Reviewerly/exHarmony huggingface dataset:
A Fast Teacher Pose Estimation Framework Base on Kernelized Correlation Filter and Spatial Transformed High-Resolution Network

Smart Education gradually becomes more of a concern for people, it providing sharable education resources, diversified teaching methods and data-based teaching evaluation methods. Virtual teacher is a part of smart education and it helps on improving learners’ cognitive level. The difficulty of virtual teacher technology lies in accurately recording and reproducing the actions and postures of teachers. It requires an accurate and fast teacher pose estimation algorithm as a support. This paper proposes a teacher pose estimation framework based on KCF and ST-HRN. Firstly, the target detection network YOLOv3 and the similarity algorithm based on SIFT feature are used to predict the position of the teacher in the video, and the KCF target tracking algorithm is used to accelerate the calculation. Secondly, based on the high-resolution network, the ST-HRN single-person pose estimation model is proposed and trained, and the average [email protected]=90.2% accuracy is obtained on the MPII dataset. Finally, the framework is tested on class teaching videos and have 30.8% estimate speed promotion by implementing tracking algorithm.
A Two-Station Passive Locating Solution Independent of the Baseline Length

The two-station positioning system based on time difference and azimuth measurement has measurement redundancy.Therefore, not only can a positioning solution which is completely independent of the baseline length between two stations be derived, but also the baseline length can be solved as an unknown quantity.These findings not only enhance the performance of the two-station positioning system, but also provide a design basis for the construction of a self-organizing dynamic intelligent positioning system.
Reader characteristics and mammogram features associated with breast imaging reporting scores

This study aims to explore the reading performances of radiologists in detecting cancers on mammograms using Tabar Breast Imaging Reporting and Data System (BIRADS) classification and identify factors related to breast imaging reporting scores.117 readings of five different mammogram test sets with each set containing 20 cancer and 40 normal cases were performed by Australian radiologists. Each radiologist evaluated the mammograms using the BIRADS lexicon with category 1 - negative, category 2 - benign findings, category 3 - equivocal findings (Recall), category 4 - suspicious findings (Recall), and category 5 - highly suggestive of malignant findings (Recall). Performance metrics (true positive, false positive, true negative, and false negative) were calculated for each radiologist and the distribution of reporting categories was analyzed in reader-based and case-based groups. The association of reader characteristics and case features among categories was examined using Mann-Whitney U and Kruskal-Wallis tests.38% of cancer-containing mammograms were reported with category 3 which decreased to 32.3% with category 4 and 16.2% with category 5 while 16.6 and 10.3% of cancer cases were marked with categories 1 and 2. Female readers had less false-negative rates when using categories 1 and 2 for cancer cases than male readers (p < 0.01). A similar pattern as gender category was also found in Breast Screen readers and readers completed breast reading fellowships compared with non-Breast Screen and non-fellowship readers (p < 0.05). Radiologists with low number of cases read per week were more likely to record the cancer cases with category 4 while the ones with high number of cases were with category 3 (p < 0.01). Discrete mass and asymmetric density were the two types of abnormalities reported mostly as equivocal findings with category 3 (47-50%; p = 0.005) while spiculated mass or stellate lesions were mostly selected as highly suggestive of malignancy with category 5 (26%, p = 0.001).Most radiologists used category 3 when reporting cancer mammograms. Gender, working for BreastScreen, fellowship completion, and number of cases read per week were factors associated with scoring selection. Radiologists reported higher Tabar BIRADS category for specific types of abnormalities on mammograms than others.The study identified factors associated with the decision of radiologists in assigning a BIRADS Tabar score for mammograms with abnormality. These findings will be useful for individual training programs to improve the confidence of radiologists in recognizing abnormal lesions on screening mammograms.
This AI can see the forest and the trees

In 2007, one of us (Parisa) found himself standing alone in the woods of Armenia and fighting off a rising feeling of dread. Armenia, a former Soviet-bloc country, is about the size of Maryland. Its forests provide residents with mushrooms and berries, habitat for game animals, and firewood to heat homes during the cold winters. The forests also shelter several endangered bird species.
Designing the Architecture of a Blockchain Platform

The financial services industry is experiencing—or better said, is suffering—an enormous paradigm shift. New technologies, combined with new consumers, are redefining the way financial institutions design products and deliver services. Furthermore, Mastercard has filed four blockchain patents to improve settlement systems. The Alastria case, explored in this paper, is a still rare phenomenon of national blockchain as a country territorial and jurisdictional case of public permissioned blockchain oriented to combine the satisfaction of public and private interests with full legal compliance.
A Novel Communication System For Deaf And Dumb People using gesture

Human Beings know each other and contact with themselves through thoughts and ideas.The best way to present our idea is through speech. Some people don’t have the power of speech; the only way they communicate with others is through sign language. Now a days technology has reduced the gap through systems which can be used to change the sign language used by these people to speech. Sign language recognition (SLR) and gesture-based control are two major applications used for hand gesture recognition technologies. On the other side the controller converts the sign language in to the text and speech which gets converted with the help of text to speech conversion and analog to digital conversion. A Dumb person throughout the world uses sign language for the communication.The best way to present our idea is through speech. Some people don’t have the power of speech; the only way they communicate with others is through sign language. Now a days technology has reduced the gap through systems which can be used to change the sign language used by these people to speech. Sign language recognition (SLR) and gesture-based control are two major applications used for hand gesture recognition technologies. On the other side the controller converts the sign language in to the text and speech which gets converted with the help of text to speech conversion and analog to digital conversion. A Dumb person throughout the world uses sign language for the communication.
Violence Detection using Embedded GPU

Since the CCTV cameras been introduced in this world, society has started to depend heavily on the usage of this technology for the high security purposes in most of the public and private areas. It is convenient to use these CCTV footages in courts as evidence and has been beneficial many times. But these footages are given priority and checked later when the incident has already taken place and that too after some period of time and not in real-time of happening. The screening of the multiple CCTV footages on a single monitor is done with very less efficiency as the ratio of number of CCTV footages to that of number of surveillance staff is very high. Also, the human unreliable supervision due to many reasons like tiredness from physical or mental effort, worker boredom, or discontinuous observation make the surveillance more inefficient. To address the issue and automatically detect the violent scenes using surveillance cameras and Embedded GPU in real-time we have developed this project for the benefit of our society. As the alert is generated in real-time, the security can take action immediately to prevent any further damage or mishappening in the crowd. Our primary objective is to automatically differentiate between violent activities and non-violent activities through CCTV surveillance cameras and automatically display the security alert on the screen as soon as any violent activity is captured and thus ensuring the safety of our society.
Generative Classifiers as a Basis for Trustworthy Computer Vision.

With the maturing of deep learning systems, trustworthiness is becoming increasingly important for model assessment. We understand trustworthiness as the combination of explainability and robustness. Generative classifiers (GCs) are a promising class of models that are said to naturally accomplish these qualities. However, this has mostly been demonstrated on simple datasets such as MNIST, SVHN and CIFAR in the past. In this work, we firstly develop an architecture and training scheme that allows for GCs to be trained on the ImageNet classification task, a more relevant level of complexity for practical computer vision. The resulting models use an invertible neural network architecture and achieve a competetive ImageNet top-1 accuracy of up to 76.2%. Secondly, we show the large potential of GCs for trustworthiness. Explainability and some aspects of robustness are vastly improved compared to standard feed-forward models, even when the GCs are just applied naively. While not all trustworthiness problems are solved completely, we argue from our observations that GCs are an extremely promising basis for further algorithms and modifications, as have been developed in the past for feedforward models to increase their trustworthiness. We release our trained model for download in the hope that it serves as a starting point for various other generative classification tasks in much the same way as pretrained ResNet models do for discriminative classification.
Assessing Textbook Costs at a Small College

In recent years, the cost of textbooks has risen drastically, making it an important issue for everyone involved in higher education. Accurate and specific information on the cost of textbooks is needed to effectively address the situation. Over the summer of 2018, a librarian and an undergraduate student at a small liberal arts college assessed the textbook costs at their institution, using methodology developed by community colleges doing similar research. Based on course catalogs, bookstore records, and other resources, they calculated the costs for students in different disciplines over four years of study. These costs were impacted by major, courses chosen, and where materials were purchased. Patterns included higher prices in the sciences than in the humanities, as well as more expensive textbooks at the college bookstore than at other sources. Estimated total textbook costs varied widely (from under $100 to over $7,000 for four years of study), leading the researchers to conclude that no single average was representative of any one student’s experience.
Cyber-attacks on Saudi Arabia Environment

Even though cyber-attacks can’t be compared to a nuclear attack but they both pose a serious threat to national and international security. As we witness today cyberwarfare is increasing and Saudi Arabia has become a major target of Cyber-attacks as a result of its economic and digital revolution, high technology adoption and the growth of the gas and oil industry. This paper presents, a case study of the cyber-attacks on the Saudi environment. We focused on two specific malwares Shamoon and Mamba Ransomware. It also explores their methodologies and structure for future defense
Hindi Language Interface to Database

In our everyday lives we require information to accomplish daily tasks. Database is one of the most important sources of information. Database systems have been widely used in data storage and retrieval. However, to extract information from databases, we need to have some knowledge of database languages like SQL. But SQL has predefined structures and format, so it is hard for the non-expert users to formulate the desired query. To override this complexity, we have turned to natural language to retrieve information from database, which can be an ideal channel between a non-technical user and the application. But the application cannot understand natural language so an interface is required. This interface is capable of converting the user’s natural language query to an equivalent database language query. In this paper, we address the system architecture for translating a Hindi sentence in the form of an audio to an equivalent SQL query. The users don’t need to learn any formal query language; hence it’s easy to use for common people.
GLIMG: Global and Local Item Graphs for Top-N Recommender Systems

Graph-based recommendation models work well for top-N recommender systems due to their capability to capture the potential relationships between entities. However, most of the existing methods only construct a single global item graph shared by all the users and regrettably ignore the diverse tastes between different user groups. Inspired by the success of local models for recommendation, this paper provides the first attempt to investigate multiple local item graphs along with a global item graph for graph-based recommendation models. We argue that recommendation on global and local graphs outperforms that on a single global graph or multiple local graphs. Specifically, we propose a novel graph-based recommendation model named GLIMG (Global and Local IteM Graphs), which simultaneously captures both the global and local user tastes. By integrating the global and local graphs into an adapted semi-supervised learning model, users' preferences on items are propagated globally and locally. Extensive experimental results on real-world datasets show that our proposed method consistently outperforms the state-of-the art counterparts on the top-N recommendation task.
Phishing-Inspector: Detection & Prevention of Phishing Websites

With a tremendous boost in technologies &amp; available learning material developing any website has become very easy. Due to this the number of websites are exponentially growing day by day. The traditional approach of comparing websites with Blacklist and whitelist is not so efficient. As attackers have become more intelligent regarding hiding and redirecting of the URL and thereby tricking the user into phishing attack without been getting detected. So there’s a need to have a novel approaches based on Machine learning (ML) which would expose this phishing websites. In this paper, the proposed system is an extension to web browser which made use of ML algorithm to extract various features and thereby helping the user to distinguish between Legitimate website and Phishing website.
Developing a How-to Tip Machine Comprehension Dataset and its Evaluation in Machine Comprehension by BERT

In the field of factoid question answering (QA), it is known that the state-of-the-art technology has achieved an accuracy comparable to that of humans in a certain benchmark challenge.On the other hand, in the area of nonfactoid QA, there is still a limited number of datasets for training QA models, i.e., machine comprehension models.Considering such a situation within the field of the non-factoid QA, this paper aims to develop a dataset for training Japanese how-to tip QA models.This paper applies one of the state-of-the-art machine comprehension models to the Japanese how-to tip QA dataset.The trained how-to tip QA model is also compared with a factoid QA model trained with a Japanese factoid QA dataset.Evaluation results revealed that the how-to tip machine comprehension performance was almost comparative with that of the factoid machine comprehension even with the training data size reduced to around 4% of the factoid machine comprehension.Thus, the how-to tip machine comprehension task requires much less training data compared with the factoid machine comprehension task.
Review of Fault Tolerance Frameworks in the Cloud

Fault tolerance is the most imperious issue in the cloud to provide reliable services. Inherent vulnerability to failure hampers the performance and reliability of cloud services. Hence, to achieve reliability, fault tolerance becomes a mandatory feature which is hard to implement due to the dynamic infrastructure and complex interdependencies. Numerous fault tolerance techniques have been developed in the literature to address the challenges of cloud reliability. A recent research survey presented in this paper attempts to integrate the different fault tolerance architecture. This study presents a critical research review on various existing fault tolerance techniques to improve services reliability, availability, and applications execution in the cloud. A comparative analysis, based on different critical metrics like failure prediction, detection strategy, failure history, VM placement, and limitations, of the reviewed framework systems is also included in the paper. This review intends to facilitate the development of the new fault tolerance technique for the cloud environment.
Original vs Counterfeit Indian Currency Detection

Currency is used to carry out not only business but also for various other transactions to get access to various services and commodities. There are a total of 7 denominations for the Indian currency each with unique features to distinguish them from each other and with various and distinct security features to prevent them from fraudulent copying. However,with the evolution of technology, there is also an increase in the ways in which fake forms of these currencies are created. These fake or counterfeit notes have various ill-effect on society. The proposed system will be used to check the genuine Indian currency notes and to find the denomination of the currency note. Comparative study for various image processing algorithms was conducted to identify and select the one which will be able to extract more prominent features,and is also better in terms of processing time,outlier rejection, efficiency in computation and in feature matching. After which a real time system is created for currency detection in real time.
PCB Defect Recognition and Elimination Based on Secondary Error and Statistical Histogram

Aiming at the difficulty of missing, wrong and reverse detection in PCB component detection, the paper proposes a new PCB defect detection and elimination based on visual recognition technology and computer-aided system, it can find out target more efficiently by visual inspection technology in the non-stop line. The target region was marked through secondary error matching algorithm, the threshold segmentation graph of original images were investigated in the marked region. The recognition capability for missing and wrong detection was enhanced. The component matching compatibility was analyzed by mask operation and statistical histogram algorithm. The recognition capability for polarity detection was enhanced. The results were judged by computer aided evaluation system that was carried out under the environment of Vision Pro and Visual Studio. The dynamic position of defective component was calculated by PLC. The operation results showed that through the proposed method, the recognition rate was 100%, and the false alarm rate was about 3% in the detection of anti logic, color components, pins and capacitance, and meet the needs of real-time detection and elimination.
Sentiment Classification Using Hybrid Bayes Theorem Support Vector Machine Over Social Network

Opinions or information can be shared on social media sites including, LinkedIn, blogs, Facebook, twitter, etc., in text form. Opinions or views about movies, products, politics or any interested topics of user can be shared using social networking sites in comments or feedback or picture form. Individuals opinion about political events, social, issues and products can be gathered and analysed by sentiment analysis. The proposed system includes preprocessing, feature extraction, sentiment classification using hybrid Bayes theorem support vector machine (HBSVM) algorithm. Preprocessing is used for removing unnecessary data, and it helps to improve the classification accuracy in the given dataset. Then, feature extraction is performed to select the prominent features based on the frequent terms. Then, apply HBSVM model for classifying neutral and non-neutral posts. Negative and positive are the class of non-neutral posts. Based on response to a post of various aspects, classification of group members is done. High performance is exhibited by proposed HBSVM as proven by results of experimentation when compared with existing techniques.
ProDeblurGAN: Progressive Growing of GANs for Blind Motion Deblurring in Face Recognition

Elimination of motion blur is one of the key challenges in face recognition, especially from the video feed of low quality surveillance cameras. We present ProDeblurGAN, a progressively growing Generative Adversarial Nets (GAN) for single photograph motion deblurring. The current state-of-the-art DeblurGAN network performs well in structural similarity and visual appearance. However, the discriminator’s inability in distinguishing real and fake images concerning to finer or lower-level details result in the generator synthesizing low quality deblurred image. With recent advances in the progressive growing of GANs, we propose a motion deblurring model to utilize the advantages of such progressive adversarial training and generate a high quality deblurred image. The approach to gradually increase layers in training instead of initializing all at once facilitates the generation of higher resolution images. Also, at higher resolution, the finer details like the facial hair, moles, freckles and so on, are considered as features and used in the learning process. The proposed generator takes the blurred image as input as opposed to random noise in the case of traditional GAN networks. Implementation of the proposed method in face recognition systems will enhance the quality of the captured image and the accuracy of recognition of the face. The quality of the deblurred image generated by ProDeblurGAN is evaluated using state-of-the-art metrics.
Multi-scale SR-μXRF Imaging and Characterization of Gold Mineralization at the Monument Bay Deposit, Stull Lake Greenstone Belt, Manitoba, Canada

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Parameterized interpolation for fusion of multidimensional signals of various resolutions

Parameterized interpolation algorithms are adapted to fusion of multidimensional signals of various resolutions. Interpolating functions, switching rules for them and local features are specified, based on which the interpolating function is selected at each point of the signal. Parameterized interpolation algorithms are optimized based on minimizing the interpolation error. The recurrent interpolator optimization scheme is considered for the situation of inaccessibility of interpolated samples at the stage of setting up the interpolation procedure. Computational experiments are carried out to study the proposed interpolators for fusion of real multidimensional signals of various types. It is experimentally confirmed that the use of parameterized interpolators allows one to increase the accuracy of signal fusion.