Decision Tree(DT)and Support Vector Machine(SVM)is built,trained and tested.Finally,cross validation is carried out to evaluate the developed ensemble model.The results shows that the proposed classifier has the best performance in terms of precision,recall,F-measure and classification accuracy.

“recall election”是什么意思?

[问] [427]2003年10月7日美国加州州长、民主党人戴维斯(Gray Davis)由于争取竞选连任而谎报预算赤字遭到指责,为此加州选民举行了“recallelection”。在这次选举活动中,Davis被迫下台把州长宝座让给好莱坞动作明星、共和党人施瓦辛格(Arnold Schwarzenegger)。那么,请问“:recallelection”是什么意思? [答] 根据加州法律,选民有权通过选举对在任的不称职州长进行罢免,这种决定其是否能继续留任的选举被称为“recall election”,即罢免选举。