Reviewerly/exHarmony huggingface dataset:
Augmented Reality Systems for Improved Operating Room Workflow

INTRODUCTION: Minimally invasive surgery (MIS) is considered superior to open surgery because of the reduced recovery times, pain medication, and infection rates. However, the smaller surgical site increases the surgical difficulty as physical landmarks are no longer visible. Using an augmented reality (AR) overlay where a patient's CT or MRI information is registered to the physical patient, users would be able to view the internal patient anatomy while maintaining a small surgical site. Our goal is to determine how to best render the virtual information intuitively for surgeons while operating MIS cases. METHODS: Given AR systems available today, there are many options regarding rendering virtual objects. Using the Microsoft HoloLens, we virtually overlay two layers of the patient body (soft and hard tissue) and vary the opacity and color in real time. Surgeons wore the headset during nonoperative stages of the surgery and asked usability questions regarding the system. RESULTS: Users surveyed displayed strong predilection towards more opaque renderings of hard tissue, with an average setting of approximately 95%. Comparatively, users preferred much lower opacity for soft tissue, with an average opacity of 26.88%, and with some users wanting no visualization of soft tissue. It was also found that users at earlier stages in their training (ie, medical students and residents) preferred more contrasting colors, whereas attendings and fellows preferred grey-scaled color profiles analogous to traditional imaging modalities. CONCLUSION: Based on the results found in the usability testing, it was found that current state-of-the-art AR headsets are not currently well suited for the operating room. Ergonomics, eye fatigue, and changing lighting conditions were all undesirable factors, especially for extended use during surgery. Data gathered from the rendering tests and pain points will be further addressed in the next generation of AR headsets.
Geology of South and Southwest part of Uttar Pradesh and its Mineral Significance

The major part of Uttar Pradesh is covered by Gangatic alluvium in the north whereas the southern part is covered by peninsular terrain. This alluvium cover belongs to Holocene age and is mainly dominated by Varanasi older sediments exposed beyond the river basinal areas and Banda younger sediments exposed around the rivers flowing. The older alluvium comprises finer grained, well compacted and more mature sediments occupying extensive stretches at relatively higher elevations. The drainage pattern in region is subdendritic to dendritic type formed by hills and nalas flowing over the soil cover. The Peninsular part of Uttar Pradesh is covered by the rocks of Archean to Mesozoic age. The Bundelkhand craton nuclei mostly composed of Archean granitoids show heterogeneity in texture and composition, intruded by later phase of magmatic activity. The Mahrauni Group of the rocks exposed around Girar, Manpura and Rajaula areas in Madawara block of Lalitpur region. The Bijawar Group is exposed in Sonrai area district Lalitpur of Uttar Pradesh. The rocks of Dudhi Gneissic Complex and Mahakoshal Group are exposed in Sonbhadra and Mirzapur district. Few exposures of Ajabgarh Group rocks are exposed in Mathura district. These Bijawar Group, Mahakoshal Group and Ajabgarh Group rocks are equivalent to Delhi Supergroup. The Bundelkhand craton is capped by the sedimentary units of Vindhyan Supergroup which is exposed mainly in the southern part of Uttar Pradesh and a few exposures are observed in Agra district. The Deccan Trap rocks are also traces in Lalitpur district of Uttar Pradesh.
A GENERAL FRAMEWORK BASED ON IOT TECHNOLOGY FOR SMART GOVERNANCE

The paper presents research on the concept of intelligent government in the context of the use of IoT technologies and wireless sensor networks.Smart governance targets the future of public services and focuses on using new technologies to streamline decision-making and management processes.The extending of modern ICT capacities and the increasing need for information services systems widespread impose the need to find solutions to the interoperability problems at the technical, informational and organizational level.Our research has focused on interoperability issues and on finding solutions to the challenges process of moving from eGovernment to smart governance.Finally, the paper presents a general framework architecture for smart governance, based on new IoT, fog and Cloud technologies.It is proposed to redesign the ICT infrastructure by virtualizing network resources and data and using advanced data analysis methods to enable better collaboration between government, government institutions, the business environment and citizens.
Late Line-of-Sight Check and Prioritized Trees for Path Planning

How to generate a safe and high-quality path for a moving robot is a classical issue. The relative solution is also suitable for computer games and animation. To make the problem simpler, it is common to discretize the continuous planning space into grids, with blocked cells to represent obstacles. However, grid-based path-finding algorithms usually cannot find the truly shortest path because the extending paths are constrained to grid edges. Meanwhile, Line-of-Sight Check (LoS-Check) is an efficient operation to find the shorter any-angle path by relaxing the constraint of grid edges, which has been successfully used in Theta*. Through reducing the number of LoS-Check operations in Theta*, a variant version called LazyTheta* speeds up the planning especially in the 3D environment. We propose Late LoS-Check A* (LLA*) to further reduce the LoS-Check amount. It uses the structure of the prioritized trees to partially update the gvalues of different successors that share the same parent (i.e., the parent of the current vertex waiting to be extended). The sufficient experiments on various benchmark maps show that LLA* costs less execution time than Lazy Theta* while generating shorter paths. If we just delay the LoS-Check, the path planned by LLA* will hardly be shorter than that of Lazy Theta*. Therefore, the key of LLA* is the discriminatory strategy, and we empirically explain the reason why both the path length and execution time of LLA* are shorter than those of Lazy Theta*.
Interpretable Cognitive Diagnosis with Neural Network for Intelligent Educational Systems.

Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts. Existing approaches usually mine linear interactions of student exercising process by manual-designed function (e.g., logistic function), which is not sufficient for capturing complex relations between students and exercises. In this paper, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework, which incorporates neural networks to learn the complex exercising interactions, for getting both accurate and interpretable diagnosis results. Specifically, we project students and exercises to factor vectors and leverage multi neural layers for modeling their interactions, where the monotonicity assumption is applied to ensure the interpretability of both factors. Furthermore, we propose two implementations of NeuralCD by specializing the required concepts of each exercise, i.e., the NeuralCDM with traditional Q-matrix and the improved NeuralCDM+ exploring the rich text content. Extensive experimental results on real-world datasets show the effectiveness of NeuralCD framework with both accuracy and interpretability.
Face and Finger Vein Recognition for Security Based Electronic Voting Machine Using Raspberry Pi

The assist device for blind people is much needed in recent days. The gadgets in recent trends are digitized that the blind people are not capable of handling digitized applications hence in our project we design text to voice converter, that converts the eBooks, audiobooks to voice format. In this project the audiobook eBooks are fed into SD card reader after that the sd card reader is interfaced and the Arduino UNO converts the text file to Audio output. The audio books can be heard in headphone\speaker using Arduino UNO. This project is low cost it will be more useful for blind people. Secure voting must be necessary to avoid the fake voting. The problem of fake voting can be solved by using Face detection and Finger vein based Electronics Voting Machine (EVM) system. In electronic voting system the details of the voters are stored; the voting date is recorded and processed digitally. Most of the existing Electronic voting machines has been developed using Radio Frequency ID. In developing countries RFID for each person is not possible due to high cost efficiency. Image processing based EVM also introduced, but it not provides accurate results for the twin persons. The EVM with finger print also not providing the accurate results. So the modified EVM with face and finger vein was introduced to obtain the satisfied result. The Raspberry Pi plays major roll in this project. It is act as a on board CPU work on the operating system called rasbian OS. The necessary hardwires like, camera, finger vein scanner, EVM are connected with Rasperry Pi. The datas of all the voters along with the images of the face and the finger vein are already stored in the system. The camera capture the picture of the person then compare with the stored data and find whether the person is authorized person or not. Once the person is authorized then the next section scan the finger and compare with the data and inform to the system that whether known or unknown. If both face and finger vein are recognized then the voter can register their vote. The fake voting can be completely avoid using this voting method.
Usability Impact of Occlusion-free Techniques on Commonly-used Multitouch Actions

Many interaction techniques have been proposed to combat the finger occlusion and precision problem in multitouch platforms such as tablets and smartphones, supporting more accurate ways to select a target item with a finger. Recognising how the typical touch interactions on multitouch platforms are becoming more complex and composite, in this paper we study some combinations of basic occlusion-free/precision finger interaction techniques in the context of commonly-used composite user actions in realistic usage scenarios including copying and pasting a portion of text and drawing a curved line. We develop full-fledged tablet applications featuring the techniques to assess their usability impact on the rest of the actions. Usability testing with 25 participants qualitatively reveals how the integration of these techniques is useful for their intended part (i.e. accurate targeting) but negatively influence the remaining parts of the action. Insights from the study and areas that will benefit further investigation are discussed.
Face It!: A Pipeline for Real-Time Performance-Driven Facial Animation

This paper presents a new lightweight approach for real-time performance-driven facial animation from monocular videos. We transfer facial expressions from 2D images to a 3D virtual character, by estimating the rigid head pose and non-rigid face deformation from detected and tracked 2D facial landmarks. We map the input face into the facial expression space of the 3D head model using blendshape models and formulate a lightweight energy-based optimization problem, which is solved by non-linear least squares at 18 FPS on a single CPU. Our method robustly handles varying head poses and different facial expressions, including moderately asymmetric ones. Compared to related methods, our approach does not require training data, specialised camera setups or graphics cards, and is suitable for embedded systems. We support our claims with several experiments.
UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference

Recent advances in distributed language modeling have led to large performance increases on a variety of natural language processing (NLP) tasks.However, it is not well understood how these methods may be augmented by knowledge-based approaches.This paper compares the performance and internal representation of an Enhanced Sequential Inference Model (ESIM) between three experimental conditions based on the representation method: Bidirectional Encoder Representations from Transformers (BERT), Embeddings of Semantic Predications (ESP), or Cui2Vec.The methods were evaluated on the Medical Natural Language Inference (MedNLI) subtask of the MEDIQA 2019 shared task.This task relied heavily on semantic understanding and thus served as a suitable evaluation set for the comparison of these representation methods.
Solve fraud detection problem by using graph based learning methods

: The credit cards’ fraud transactions detection is the important problem in machine learning field. To detect the credit cards’ fraud transactions help reduce the significant loss of the credit cards’ holders and the banks. To detect the credit cards’ fraud transactions, data scientists normally employ the un-supervised learning techniques and supervised learning technique. In this paper, we employ the graph p-Laplacian based semi-supervised learning methods combined with the under-sampling technique such as Cluster Centroids to solve the credit cards’ fraud transactions detection problem. Experimental results show that that the graph p-Laplacian semi-supervised learning methods outperform the current state of art graph Laplacian based semi-supervised learning method (p=2).
A Face Detection Method Based on Sliding Window and Support Vector Machine

Face detection is a biometric technology based on human face features for identity authentication.With the development of e-commerce and other applications, face recognition has become the most potential means of biometric authentication.Classical face recognition is based on statistical methods, but the accuracy of this method is not high.In this paper, a face detection method based on the sliding window and support vector machine is proposed.Firstly, the image is divided into blocks, and the HOG features of the target image are extracted.Then the support vector machine model is trained through the data sets of human face and non-face.The support vector machine model can detect whether the target area belongs to the face area or not.Finally, the whole face area is detected by the sliding window model.Experiments verify the effectiveness of the proposed method.
Multimedia password retrieval programs in information security education

The main objective of our research is to examine the impact of the information security course on students' habits, attitudes, and everyday security awareness, furthermore, to analyze and develop educational methods.In this study, two groups are compared.In Group 1, students were watching video recordings exemplifying password decryption by using programs.In Group 2, students could test these programs as well.When assessing the effectiveness of learning, exams focus on knowledge and skills, though hardly give insight into the change in attitude.Therefore, we tried to reveal the impact appearing in students' personal lives by examining their password and device usage.Before and after the course an online survey was carried out among safety engineering students.Answers about password management were scored according to their safety, or security risk.Comparisons of the groups and analysis of changes were performed by descriptive statistics and nonparametric tests.Based on multivariate statistical methods and qualitative analysis of the students' comments on their password habits, we evaluated the changes in the dimensions of security and awareness.In Group 1, password usage patterns did not change significantly.In Group 2, a significant improvement was demonstrated in the diversity and length of passwords, variety of character types used in passwords, frequency of password change, and in the use of foreign Wi-Fi.In conclusion, the mixed educational method facilitating student activity had greater impact on students' information security practices and awareness than the method applying only video demonstration which provides purely a passive reception.
A New Approach on Skull Stripping of Brain MRI based on Saliency Detection using Dictionary Learning and Sparse Coding

In brain magnetic resonance images (brain MRI) analysis, for diagnosing certain brain conditions, it is necessary to quantify the brain tissue, which implies to separate the brain from extracranial or non-brain tissues through a process of isolation known as skull stripping. This is a non-trivial task since different types of tissues may have the same gray level, and during the separation process, some brain tissues could be removed. This paper presents a new solution approach for the skull stripping problem, based on saliency detection using dictionary learning and sparse coding, which can operate over T1 and T2 weighted axial brain MRI. Our method first subdivides the axial MRI into full overlapped patches and runs a dictionary learning over them for obtaining its sparse representation. Then, by analyzing the sparse coding matrix, we compute how many patches a dictionary atom affects to classify them as frequent or rare. Then, we calculate the saliency map of the axial MRI according to the composition of the image patches, i.e. an image patch is considered salient if it is mainly composed of frequent atoms, an atom is frequent whether it affects many patches. The non-salient pixels, corresponding to non-brain tissues, are eliminated from the MRI. Numerical results validate our method
Analysis of latent Dirichlet allocation and non-negative matrix factorization using latent semantic indexing

A word is a major attribute in the field of opinion/text mining.Based on this attribute, it is decided that whether it is a keyword, aspect, feature, entity, title, or topic?Lots of work has been done to detect such targets using both supervised and unsupervised approaches.These targets can be used in further processing such as text analytics, sentiment analysis, information retrieval, and searches, etc. Latent Dirichlet allocation (LDA) and nonnegative matrix factorization (NMF) are the major models used for detecting topics.Understanding the depth and details of them algorithms are necessary for those who want to extend these models.The research community of opinion/text mining uses them as a black box.However, there is a question about which model is the most accurate for detecting topics.Latent semantic indexing (LSI) is the best approach for detecting the best match for document in a given query.In this study, we analyzed the LDA and NMF models using LSI to determine the best model for opinion/text mining and found that both are very good, but NMF is slightly better than LDA.
Effort-Aware Neural Automatic Post-Editing

For this round of the WMT 2019 APE shared task, our submission focuses on addressing the "over-correction" problem in APE.Overcorrection occurs when the APE system tends to rephrase an already correct MT output, and the resulting sentence is penalized by a reference-based evaluation against human post-edits.Our intuition is that this problem can be prevented by informing the system about the predicted quality of the MT output or, in other terms, the expected amount of needed corrections.For this purpose, following the common approach in multilingual NMT, we prepend a special token to the beginning of both the source text and the MT output indicating the required amount of post-editing.Following the best submissions to the WMT 2018 APE shared task, our backbone architecture is based on multi-source Transformer to encode both the MT output and the corresponding source text.We participated both in the English-German and English-Russian subtasks.In the first subtask, our best submission improved the original MT output quality up to +0.98 BLEU and -0.47 TER.In the second subtask, where the higher quality of the MT output increases the risk of over-correction, none of our submitted runs was able to improve the MT output.
Feature Fusion and Voiceprint-Based Access Control for Wireless Insulin Pump Systems

Without effective cryptographic mechanisms, the wireless channel between the USB/uploader and insulin pump frequently suffers from vulnerabilities. Either eavesdropping or therapy manipulation attacks would put the patients in a life-threatening situation. Towards tackling this problem, we propose an access control scheme by introducing feature fusion and voiceprint. Featured by the anti-replay speaker verification and voiceprint-based key agreement, it secures communications over the wireless channel. Through a cascaded fusion of speaker verification and anti-replay countermeasure, the anti-replay speaker verification guarantees that the pump can only be accessed after the verification. When defending against zero-effort and replay impostors with our scheme, the equal error rate can be reduced to 2.22%. Furthermore, to generate a common key for the wireless channel, in the voiceprint-based key agreement, we present a non-interactive energy-difference-based voiceprint extraction and adaptive Reed-Solomon coding based fuzzy extractor. Thus, it enhances the communication encryption which protects the pump from eavesdropping and therapy manipulation attacks. Also, with an appropriate constraint on voiceprints similarity, the key agreement lowers the risk of channel establishment from device locating outside the pump's close proximity.
Detection and Synchronization of Video Sequences for Event Reconstruction

With an ever-growing amount of unexpected menaces in crowded places such as terrorist attacks, it is paramount to develop techniques to aid investigators reconstructing all details about an event of interest. To extract reliable information about the event, all kinds of available clues must be jointly exploited. As a matter of fact, today's sources of information are plenty and varied, as important events affecting many people are typically documented by different sources. Both witnesses' smartphones and security cameras can provide valuable information coming from multiple viewpoints and time instants - "the eyes of the crowd". In this paper, we focus on the specific problem of automatically detecting and temporally synchronizing videos depicting the same event of interest. Videos can be either near-duplicates (i.e., edited copies of the same original source) or sequences shot by different users from different vantage points. The proposed method relies upon a video fingerprinting technique capable of describing how video semantic content evolves in time. The solution does not assume a priori information about cameras location, and it only exploits visual cues, not relying on audio channels.
Panoramic View of Cloud Storage Security Attacks: an Insight and Security Approaches

Recent years the computer technologies are reaching every corner in the world through the cloud computing services. Cloud storage has many advantages and it allows the user to store a large amount of critical and sensitive data in the cloud. As we store the critical information in the cloud, the information attracts the highly skilled hackers to access the data in the cloud by breaching the security mechanisms. This paper discusses specific attacks related to efficient and secure storage of data in cloud. Specifically, we outline network-based, browser-based and social network-based attacks. We classify the mechanisms and prominent security systems available to handle the challenges faced by these security threats. The study contributes to the overview of drawbacks in state of art mechanisms and it's mitigations techniques to protect the outsourced data. The analysis enables to develop a suitable architecture for efficient and secure storage which outperforms the existing systems with low performance, high communication overheads, high computation cost, less efficiency, limited access for a system, less number of security properties and difficulty in handling the system.