Reviewerly/exHarmony huggingface dataset:
A New Remote User Authentication Scheme for E-Health-Care Applications Using Steganography

The internet is getting more popular these days and the applications that help human lives are spreading across the world, one useful area to mention is healthcare, healthcare applications are increasing very fast to ease human life. In order to overcome security challenges, we need to secure these applications, such that privacy and authentication needs many techniques to be defined. In this paper, we presented a comprehensive survey of collection of remote user authentication schemes used in Telecare Medicine Information Systems (TMISs), and we compared between them, according to used factors in some security aspects. Then we proposed a new three-factor authentication scheme for the TMIS. The proposed scheme provides the user anonymity, the telecare server cannot extract the password and there is a freedom of password update. Moreover, the new scheme used steganography method on a biometrics to add some level of obfuscation. These leads to get more secure and practical scheme.
Cave

CAVE is a shared narrative six degrees of freedom (6DoF) virtual reality experience. In 3.5 days, 1,927 people attended its premiere at SIGGRAPH 2018. Thirty participants at a time each saw and heard the same narrative from their own individual location in the room, as they would when attending live theater. CAVE set out to disruptively change how audiences collectively experience immersive art and entertainment. Inspired by the social gatherings of theater and cinema, CAVE resonated with viewers in powerful and meaningful ways. Its specific pairing of colocated audiences and physically shared immersive narrative suggests a possible future path for shared cinematic experiences.
Kraaler: A User-Perspective Web Crawler

Adaption of technologies being used on the web is changing frequently, requiring applications that interact with the web to continuously change their ability to parse it. This has led most web crawlers to either inherent simplistic parsing capabilities, differentiating from web browsers, or use a web browser with high-level interactions that restricts observable information. We introduce Kraaler, an open source universal web crawler that uses the Chrome Debugging Protocol, enabling the use of the Blink browser engine for parsing, while obtaining protocol-level information. The crawler stores information in a database and on the file system and the implementation has been evaluated in a predictable environment to ensure correctness in the collected data. Additionally, it has been evaluated in a real-world scenario, demonstrating the impact of the parsing capabilities for data collection.
MLTS: A Multi-Language Scene Text Spotter

Scene text detection and recognition are popular research topics in computer vision due to its various applications such as autonomous driving, blind assistance and text translation. However, many methods currently can only detect or recog-nize the text of one language. In scene text images, we can often see text in multi-language appearing on the same image. However, there is no valid model for multi-language text spotting. In this paper, an end-to-end method for multi-language scene text detection, recognition and script identification is proposed. The method, called MLTS, is an abbreviation of a Multi-Language Scene Text Spotter. By designing a special backbone for text and combining two different kinds of attention. MLTS achieves state-of-the-art performance for both joint localization and script identification in natural images and in cropped word script identification, the precision, recall and F-measure are 0.7145, 0.6583 and 0.6852 respectively, while the corresponding values of the best existing methods are 0.5759, 0.6207, 0.5974 respectively. Additionally, our MLTS achieves comparable performance on ICDAR2013 and ICDAR2015, which proves the effectiveness of the model.
Local equivalence of qudit color codes and toric codes

In this paper, we study nonbinary toric codes and color codes. While the relationships between the qubit color codes and toric codes have been studied extensively from various perspectives, similar studies for qudit toric codes and color codes are missing. We show that just as in the binary case, qudit color codes can be mapped to qudit toric codes. Qudit color codes are known to have the capability for implementing the generalized Clifford group transversally. The proposed mapping of qudit color codes to toric codes would allow us to port the computational capabilities of the qudit color codes to qudit toric codes. Another application would be to decode qudit color codes via qudit toric codes. Along the way, we show an important structural result for higher-dimensional qudit toric codes. Specifically, we show that the toric codes over prime power dimension can be viewed as a direct sum of copies of codes over the prime dimension. This result simplifies the study of codes over prime power dimension by reducing them to the study of codes over the prime dimension. Furthermore, we provide explicit circuits for the transformation between qudit color codes and toric codes.
Lightweight Accountable Privacy-Preserving Protocol Allowing the Cloud Client to Audit the Third-Party Auditor for Malicious Activities

Cloud computing is reserving its position in the market as the next disruptive utility paradigm. It is found on the pay-as-you-use model. Cloud computing is changing the way information technology (IT) operates for individuals as well as for companies. Cloud computing comes with different offerings to accommodate diverse applications. It comes with many successful adoption stories and a few unfortunate ones that are related to security breaches. Security concerns are what is making many companies reluctant to fully embrace the cloud realm. To enhance trust and entice adoption between cloud clients (CC) and cloud service providers (CSP), a new paradigm of depending on involving a third-party auditor (TPA) has been introduced. Hence, implementing a solution with a TPA comes with its toll in terms of trust and processing overhead. A lightweight security protocol to give the CC extra control with tools to audit the TPA and the CSP is paramount to the solution. In this paper, we are introducing a novel protocol: the lightweight accountable privacy-preserving (LAPP) protocol. Our proposed protocol is lightweight in terms of processing and communication costs. It is based on a newly introduced mathematical model along with two algorithms. We have conducted simulation experiments to measure the impact of our method. We have compared LAPP to the most eminent privacy-preserving methods in the cloud research field, using the open source cloud computing simulator GreenCloud. Our simulation results showed superiority in performance for LAPP in regard to time complexity, accuracy, and computation time on auditing. The aim of the time complexity and computation time on auditing simulations is to measure the lightweight aspect of our proposed protocol as well as to improve the quality of service.
Spatial Mask ConvLSTM Network and Intra-Class Joint Training Method for Human Action Recognition in Video

For action recognition, attention model is widely used, but most of them lack consideration of the relationship of spatial and temporal information. We thus propose a Spatial Mask ConvLSTM Network (SM_ConvLSTM-Net) to determine the attention score of each pixel position. SM_ConvLSTM-Net is used to combine the information of space and time for getting more precise spatial mask, which has a long receptive field in time domain. Furthermore, to combine the connection of different samples from same category, a novel training method called intra-class joint training method is proposed to make network extract the common characteristics related to actions of the same class in different background. Extensive experiments illustrate the effectiveness of our method and our method significantly outperforms the baseline C3D network on UCF101 and HMDB51. Moreover, our approach achieves the best performance on UCF101 and a compared result on HMDB51 in comparison to some state-of-the-art approaches with RGB input.
Comprehensive analysis of trends in software engineering Q&amp;A site

Stack Exchange Q&A website is a major platform of sharing knowledge about number of topics in various domains like Arts, technology, science, communication, literature etc. through Questions & Answers. In this paper, we are analyzing the textual content of Software Engineering, Stack Exchange Q&A website in various dimensions. The dimensions LDA based topic modelling, temporal trends of topics over time, technology impact, technology trends and their popularity. The analysis shows that wide range of topics are discussed in the Software Engineering Q& A website amongst the most popular are Programming skills, object-oriented design and design and development.
Developing a Statistical Turkish Sign Language Translation System for Primary School Students

As the access to information in the education domain increases, new technologies are developing for school children. However, deaf and dumb children still have limited access to the information, especially in their school lives. One of the most important reasons for this problem is the lack of studies in the Sign Language domain. In this paper, we propose a novel method for translation from Turkish to Turkish Sign Language for primary school students using the statistical machine translation approach. To the best of our approach, this is the first work that applies statistical translation to Turkish Sign Language. A parallel corpus is compiled from the books published by Ministry of National Education of Turkey. The results of the system were tested using different evaluation metrics. We observe that the results obtained are motivating for new studies.
An Improved Genetic Algorithm for Optimal Search Path of Unmanned Underwater Vehicles

To solve path planning problem of continuous space-time Markov moving targets for UUV search, an optimal path planning model is established. The search direction of the UUV is set as decision variables. An improved genetic algorithm is adopted to pursue an optimal path for underwater anti-submarine search. The algorithm utilizes an improved real number encoding method to describe the path. The target's motion is assumed as uniform distribution in direction and normal distribution in velocity around an initial speed. The results show that the search path planning is more reasonable through a certain number of genetic and cross mutation operations. The proposed method has the advantages of high search efficiency, good stability and short reaction period, and is suitable for solving underwater path-searching problems.
Automated Deep Learning Analysis of Purple Martin Videos Depicting Incubation and Provisioning

Deep learning models have been developed to automatically analyze video clips of purple martin nesting behavior. Two separate models have been constructed, one for incubation and one for provisioning. The incubation model is a simple two class model that analyzes the videos to determine if an adult purple martin is incubating the eggs/young nestlings or not. The model is a Keras/Tensor Flow convolutional neural network (CNN) trained with 12 thousand still images and achieves a validation data set accuracy of 99.5% percent on still images. A comparison of the results of the automated video analysis with sample validation videos viewed manually shows good agreement; the model approaches human accuracy. Some conclusions from the incubation analysis will be discussed. The provisioning analysis requires a much more complex 3 class model which must distinguish between zero, one parent or both parents on the nest. With training sets including 26 thousand images the CNN model demonstrates a validation set accuracy of 99% on the still images. However, the actual video analysis presents difficulties. Several different CNN models have been tried but results were similar. The best results to date on analyzing the videos for provisioning events have been 88% accuracy with 10% false positives. A discussion of the conclusions from the provisioning model and model analysis will be presented.
Self-similar decomposition of digital signals

The behaviour of a physical quantity in time is described by signals. Traditionally, signals are analysed in the time domain either as time - amplitude relationship or in the frequency domain as frequency - signal power dependency. Both traditional representations have substantial limitations. New algorithms for signal representation and processing are required in order to give some additional useful information about observed processes. The present paper proposes a self-similar decomposition of digital signals, which gives rise to a multiscale description, preserving all features of the signals. The proposed description does not depend on predefined basis functions like sine waves, basic wavelets, etc. Instead, the newly proposed approach looks for self-similar associations of signal segments. The proposed signal description can be considered as an attempt to combine signal representation in time domain with signal representation in frequency domain.
Survey on Context-aware Systems and Their Applications

In this paper, significant concepts and technology of context-aware systems are firstly expatiated, wherein reasoning techniques are classified and compared. Then based on our study, related applications in various fields, specifically the service recommendation and content adaptation transmission, are introduced. Finally, open issues are discussed to point out the challenge and emerging development direction of context-aware systems.
Applied Logics to Develop Ontology Model of the Complex-Structured Domains: Organic Chemistry and Biochemistry

Modern challenges in organic chemistry and biochemistry, such as protein folding modeling, synthesis prediction tasks, and different kinds of hypothesis testing, stem from the increase in quantity and complexity of knowledge in these fields. Intelligent system engineering demands consistency and coherency in terms and their definitions. Ontology development in the mentioned areas can partially address this issue. However, real-world complex-structured domain modeling cannot be achieved if ontologies stay pure taxonomies. The rule of thumb says that the more complex the application is, the more expressive semantic model it requires. To create one in the fields of organic chemistry and biochemistry, the notions of complex-structured domain, conceptualization model defined as many-sorted algebraic system, and unenriched system of logical formulas of the level m have been used. Modular ontology model for organic chemistry has been reengineered, and modular ontology model for biochemistry has been introduced. Module “Nuclide” has been introduced. This module has been written in the language of applied logics. Explicit content of “Nuclide” module can be found at the very end of the following paper.
Scalable and Accurate Detection of Function Clones in Software Using Multithreading

Code cloning is defined as a programming practice in which a programmer reuses a code fragment after modifying existing codes or copying it as it is. Code clones can be detected at various levels or varying granularity size but when they are detected at the function level of a program, they are called function clones. Since in non-object oriented languages, functions are the defining components of any software, so detection of function clones can be very useful and can help in reducing the cost of maintenance. In this paper, we have proposed a multi-threading metrics-based detectionSingh, Manoj Kumar algorithm for detecting Type-I and Type-II function clones. We evaluated our approach on three C-language open source projects. The time-consumption for detection is reduced toKumar, Kuldeep a greater extent giving a higher performance as compared to CloneManager, one of the recent function clone detector.
Optimized Availability-Aware Component Scheduler for Applications in Container-Based Cloud

Cloud Computing is a popular platform for providing online computing services to the tenants. With migrating large number of the applications to the cloud, application availability is raised as a concern issue. Availability is a non-functional requirement that refers to the percentage of time the application service is available for the use. Many factors can impact the application availability especially in the cloud environment. In this paper, we show the impact of scheduling (placement) of the application's components inside the Data Center of the cloud on the application availability. We propose an optimized Availability- Aware strategy to schedule the components with the goal to maximize the availability of the application while satisfying several constraints. We map the scheduling problem to Integer Linear Programming (ILP) optimization model. We compare the Availability-Aware strategy with other scheduling strategies. The results show that the Availability-Aware strategy achieves higher application availability compared to the other strategies.
Alternating optimization to solve penalized regression‐based clustering model

Abstract Two previously proposed heuristic algorithms for solving penalized regression‐based clustering model (PRClust) are (a) an algorithm that combines the difference‐of‐convex programming with a coordinate‐wise descent (DC‐CD) algorithm and (b) an algorithm that combines DC with the alternating direction method of multipliers (DC‐ADMM). In this paper, a faster method is proposed for solving PRClust. DC‐CD uses p × n × ( n − 1)/2 slack variables to solve PRClust, where n is the number of data and p is the number of their features. In each iteration of DC‐CD, these slack variable and cluster centres are updated using a second‐order cone programming (SOCP). DC‐ADMM uses p × n × ( n − 1) slack variables. In each iteration of DC‐ADMM, these slack variables and cluster centres are updated using ADMM. In this paper, PRClust is reformulated into an equivalent model to be solved using alternating optimization. Our proposed algorithm needs only n × ( n − 1)/2 slack variables, which is much less than that of DC‐CD and DC‐ADMM and updates them analytically using a simple equation in each iteration of the algorithm. Our proposed algorithm updates only cluster centres using an SOCP. Therefore, our proposed SOCP is much smaller than that of DC‐CD, which is used to update both cluster centres and slack variables. Experimental results on real datasets confirm that our proposed method is faster and much faster than DC‐ADMM and DC‐CD, respectively.
Irregular-Program-Based Hash Algorithms

Because of their energy efficiency over general-purpose central processing unit (CPU), application-specific integrated circuit (ASIC) and field-programmable gate array (FPGA) have been used to accelerate blockchain mining. These hardware accelerators create the concern of centralization because the ASIC devices are dominated by a few manufacturers. Several algorithms are proposed to lower the benefits of ASIC including memory-intensive hash algorithms, where the memory access patterns are random and depend on input data. To further lower the potential hardware acceleration, in this paper, we introduce irregular-program-based hash algorithms, where both the code path and memory access are random and depend on input data. We present an example of the hash algorithm based on dynamic search tree (DST), which exhibits both control-path and memory-access irregularities. We compare the performance of a CPU-based implementation of the DST to an existing FPGA-based implementation, which shows comparable performance. An instance of the proposed hash algorithm is elaborated, and the test inputs and outputs are given.
User Guidance for Interactive Camera Calibration

For building a Augmented Reality (AR) pipeline, the most crucial step is the camera calibration as overall quality heavily depends on it. In turn camera calibration itself is influenced most by the choice of camera-to-pattern poses – yet currently there is only little research on guiding the user to a specific pose. We build upon our novel camera calibration framework that is capable to generate calibration poses in real-time and present a user study evaluating different visualization methods to guide the user to a target pose. Using the presented method even novel users are capable to perform a precise camera calibration in about 2 min.
Analyzing the Effect of Eye Center Localization on Accurate Landmark Localization in a Facial Image

Localization of facial landmarks on a human face is an important step for many face-related computer vision applications. The most of the earlier techniques (AAMs, CLMs) has achieved good performance in landmark localization but they always limited by the initialization of landmarks. In this paper, the initialization problem is solved by taking the eye center as references to the mean face shape. In the proposed method, the eye centers are estimated using multi-scale iris shape feature first and then the constrained local model is applied for landmark localization where initialization is done using mean face shape taking eye centers as references. The performance of eye center estimation and landmark localization method are evaluated on AR and Multi-PIE databases. For eye center estimation three normalized eye localization error is considered whereas for landmark localization RMSE and detection rate are considered. For landmark localization, a total of 130 and 68 landmarks are considered for AR and Multi-PIE database respectively. The experimental results suggest that the proposed method has achieved improved performance as compared to some of the other methods.
PCPCAD: Proposal Complementary Action Detector

Temporal action detection is still a challenging task. This task not only requires correct classification, but also needs to accurately detect the start and end times of each action. In this paper, we present a novel proposal complementary action detector (PCAD) to deal with video streams under continuous, untrimmed conditions. Our approach first uses a simple fully 3D convolutional (Conv3D) network to encode the video streams and then generates candidate temporal proposals for activities by using anchor segments. To generate more precise proposals, we also designed a boundary proposal network (BPN) to offer some complementary information for the candidate proposals. Finally, we learn an efficient classifier to classify the generated proposals into different activities and refine their temporal boundaries at the same time. Our model can achieve end-to-end training by jointly optimizing classification loss and regression loss. When evaluating on THUMOS'14 detection benchmark, PCAD achieves the state-of-the-art performance in high-speed models.